Images are a rich source of data. In this chapter, we will see how quantitative information can be extracted from images, and how we can use statistical methods to summarize and understand the data. The goal of the chapter is to show that getting started working with image data is easy – if you are able to handle the basic R environment, you are ready to start working with images. That said, this chapter is not a general introduction to image analysis. The field is extensive; it touches many areas of signal processing, information theory, mathematics, engineering and computer science, and there are excellent books that present a systematic overview.

We will mainly study series of two-dimensional images, in particular, images of cells. We will learn how to identify the cells’ positions and shapes and how to quantitatively measure characteristics of the identified shapes and patterns, such as sizes, intensities, color distributions and relative positions. Such information can then be used in down-stream analyses: for instance, we can compare cells between different conditions, say under the effect of different drugs, or in different stages of differentiation and growth; or we can measure how the objects in the image relate to each other, e.g., whether they like to cluster together or repel each other, or whether certain characteristics tend to be shared between neighboring objects, indicative of cell-to-cell communication. In the language of genetics, what this means is that we can use images as complex phenotypes or as multivariate quantitative traits.

We will here not touch upon image analysis in more than two dimensions: we won’t consider 3D segmentation and registration, nor temporal tracking. These are sophisticated tasks for which specialized software would likely perform better than what we could assemble in the scope of this chapter.

There are similarities between data from high-throughput imaging and other high-throughput data in genomics. Batch effects tend to play a role, for instance because of changes in staining efficiency, illumination or many other factors. We’ll need to take appropriate precautions in our experimental design and analysis choices. In principle, the intensity values in an image can be calibrated in physical units, corresponding, say to radiant energy or fluorophore concentration; however this is not always done in practice in biological imaging, and perhaps also not needed. Somewhat easier to achieve and clearly valuable is a calibration of the spatial dimensions of the image, i.e., the conversion factor between pixel units and metric distances.

In this chapter we will:

Learn how to read, write and manipulate images in R.

Understand how to apply filters and transformations to images.

Combine these skills to do segmentation and feature extraction; we will use cell segmentation as an example.

Learn how to use statistical methods to analyse spatial distributions and dependencies.

Get to know the most basic distribution for a spatial point process: the homogeneous Poisson process.

Recognize whether your data fit that basic assumption or whether they show evidence of clumping or exclusion.

A useful toolkit for handling images in R is the Bioconductor package **EBImage** (Pau et al. 2010). We start out by reading in a simple picture to demonstrate the basic functions.

```
library("EBImage")
imagefile = system.file("images", "mosquito.png",
package = "MSMB")
mosq = readImage(imagefile)
```

**EBImage** currently supports three image file formats: `jpeg`

, `png`

and `tiff`

. Above, we loaded a sample image from the **MSMB** package. When you are working with your own data, you do not need that package, just provide the name(s) of your file(s) to the `readImage`

function. As you will see later in this chapter, `readImage`

can read multiple images in one go, which are then all assembled into a single image data object. For this to work, the images need to have the same dimensions and color mode.

► Question 11.1

The **RBioFormats** package136 Available on GitHub: https://github.com/aoles/RBioFormats. provides functionality for reading and writing many more image file formats. How many different file formats are supported?

► Solution

Let’s visualize the image that we just read in. The basic function is `display`

.

The above command opens the image in a window of your web browser (as set by `getOption("browser")`

). Using the mouse or keyboard shortcuts, you can zoom in and out of the image, pan and cycle through multiple image frames.

Alternatively, we can also display the image using R’s built-in plotting by calling `display`

with the argument `method = "raster"`

. The image then goes to the current device. In this way, we can combine image data with other plotting functionality, for instance, to add text labels.

Figure 11.1: Mosquito discovered deceased in the suburbs of Decatur, Georgia (credit: CDC / Janice Haney Carr).

```
display(mosq, method = "raster")
text(x = 85, y = 800, label = "A mosquito",
adj = 0, col = "orange", cex = 1.5)
```

The resulting plot is shown in Figure 11.1. As usual, the graphics displayed in an R device can be saved using the **base** R functions `dev.print`

or `dev.copy`

.

Note that we can also read and view color images, see Figure 11.2.

```
imagefile = system.file("images", "hiv.png",
package = "MSMB")
hivc = readImage(imagefile)
display(hivc)
```

Figure 11.2: Scanning electron micrograph of HIV-1 virions budding from a cultured lymphocyte (credit: CDC / C. Goldsmith, P. Feorino, E.L. Palmer, W.R. McManus).

Furthermore, if an image has multiple frames, they can be displayed all at once in a grid arrangement by specifying the function argument `all = TRUE`

(Figure 11.3),

```
nuc = readImage(system.file("images", "nuclei.tif",
package = "EBImage"))
display(1 - nuc, method = "raster", all = TRUE)
```

or we can just view a single frame, for instance, the second one.

► Question 11.2

Why did we pass the argument `1 - nuc`

to the `display`

function in the code for Figure 11.3? How does it look if we display `nuc`

directly?

Let’s dig into what’s going on by first identifying the class of the image object.

```
## [1] "Image"
## attr(,"package")
## [1] "EBImage"
```

So we see that this object has the class *Image*. This is not one of the base R classes, rather, it is defined by the package **EBImage**. We can find out more about this class through the help browser or by typing `class ? Image`

. The class is derived from the base R class *array*, so you can do with *Image* objects everything that you can do with R arrays; in addition, they have some extra features and behaviors137 In R’s parlance, the extra features are called **slots** and the behaviors are called methods; methods are a special kind of function..

► Question 11.3

How can you find out what the slots of an *Image* object are and which methods can be applied to it?

► Solution

The dimensions of the image can be extracted using the `dim`

method, just like for regular arrays.

`## [1] 1400 952`

The `hist`

method has been redefined138 In object oriented parlance, *overloaded* compared to the ordinary `hist`

function for arrays; it uses different and possibly more useful defaults (Figure 11.4).

Figure 11.4: Histogram of the pixel intensities in `mosq`

. Note that the range is between 0 and 1.

If we want to directly access the data matrix as an R *array*, we can use the accessor function `imageData`

.

```
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.1960784 0.1960784 0.1960784 0.1960784 0.1960784
## [2,] 0.1960784 0.1960784 0.1960784 0.1960784 0.1960784
## [3,] 0.1960784 0.1960784 0.2000000 0.2039216 0.2000000
## [,6]
## [1,] 0.1960784
## [2,] 0.1960784
## [3,] 0.1960784
```

A useful summary of an *Image* object is printed if we simply type the object’s name.

```
## Image
## colorMode : Grayscale
## storage.mode : double
## dim : 1400 952
## frames.total : 1
## frames.render: 1
##
## imageData(object)[1:5,1:6]
## [,1] [,2] [,3] [,4] [,5]
## [1,] 0.1960784 0.1960784 0.1960784 0.1960784 0.1960784
## [2,] 0.1960784 0.1960784 0.1960784 0.1960784 0.1960784
## [3,] 0.1960784 0.1960784 0.2000000 0.2039216 0.2000000
## [4,] 0.1960784 0.1960784 0.2039216 0.2078431 0.2000000
## [5,] 0.1960784 0.2000000 0.2117647 0.2156863 0.2000000
## [,6]
## [1,] 0.1960784
## [2,] 0.1960784
## [3,] 0.1960784
## [4,] 0.1960784
## [5,] 0.1921569
```

Now let us look at the color image.

```
## Image
## colorMode : Color
## storage.mode : double
## dim : 1400 930 3
## frames.total : 3
## frames.render: 1
##
## imageData(object)[1:5,1:6,1]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 0 0 0 0 0
## [2,] 0 0 0 0 0 0
## [3,] 0 0 0 0 0 0
## [4,] 0 0 0 0 0 0
## [5,] 0 0 0 0 0 0
```

The two images differ by their property `colorMode`

, which is `Grayscale`

for `mosq`

and `Color`

for `hivc`

. What is the point of this property? It turns out to be convenient when we are dealing with stacks of images. If `colorMode`

is `Grayscale`

, then the third and all higher dimensions of the array are considered as separate image frames corresponding, for instance, to different \(z\)-positions, time points, replicates, etc. On the other hand, if `colorMode`

is `Color`

, then the third dimension is assumed to hold different color channels, and only the fourth and higher dimensions – if present – are used for multiple image frames. In `hivc`

, there are three color channels, which correspond to the red, green and blue intensities of our photograph. However, this does not necessarily need to be the case, there can be any number of color channels.

► Question 11.4

Describe how R stores the data `nuc`

.

► Solution

Directly saving images to disk in the array representation that we saw in the previous section would lead to large file sizes – in most cases, needlessly large. It is common to use compression algorithms to reduce the storage consumption. There are two main types of image139 In an analogous way, this is also true for movies and music. compression:

Lossless compression: it is possible to exactly reconstruct the original image data from the compressed file. Simple priciples of lossless compression are: (i) do not spend more bits on representing a pixel than needed (e.g., the pixels in the

`mosq`

image have a range of 256 gray scale values, and this could be represented by 8 bits, although`mosq`

stores them in a 64-bit numeric format140 While this is somewhat wasteful of memory, it is more compatible with the way the rest of R works, and is rarely a limiting factor on modern computer hardware.); and (2) identify patterns (such as those that you saw above in the printed pixel values for`mosq`

and`hivc`

) and represent them by much shorter to write down rules instead.Lossy compression: additional savings are made compared to lossless compression by dropping details that a human viewer would be unlikely to notice anyway.

An example for a storage format with lossless compression is PNG141 https://en.wikipedia.org/wiki/Portable_Network_Graphics, an example for lossy compression is the JPEG142 https://en.wikipedia.org/wiki/JPEG format. While JPEG is good for your holiday pictures, it is good practice to store scientific images in a lossless format.

We read the image `hivc`

from a file in PNG format, so let’s now write it out as a JPEG file. The lossiness is specified by the *quality* parameter, which can lie between 1 (worst) and 100 (best).

Similarly, we could have written the image as a `TIFF`

file and chosen among several compression algorithms (see the manual page of the `writeImage`

and `writeTiff`

functions). The package **RBioFormats** lets you write to many further image file formats.

► Question 11.5

How big is the `hivc`

object in R’s memory? How big is the JPEG file? How much RAM would you expect a three color, 16 Megapixel image to occupy?

► Solution

Now that we know that images are stored as arrays of numbers in R, our method of manipulating images becomes clear – simple algebra! For example, we can take our original image, shown again in the top left of Figure 11.5, and flip the bright areas to dark and vice versa by multiplying the image with -1 (Figure 11.5, top right).

Figure 11.6: Cropping: `mosqcrop`

► Question 11.6

What does the function `normalize`

do?

We could also adjust the contrast through multiplication (Figure 11.5, bottom left) and the gamma-factor through exponentiation (Figure 11.5, bottom right).

Figure 11.7: Threshold: `mosqthresh`

Furthermore, we can crop, threshold and transpose images with matrix operations (Figures 11.6–11.8).

Figure 11.8: Transposition: `mosqtransp`

► Question 11.7

What data type is `mosqthresh`

, the result of the thresholding?

► Solution

► Question 11.8

Instead of the `transpose`

function as above, could we also have used R’s **base** function `t`

?

► Solution

We just saw one type of spatial transformation, transposition, but there are many more – here are some examples:

```
mosqrot = EBImage::rotate(mosq, angle = 30)
mosqshift = translate(mosq, v = c(40, 70))
mosqflip = flip(mosq)
mosqflop = flop(mosq)
```

In the code above, the function `rotate`

143 Here we call the function with its namespace qualifier `EBImage::`

to avoid confusion with a function of the same name in the namespace of the **spatstat** package, which we will attach later. rotates the image clockwise with the given angle, `translate`

moves the image by the specified two-dimensional vector (pixels that end up outside the image region are cropped, and pixels that enter into the image region are set to zero). The functions `flip`

and `flop`

reflect the image around the central horizontal and vertical axis, respectively. The results of these operations are shown in Figure 11.9.

Let’s now switch to an application in cell biology. We load images of human cancer cells that were studied by Laufer, Fischer and co-workers (Laufer et al. 2013). They are shown in Figure 11.10.

```
imagefiles = system.file("images", c("image-DAPI.tif",
"image-FITC.tif", "image-Cy3.tif"), package="MSMB")
cells = readImage(imagefiles)
```

The *Image* object `cells`

is a three-dimensional array of size 340 \(\times\) 490 \(\times\) 3, where the last dimension indicates that there are three individual grayscale frames. Our goal now is to computationally identify and quantitatively characterize the cells in these images. That by itself would be a modest goal, but note that the dataset of Laufer et al.contains over 690,000 images, each of which has 2,048 \(\times\) 2,048 pixels. Here, we are looking at three of these, out of which a small region was cropped. Once we know how to achieve our stated goal, we can apply our abilities to such large image collections, and that is no longer a modest aim!

However, before we can start with real work, we need to deal with a slightly mundane data conversion issue. This is, of course, not unusual. Let us inspect the dynamic range (the minimum and the maximum value) of the images.

```
## image-DAPI image-FITC image-Cy3
## [1,] 0.001586938 0.002899214 0.001663233
## [2,] 0.031204700 0.062485695 0.055710689
```

We see that the maximum values are small numbers well below 1. The reason for this is that the `readImage`

function recognizes that the `TIFF`

images uses 16 bit integers to represent each pixel, and it returns the data – as is common for numeric variables in R – in an array of double precision floating point numbers, with the integer values (whose theoretical range is from 0 to \(2^{16}-1=65535\)) stored in the mantissa of the floating point representation and the exponents chosen so that the theoretical range is mapped to the interval \([0,1]\). However, the scanner that was used to create these images only used the lower 11 or 12 bits, and this explains the small maximum values in the images. We can rescale these data to approximately cover the range \([0,1]\) as follows144 The function `normalize`

provides a more flexible interface to the scaling of images..

```
## image-DAPI image-FITC image-Cy3
## [1,] 0.05078202 0.04638743 0.02661173
## [2,] 0.99855039 0.99977111 0.89137102
```

We can keep in mind that these multiplications with a multiple of 2 have no impact on the underlying precision of the stored data.

Now we are ready to get going with analyzing the images. As our first goal is segmentation of the images to identify the individual cells, we can start by removing local artifacts or noise from the images through smoothing. An intuitive approach is to define a window of a selected size around each pixel and average the values within that window. After applying this procedure to all pixels, the new, smoothed image is obtained. Mathematically, we can express this as

\[\begin{equation}
f^*(x,y) = \frac{1}{N} \sum_{s=-a}^{a}\sum_{t=-a}^{a} f(x+s, y+t),
\tag{11.1}
\end{equation}\]

where \(f(x,y)\) is the value of the pixel at position \(x\), \(y\), and \(a\) determines the window size, which is \(2a+1\) in each direction. \(N=(2a+1)^2\) is the number of pixels averaged over, and \(f^*\) is the new, smoothed image.

More generally, we can replace the moving average by a weighted average, using a weight function \(w\), which typically has highest weight at the window midpoint (\(s=t=0\)) and then decreases towards the edges.

\[\begin{equation}
(w * f)(x,y) = \sum_{s=-\infty}^{+\infty} \sum_{t=-\infty}^{+\infty} w(s,t)\, f(x+s, y+s)
\tag{11.2}
\end{equation}\]

For notational convenience, we let the summations range from \(-\infty\) to \(\infty\), even if in practice the sums are finite as \(w\) has only a finite number of non-zero values. In fact, we can think of the weight function \(w\) as another image, and this operation is also called the *convolution* of the images \(f\) and \(w\), indicated by the the symbol \(*\). In **EBImage**, the 2-dimensional convolution is implemented by the function `filter2`

, and the auxiliary function `makeBrush`

can be used to generate weight functions \(w\).

► Question 11.9

How does the weight matrix `w`

look like?

► Solution

Figure 11.12: `nucSmooth`

, a smoothed version of the DNA channel in the image object `cells`

(the original version is shown in the leftmost panel of Figure 11.10)

In fact, the `filter2`

function does not directly perform the summation indicated in Equation (11.2). Instead, it uses the Fast Fourier Transformation in a way that is mathematically equivalent and computationally more efficient.

The convolution in Equation (11.2) is a *linear* operation, in the sense that \(w*(c_1f_1+c_2f_2)= c_1w*f_1 + c_2w*f_2\) for any two images \(f_1\), \(f_2\) and numbers \(c_1\), \(c_2\). There is beautiful and powerful theory underlying linear filters (Vetterli, Kovačević, and Goyal 2014).

To proceed we now use smaller smoothing bandwidths than what we displayed in Figure 11.12 for demonstration. Let’s use a `sigma`

of 1 pixel for the DNA channel and 3 pixels for actin and tubulin.

```
cellsSmooth = Image(dim = dim(cells))
sigma = c(1, 3, 3)
for(i in seq_along(sigma))
cellsSmooth[,,i] = filter2( cells[,,i],
filter = makeBrush(size = 51, shape = "gaussian",
sigma = sigma[i]) )
```

The smoothed images have reduced pixel noise, yet still the needed resolution.

The idea of adaptive thresholding is that, compared to straightforward thresholding as we did for Figure 11.7, the threshold is allowed to be different in different regions of the image. In this way, one can anticipate spatial dependencies of the underlying background signal caused, for instance, by uneven illumination or by stray signal from nearby bright objects. In fact, we have already seen an example for uneven background in the bottom right image of Figure 11.3.

Our colon cancer images (Figure 11.10) do not have such artefacts, but for demonstration, let’s simulate uneven illumination by multiplying the image with a two-dimensional bell function `illuminationGradient`

, which has highest value in the middle and falls off to the sides (Figure 11.13).

```
py = seq(-1, +1, length.out = dim(cellsSmooth)[1])
px = seq(-1, +1, length.out = dim(cellsSmooth)[2])
illuminationGradient = Image(
outer(py, px, function(x, y) exp(-(x^2+y^2))))
nucBadlyIlluminated = cellsSmooth[,,1] * illuminationGradient
```

We now define a smoothing window, `disc`

, whose size is 21 pixels, and therefore bigger than the nuclei we want to detect, but small compared to the length scales of the illumination artifact. We use it to compute the image `localBackground`

(shown in Figure 11.13) and the thresholded image `nucBadThresh`

.

```
disc = makeBrush(21, "disc")
disc = disc / sum(disc)
localBackground = filter2(nucBadlyIlluminated, disc)
offset = 0.02
nucBadThresh = (nucBadlyIlluminated - localBackground > offset)
```

After having seen that this may work, let’s do the same again for the actual (not artificially degraded) image, as we need this for the next steps.

By comparing each pixel’s intensity to a background determined from the values in a local neighborhood, we assume that the objects are relatively sparse distributed in the image, so that the signal distribution in the neighborhood is dominated by background. For the nuclei in our images, this assumption makes sense, for other situations, you may need to make different assumptions. The adaptive thresholding that we have done here uses a linear filter, `filter2`

, and therefore amounts to (weighted) local averaging. Other distribution summaries, e.g. the median or a low quantile, tend to be preferable, even if they are computationally more expensive. For local median filtering, **EBimage** provides the function `medianFilter`

.

The thresholded image `nucThresh`

(shown in the left panel of Figure 11.14 is not yet satisfactory. The boundaries of the nuclei are slightly rugged, and there is noise at the single-pixel level. An effective and simple way to remove these nuisances is given by a set of morphological operations (Serra 1983).

Provided a binary image (with values, say, 0 and 1, representing back- and foreground pixels), and a binary mask145 An example for a mask is a circle with a given radius, or more precisely, the set of pixels within a certain distance from a center pixel. (which is sometimes also called the structuring element), these operations work as follows.

`erode`

: For every foreground pixel, put the mask around it, and if any pixel under the mask is from the background, then set all these pixels to background.`dilate`

: For every background pixel, put the mask around it, and if any pixel under the mask is from the foreground, then set all these pixels to foreground.`open`

: perform`erode`

followed by`dilate`

.

We can also think of these operations as filters, however, in contrast to the linear filters of Section 11.8 they operate on binary images only, and there is no linearity.

Let us apply morphological opening to our image.

The result of this is subtle, and you will have to zoom into the images in Figure 11.14 to spot the differences, but this operation manages to smoothen out some pixel-level features in the binary images that for our application are undesirable.

The binary image `nucOpened`

represents a segmentation of the image into foreground and background pixels, but not into individual nuclei. We can take one step further and extract individual objects defined as connected sets of pixels. In **EBImage**, there is a handy function for this purpose, `bwlabel`

.

```
## nucSeed
## 0 1 2 3 4 5 6 7 8
## 155408 511 330 120 468 222 121 125 159
## 9 10 11 12 13 14 15 16 17
## 116 520 115 184 179 116 183 187 303
## 18 19 20 21 22 23 24 25 26
## 226 164 309 194 148 345 287 203 379
## 27 28 29 30 31 32 33 34 35
## 371 208 222 320 443 409 493 256 169
## 36 37 38 39 40 41 42 43
## 225 376 214 228 341 269 119 315
```

The function returns an image, `nucSeed`

, of integer values, where 0 represents the background, and the numbers from 1 to 43 index the different identified objects.

► Question 11.10

What are the numbers in the above table?

► Solution

To visualize such images, the function `colorLabels`

is convenient, which converts the (grayscale) integer image into a color image, using distinct, arbitrarily chosen colors for each object.

This is shown in the middle panel of Figure 11.14. The result is already encouraging, although we can spot two types of errors:

Some neighboring objects were not properly separated.

Some objects contain holes.

Indeed, we could change the occurrences of these by playing with the disc size and the parameter `offset`

in Section [adapthresh]: making the offset higher reduces the probability that two neighboring object touch and are seen as one object by `bwlabel`

; on the other hand, that leads to even more and even bigger holes. Vice versa for making it lower.

Segmentation is a rich and diverse field of research and engineering, with a large body of literature, software tools (Schindelin et al. 2012; Chaumont et al. 2012; Carpenter et al. 2006; Held et al. 2010) and practical experience in the image analysis and machine learning communities. What is the adequate approach to a given task depends hugely on the data and the underlying question, and there is no universally best method. It is typically even difficult to obtain a “ground truth” or “gold standards” by which to evaluate an analysis – relying on manual annotation of a modest number of selected images is not uncommon. Despite the bewildering array of choices, it is easy to get going, and we need not be afraid of starting out with a simple solution, which we can successively refine. Improvements can usually be gained from methods that allow inclusion of more prior knowledge of the expected shapes, sizes and relations between the objects to be identified.

For statistical analyses of high-throughput images, we may choose to be satisfied with a simple method that does not rely on too many parameters or assumptions and results in a perhaps sub-optimal but rapid and good enough result (Rajaram et al. 2012). In this spirit, let us proceed with what we have. We generate a lenient foreground mask, which surely covers all nuclear stained regions, even though it also covers some regions between nuclei. To do so, we simply apply a second, less stringent adaptive thresholding.

and apply another morphological operation, `fillHull`

, which fills holes that are surrounded by foreground pixels.

To improve `nucSeed`

, we can now *propagate* its segmented objects until they fill the mask defined by `nucMask`

. Boundaries between nuclei, in those places where the mask is connected, can be drawn by Voronoi tessellation, which is implemented in the function `propagate`

, and will be explained in the next section.

The result is displayed in the rightmost panel of Figure 11.14.

Figure 11.15: Example of a Voronoi segmentation, indicated by the gray lines, using the nuclei (indicated by black regions) as seeds..

Voronoi tessellation is useful if we have a set of seed points (or regions) and want to partition the space that lies between these seeds in such a way that each point in the space is assigned to its closest seed. As this is an intuitive and powerful idea, we’ll use this section for a short digression on it. Let us consider a basic example. We use the image `nuclei`

as seeds. To call the function `propagate`

, we also need to specify another image: for now we just provide a trivial image of all zeros, and we set the parameter `lambda`

to a large positive value (we will come back to these choices).

```
zeros = Image(dim = dim(nuclei))
voronoiExamp = propagate(seeds = nuclei, x = zeros, lambda = 100)
voronoiPaint = paintObjects(voronoiExamp, 1 - nucOpened)
```

► Question 11.11

How do you select partition elements from the tessellation?

► Solution

The result is shown in Figure 11.15. This looks interesting, but perhaps not yet as useful as the image `nuclei`

in Figure 11.14. We note that the basic definition of Voronoi tessellation, which we have given above, allows for two generalizations:

By default, the space that we partition is the full, rectangular image area – but indeed we could restrict ourselves to any arbitrary subspace. This is akin to finding the shortest distance from each point to the next seed not in a simple flat landscape, but in a landscape that is interspersed by lakes and rivers (which you cannot cross), so that all paths need to remain on the land.

`propagate`

allows for this generalization through its`mask`

parameter.By default, we think of the space as flat – but in fact it could have hills and canyons, so that the distance between two points in the landscape not only depends on their \(x\)- and \(y\)-positions but also on the ascents and descents, up and down in \(z\)-direction, that lie in between. We can think of \(z\) as an “elevation”. You can specify such a landscape to

`propagate`

through its`x`

argument.

Mathematically, we say that instead of the simple default case (a flat rectangle, or image, with a Euclidean metric on it), we perform the Voronoi segmentation on a Riemann manifold that has a special shape and a special metric. Let us use the notation \(x\) and \(y\) for the column and row coordinates of the image, and \(z\) for the elevation. For two neighboring points, defined by coordinates \((x, y, z)\) and \((x+\text{d}x, y+\text{d}y, z+\text{d}z)\), the distance \(\text{d}s\) between them is thus not obtained by the usual Euclidean metric on the 2D image,

\[\begin{equation}
\text{d}s^2 = \text{d}x^2 + \text{d}y^2
\tag{11.3}
\end{equation}\]

but instead

\[\begin{equation}
\text{d}s^2 = \frac{2}{\lambda+1} \left[ \lambda \left( \text{d}x^2 + \text{d}y^2 \right) + \text{d}z^2 \right],
\tag{11.4}
\end{equation}\]

where the parameter \(\lambda\) is a real number \(\ge0\). To understand this, lets look at some important cases:

\[\begin{equation}
\begin{aligned}
\lambda=1:&\quad \text{d}s^2 = \text{d}x^2 + \text{d}y^2 + \text{d}z^2\\
\lambda=0:&\quad \text{d}s^2 = 2\, \text{d}z^2\\
\lambda\to\infty:&\quad \text{d}s^2 = 2 \left( \text{d}x^2 + \text{d}y^2 \right)\\
\end{aligned}
\tag{11.5}
\end{equation}\]

For \(\lambda=1\), the metric becomes the isotropic Euclidean metric, i.e., a movement in \(z\)-direction is equally “expensive” or “far” as in \(x\)- or \(y\)-direction. In the extreme case of \(\lambda=0\), only the \(z\)-movements matter, whereas lateral movements (in \(x\)- or \(y\)-direction) do not contribute to the distance. In the other extreme case, \(\lambda\to\infty\), only lateral movements matter, and movement in \(z\)-direction is “free”. Distances between points further apart are obtained by summing \(\text{d}s\) along the shortest path between them. The parameter \(\lambda\) serves as a convenient control of the relative weighting between sideways movement (along the \(x\) and \(y\) axes) and vertical movement. Intuitively, if you imagine yourself as a hiker in such a landscape, by choosing \(\lambda\) you can specify how much you are prepared to climb up and down to overcome a mountain, versus walking around it. When we used `lambda = 100`

in our call to `propagate`

at the begin of this section, this value was effectively infinite, so we were in the third boundary case of Equation (11.5).

For the purpose of cell segmentation, these ideas were put forward by Thouis Jones et al. (Jones, Carpenter, and Golland 2005; Carpenter et al. 2006), who also wrote the efficient algorithm that is used by `propagate`

.

► Task

Try out the effect of using different \(\lambda\)s.

Figure 11.16: Histogram of the actin channel in `cellsSmooth`

, after taking the logarithm.

Figure 11.17: Zoom into Figure 11.16.

To determine a mask of cytoplasmic area in the images, let us explore a different way of thresholding, this time using a global threshold which we find by fitting a mixture model to the data. The histograms show the distributions of the pixel intensities in the actin image. We look at the data on the logarithmic scale, and in Figure 11.17 zoom into the region where most of the data lie.

Looking at the these histograms for many images, we can set up the following model for the purpose of segmentation: the signal in the cytoplasmic channels of the *Image* `cells`

is a mixture of two distributions, a log-Normal background and a foreground with another, unspecified, rather flat, but mostly non-overlapping distribution146 This is an application of the ideas we saw in Chapter 4 on mixure models.. Moreover the majority of pixels are from the background. We can then find robust estimates for the location and width parameters of the log-Normal component from the half range mode (implemented in the package **genefilter**) and from the root mean square of the values that lie left of the mode.

```
library("genefilter")
bgPars = function(x) {
x = log(x)
loc = half.range.mode( x )
left = (x - loc)[ x < loc ]
wid = sqrt( mean(left^2) )
c(loc = loc, wid = wid, thr = loc + 6*wid)
}
cellBg = apply(cellsSmooth, MARGIN = 3, FUN = bgPars)
cellBg
```

```
## [,1] [,2] [,3]
## loc -2.90176965 -2.94427499 -3.52191681
## wid 0.00635322 0.01121337 0.01528207
## thr -2.86365033 -2.87699477 -3.43022437
```

The function defines as a threshold the location `loc`

plus 6 widths `wid`

147 The choice of the number 6 here is ad hoc; we could make the choice of threshold more objective by estimating the weights of the two mixture components and assigning each pixel to either fore- or background based on its posterior probability according to the mixture model. More advanced segmentation methods use the fact that this is really a classification problem and include additional features and more complex classifiers to separate foreground and background regions..

Figure 11.18: As in Figure 11.17, but with `loc`

and `thr`

shown by vertical lines.

```
hist(log(cellsSmooth[,,3]), xlim = -c(3.6, 3.1), breaks = 300)
abline(v = cellBg[c("loc", "thr"), 3], col = c("brown", "red"))
```

We can now define `cytoplasmMask`

by the union of all those pixels that are above the threshold in the actin or tubulin image, or that we have already classified as nuclear in the image `nuclei`

.

```
cytoplasmMask = (cellsSmooth[,,2] > exp(cellBg["thr", 2])) |
nuclei | (cellsSmooth[,,3] > exp(cellBg["thr", 3]))
```

The result is shown in the left panel of Figure 11.19. To define the cellular bodies, we can now simply extend the nucleus segmentation within this mask by the Voronoi tessellation based propagation algorithm of Section 11.12. This method makes sure that there is exactly one cell body for each nucleus, and the cell bodies are delineated in such a way that a compromise is reached between compactness of cell shape and following the actin and \(\alpha\)-tubulin intensity signal in the images. In the terminology of the `propagate`

algorithm, cell shape is kept compact by the \(x\) and \(y\) components of the distance metric (11.4), and the actin signal is used for the \(z\) component. \(\lambda\) controls the trade-off.

As an alternative representation to the `colorLabel`

plots, we can also display the segmentations of nuclei and cell bodies on top of the original images using the `paintObjects`

function; the Images `nucSegOnNuc`

, `nucSegOnAll`

and `cellSegOnAll`

that are computed below are show in the middle to right panels of Figure 11.19

```
cellsColor = rgbImage(red = cells[,,3],
green = cells[,,2],
blue = cells[,,1])
nucSegOnNuc = paintObjects(nuclei, tgt = toRGB(cells[,,1]),
col = "#ffff00")
nucSegOnAll = paintObjects(nuclei, tgt = cellsColor,
col = "#ffff00")
cellSegOnAll = paintObjects(cellbodies, tgt = nucSegOnAll,
col = "#ff0080")
```

Now that we have the segmentations `nuclei`

and `cellbodies`

together with the original image data `cells`

, we can compute various descriptors, or features, for each cell. We already saw in the beginning of Section 11.11 how to use the base R function `table`

to determine the total number and sizes of the objects. Let us now take this further and compute the mean intensity of the DAPI signal (`cells[,,1]`

) in the segmented nuclei, the mean actin intensity (`cells[,,3]`

) in the segmented nuclei and the mean actin intensity in the cell bodies.

```
meanNucInt = tapply(cells[,,1], nuclei, mean)
meanActIntInNuc = tapply(cells[,,3], nuclei, mean)
meanActIntInCell = tapply(cells[,,3], cellbodies, mean)
```

We can visualize the features in pairwise scatterplots (Figure 11.20). We see that they are correlated with each other, although each feature also carries independent information.

With a little more work, we could also compute more sophisticated summary statistics – e.g., the ratio of nuclei area to cell body area; or entropies, mutual information and correlation of the different fluorescent signals in each cell body, as more or less abstract measures of cellular morphology. Such measures can be used, for instance, to detect subtle drug induced changes of cellular architecture.

While it is easy and intuitive to perform these computations using basic R idioms like in the `tapply`

expressions above, the package **EBImage** also provides the function `computeFeatures`

which efficiently computes a large collection of features that have been commonly used in the literature (a pioneering reference is Boland and Murphy. (2001)). Details about this function are described in its manual page, and an example application is worked through in the **HD2013SGI** vignette. Below, we compute features for intensity, shape and texture for each cell from the DAPI channel using the nucleus segmentation (`nuclei`

) and from the actin and tubulin channels using the cell body segmentation (`cytoplasmRegions`

).

```
F1 = computeFeatures(nuclei, cells[,,1], xname = "nuc",
refnames = "nuc")
F2 = computeFeatures(cellbodies, cells[,,2], xname = "cell",
refnames = "tub")
F3 = computeFeatures(cellbodies, cells[,,3], xname = "cell",
refnames = "act")
dim(F1)
```

`## [1] 43 89`

`F1`

is a *matrix, array* with 43 rows (one for each cell) and 89 columns, one for each of the computed features.

```
## nuc.0.m.cx nuc.0.m.cy nuc.0.m.majoraxis nuc.0.m.eccentricity
## 1 119.5523 17.46895 44.86819 0.8372059
## 2 143.4511 15.83709 26.15009 0.6627672
## 3 336.5401 11.48175 18.97424 0.8564444
## nuc.0.m.theta
## 1 -1.314789
## 2 -1.213444
## 3 1.470913
```

The column names encode the type of feature, as well the color channel(s) and segmentation mask on which it was computed. We can now use multivariate analysis methods – like those we saw in Chapters 5, 7 and 9 – for many dfferent tasks, such as

detecting cell subpopulations (clustering)

classifying cells into pre-defined cell types or phenotypes (classification)

seeing whether the absolute or relative frequencies of the subpopulations or cell types differ between images that correspond to different biological conditions

In addition to these “generic” machine learning tasks, we also know the cell’s spatial positions, and in the following we will explore some ways to make use of these in our analyses.

► Task

Use explorative multivariate methods to visualize the matrices `F1`

, `F2`

, `F3`

: PCA, heatmap. What’s special about the “outlier” cells?

In the previous sections, we have seen ways how to use images of cells to extract their positions and various shape and morphological features. We’ll now explore spatial distributions of the position. In order to have interesting data to work on, we’ll change datasets and look at breast cancer lymph node biopsies.

The lymph nodes function as an immunologic filter for the bodily fluid known as lymph. Antigens are filtered out of the lymph in the lymph node before returning it to the circulation. Lymph nodes are found throughout the body, and are composed mostly of T cells, B cells, dendritic cells and macrophages. The nodes drain fluid from most of our tissues. The lymph ducts of the breast usually drain to one lymph node first, before draining through the rest of the lymph nodes underneath the arm. That first lymph node is called the *sentinel* lymph node. In a similar fashion as the spleen, the macrophages and dendritic cells that capture antigens present these foreign materials to T and B cells, consequently initiating an immune response.

T lymphocytes are usually divided into two major subsets that are functionally and phenotypically different.

CD4+ T-cells, or T helper cells: they are pertinent coordinators of immune regulation. The main function of T helper cells is to augment or potentiate immune responses by the secretion of specialized factors that activate other white blood cells to fight off infection.

CD8+ T cells, or T killer/suppressor cells: these cells are important in directly killing certain tumor cells, viral-infected cells and sometimes parasites. The CD8+ T cells are also important for the down-regulation of immune responses.

Both types of T cells can be found throughout the body. They often depend on the secondary lymphoid organs (the lymph nodes and spleen) as sites where activation occurs.

Dendritic Cells or CD1a cells are antigen-presenting cells that process antigen and present peptides to T cells.

Typing the cells can be done by staining the cells with protein antibodies that provide specific signatures. For instance, different types of immune cells have different proteins expressed, mostly in their cell membranes.

We’ll look at data by Setiadi et al. (2010). After segmentating the image shown in Figure 11.22 using the segmentation method *GemIdent* (Holmes, Kapelner, and Lee 2009), the authors obtained the coordinates and the type of all the cells in the image. We call this type of data a *marked point process*, and it can be seen as a simple table with 3 columns.

```
library("readr")
library("dplyr")
cellclasses = c("T_cells", "Tumor", "DCs", "other_cells")
brcalymphnode = lapply(cellclasses, function(k) {
read_csv(file.path("..", "data",
sprintf("99_4525D-%s.txt", k))) %>%
transmute(x = globalX,
y = globalY,
class = k)
}) %>% bind_rows %>% mutate(class = factor(class))
brcalymphnode
```

```
## # A tibble: 209,462 x 3
## x y class
## <dbl> <dbl> <fct>
## 1 6355 10382 T_cells
## 2 6356 10850 T_cells
## 3 6357 11070 T_cells
## 4 6357 11082 T_cells
## 5 6358 10600 T_cells
## 6 6361 10301 T_cells
## 7 6369 10309 T_cells
## 8 6374 10395 T_cells
## 9 6377 10448 T_cells
## 10 6379 10279 T_cells
## # … with 209,452 more rows
```

```
##
## DCs other_cells T_cells Tumor
## 878 77081 103681 27822
```

We see that there are over a 100,000 T cells, around 28,000 tumor cells, and only several hundred dendritic cells. Let’s plot the \(x\)- and \(y\)-positions of the cells (Figure 11.23).

```
ggplot(filter(brcalymphnode, class %in% c("T_cells", "Tumor")),
aes(x = x, y = y, col = class)) + geom_point(shape = ".") +
facet_grid( . ~ class) + guides(col = FALSE)
```

► Question 11.12

► Solution

To use the functionality of the **spatstat** package, it is convenient to convert our data in `brcalymphnode`

into an object of class *ppp*; we do this by calling the eponymous function.

```
## Marked planar point pattern: 209462 points
## Multitype, with levels = DCs, other_cells, T_cells, Tumor
## window: rectangle = [3839, 17276] x [6713, 23006] units
```

*ppp* objects are designed to capture realizations of a **spatial point process**, that is, a set of isolated points located in a mathematical space; in our case, as you can see above, the space is a two-dimensional rectangle that contains the range of the \(x\)- and \(y\)-coordinates. In addition, the points can be *marked* with certain properties. In `ln`

, the mark is simply the *factor* variable `class`

. More generally, it could be several attributes, times, or quantitative data as well. There are similarities between a marked point process and an image, although for the former, the points can lie anywhere within the space, whereas in an image, the pixels are covering the space in regular, rectangular way.

Above, we considered space in which the point process lies as a rectangle. In fact, the space is more confined, and we can compute a tighter region from the convex hull of the points.

The heavy lifting is done by the function `convhulln`

in the **geometry** package. However, as so often, a bit of data wrangling remains to be done. Namely, the `ppp`

function expects the hull to be described by a closed polygon line, while `convhulln`

presents its result as a set of line segments in no particular order. This is because `convhulln`

works not only with two-dimensional data but just as well as with \(d\)-dimensional data. The hull is then represented by \((d-1)\)-dimensional simplices. The function’s output format reflects this more general setup. Thus, the entries of `chull`

are integer indices into the array `coords`

that refer to the points defining each simplex. In our case, \(d=2\), and 1-dimensional simplices are simply line segments. So we write a little `for`

-loop that assembles them into a closed polygon line.

```
pidx = integer(nrow(chull) + 1)
pidx[1:2] = chull[1, ]
chull[1, ] = NA
for(j in 3:length(pidx)) {
wh = which(chull == pidx[j-1], arr.ind = TRUE)
stopifnot(nrow(wh )== 1)
wh[, "col"] = 3 - wh[, "col"] ## 2->1, 1->2
pidx[j] = chull[wh]
chull[wh[, "row"], ] = NA
}
pidx = rev(pidx)
```

Figure 11.24: Polygon describing the convex hull of the points in `ln`

.

```
ggplot(tibble(x = ln$x, y = ln$y)[pidx, ], aes(x = x, y = y)) +
geom_point() + geom_path() + coord_fixed()
```

We can see the polygon in Figure 11.24 and now call `ppp`

again, this time with the polygon.

```
ln = with(brcalymphnode,
ppp(x = x, y = y, marks = class, poly = coords[ pidx, ],
check = FALSE))
ln
```

```
## Marked planar point pattern: 209462 points
## Multitype, with levels = DCs, other_cells, T_cells, Tumor
## window: polygonal boundary
## enclosing rectangle: [3839, 17276] x [6713, 23006] units
```

We do not have to use the convex hull to define the space on which the point process is considered. Alternatively, we could have provided an image mask to `ppp`

that defines the space based on prior knowledge; or we could use density estimation on the sampled points to only identify a region in which there is a high enough point density, ignoring sporadic outliers. These choices are part of the analyst’s job when considering spatial point processes.

One of the most basic questions of spatial statistics is whether neighboring points are “clustering”, i.e., whether and to what extent they are closer to each other than expected “by chance”; or perhaps the opposite, whether they seem to repel each other. There are many examples where this kind of question can be asked, for instance

crime patterns within a city,

disease patterns within a country,

soil measurements in a region.

It is usually not hard to find reasons why such patterns exist: good and bad neighborhoods, local variations in lifestyle or environmental exposure, the common geological history of the soil. Sometimes there may also be mechanisms by which the observed events attract or repel each other – the proverbial “broken windows” in a neighborhood, or the tendency of many cell types to stick close to other cells.

The cell example highlights that spatial clustering (or anticlustering) can depend on the objects’ attributes (or marks, in the parlance of spatial point processes). It also highlights that the answer can depend on the length scale considered. Even if cells attract each other, they have a finite size, and cannot occupy the same space. So there will be some minmal distance between them, on the scale of which they essentially repel each other, while at further distances, they attract.

To attack these questions more quantitatively, we need to define a probabilistic model of what we expect *by chance*. Let’s count the number of points lying in a subregion, say, a circle of area \(a\) around a point \(p=(x,y)\); call this \(N(p, a)\)148 As usual, we use the uppercase notation \(N(p, a)\) for the random variable, and the lowercase \(n(p, a)\) for its realizations, or samples. The mean and covariance of \(N\) provide first and second order properties. The first order is the intensity of the process:

\[\begin{equation}
\lambda(p) = \lim_{a\rightarrow 0} \frac{E[ N(p, a)]}{a}.
\tag{11.6}
\end{equation}\]

Here we used infinitesimal calculus to define the local intensity \(\lambda(p)\). As for time series, a stationary process is one where we have homogeneity all over the region, i.e., \(\lambda(p) = \text{const.}\); then the intensity in an area \(A\) is proportional to the area: \(E[N(\cdot, A)] = \lambda A\). Later we’ll also look at higher order statistics, such as the spatial covariance

\[\begin{equation}
\gamma(p_1, p_2) = \lim_{a \rightarrow 0}
\frac{E \left[ \left(N(p_1, a) - E[ N(p_1, a)] \right)
\left(N(p_2, a) - E[ N(p_2, a)] \right) \right]}{a^2}.
\tag{11.7}
\end{equation}\]

If the process is stationary, this will only depend on the relative position of the two points (the vector between them). If it only depends on the distance, i.e., only on the length but not on the direction of the vector, it is called second order isotropic.

Figure 11.25: Rain drops falling on the floor are modelled by a Poisson process. The number of drops falling on a particular spot only depends on the rate \(\lambda\) (and on the size of the spot), but not on what happens at other spots.

The simplest spatial process is the Poisson process. We will use it as a null model against which to compare our data. It is stationary with intensity \(\lambda\), and there are no further dependencies between occurrences of points in non-overlapping regions of the space. Moreover, the number of points in a region of area \(A\) follows a Poisson distribution with rate \(\lambda A\).

To estimate the intensity, divide up the area into subregions, small enough to see potential local variations of \(\lambda(p)\), but big enough to contain a sufficient sample of points. This is analogous to 2D density estimation, and instead of hard region boundaries, we can use a smooth kernel function \(K\).

\[\begin{equation}\label{eq:density.ppp}
\hat{\lambda}(p) = \sum_i e(p_i) K(p-p_i).
\tag{11.8}
\end{equation}\]

The kernel function depends on a smoothing parameter, \(\sigma\), the larger it is, the larger the regions over which we compute the local estimate for each \(p\). \(e(p)\) is an edge correction factor, and takes into account the estimation bias caused when the support of the kernel (the “smoothing window”) would fall outside the space on which the point process is defined. The function `density`

, which is defined for *ppp* objects in the **spatstat** package, implements Equation [eq:density.ppp].

Figure 11.26: Intensity estimate for the cells marked `Tumor`

in `ppp`

. The support of the estimate is the polygon that we specified earlier on (Figure 11.24).

The plot is shown in Figure 11.26.

► Question 11.13

How does the estimate look without edge correction?

► Solution

`density`

gives us as estimate of the *intensity* of the point process. A related, but different task is the estimation of the (conditional) *probability* of being a particular cell class. The function `relrisk`

computes a nonparametric estimate of the spatially varying risk of a particular event type. We’re interested in the probability that a cell that is present at particular spatial location will be a tumor cell (Figure 11.28).

If we pick a point at random in our spatial process, what is the distance \(W\) to its nearest neighbor? For a homogenous Poisson process, the cumulative distribution function of this distance is

\[\begin{equation}
G(w) = P(W\leq w) = 1-e^{-\lambda \pi w^2}.
\tag{11.9}
\end{equation}\]

Plotting \(G\) gives a way of noticing departure from the homogenous Poisson process. An estimator of \(G\), which also takes into account edge effects (Baddeley 1998; Ripley 1988), is provided by the function `Gest`

of the **spatstat** package.

```
## Function value object (class 'fv')
## for the function r -> G(r)
## ................................................................
## Math.label
## r r
## theo G[pois](r)
## han hat(G)[han](r)
## rs hat(G)[bord](r)
## km hat(G)[km](r)
## hazard hat(h)[km](r)
## theohaz h[pois](r)
## Description
## r distance argument r
## theo theoretical Poisson G(r)
## han Hanisch estimate of G(r)
## rs border corrected estimate of G(r)
## km Kaplan-Meier estimate of G(r)
## hazard Kaplan-Meier estimate of hazard function h(r)
## theohaz theoretical Poisson hazard function h(r)
## ................................................................
## Default plot formula: .~r
## where "." stands for 'km', 'rs', 'han', 'theo'
## Recommended range of argument r: [0, 20.998]
## Available range of argument r: [0, 52.443]
```

Figure 11.29: Estimates of \(G\), using three different edge effect corrections –which here happen to essentially lie on top of each other– and the theoretical distribution for a homogenous Poisson process.

The printed summary of the object `gln`

gives an overview of the computed estimates; further explanations are in the manual page of `Gest`

. In Figure 11.29 we see that the empirical distribution function and that of our null model, a homogenous Poisson process with a suitably chosen intensity, cross at around 4.5 units. Cell to cell distances that are shorter than this value are less likely than for the null model, in particular, there are essentially no distances below around 2; this, of course, reflects the fact that our cells have finite size and cannot overlap the same space. There seems to be trend to avoid very large distances –compared to the Poisson process–, perhaps indicative of a tendency of the cells to cluster.

The average number of points found near a randomly picked point of the process will indicate fluctuations from the complete spatial randomness model, for which this number grows with the distance \(r\) as the area of the circle, \(\pi r^2\).

The \(K\) function (variously called *Ripley’s \(K\)-function* or the *reduced second moment function*) of a stationary point process is defined so that \(\lambda K(r)\) is the expected number of (additional) points within a distance \(r\) of a given, randomly picked point. Remember that \(\lambda\) is the intensity of the process, i.e., the expected number of points per unit area. The \(K\) function is a second order moment property of the process.

The definition of \(K\) can be generalized to inhomogeneous point processes and written as in (Baddeley, Moller, and Waagepetersen 2000),

\[\begin{equation}
K_{\scriptsize \mbox{inhom}}(r)= \sum_{i,j} 𝟙_{d(p_i, p_j) \le r} \times \frac{e(p_i, p_j, r)} { \lambda(x_i) \lambda(x_j) },
\tag{11.10}
\end{equation}\]

where \(d(p_i, p_j)\) is the distance between points \(p_i\) and \(p_j\), and \(e(p_i, p_j, r)\) is an edge correction factor149 See the manual page of `Kinhom`

for more.. For estimation and visualisation, it is useful to consider a transformation of \(K\) (and analogously, of \(K_{\scriptsize \mbox{inhom}}\)), the so-called \(L\) function.

\[\begin{equation}
L(r)=\sqrt{\frac{K(r)}{\pi}}.
\tag{11.11}
\end{equation}\]

For a complete spatial random pattern, the theoretical value is \(L(r) = r\). By comparing that to the estimate of \(L\) for a dataset we can learn about inter-point dependence and spatial clustering. The square root in Equation (11.11) has the effect of stabilising the variance of the estimator, so that compared to \(K\), \(L\) is more appropriate for data analysis and simulations. The computations in the function `Linhom`

of the **spatstat** package take a few minutes for our data (Figure 11.30).

```
## Function value object (class 'fv')
## for the function r -> L[inhom](r)
## ................................................................
## Math.label
## r r
## theo L[pois](r)
## border {hat(L)[inhom]^{bord}}(r)
## bord.modif {hat(L)[inhom]^{bordm}}(r)
## Description
## r distance argument r
## theo theoretical Poisson L[inhom](r)
## border border-corrected estimate of L[inhom](r)
## bord.modif modified border-corrected estimate of L[inhom](r)
## ................................................................
## Default plot formula: .~.x
## where "." stands for 'bord.modif', 'border', 'theo'
## Recommended range of argument r: [0, 694.7]
## Available range of argument r: [0, 694.7]
```

Figure 11.30: Estimate of \(L_{\scriptsize \mbox{inhom}}\), Equations (11.10) and (11.11), of the T cell pattern.

We could now proceed with looking at the \(L\) function also for other cell types, and for different tumors as well as for healthy lymph nodes. This is what Setiadi and colleagues did in their report (Setiadi et al. 2010), where by comparing the spatial grouping patterns of T and B cells between healthy and breast cancer lymph nodes they saw that B cells appeared to lose their normal localization in the extrafollicular region of the lymph nodes in some tumors.

describes how point density varies as a function of distance from a reference point. It provides a perspective inspired by physics for looking at spatial clustering. For a stationary point process, it is defined as

\[\begin{equation}
g(r)=\frac{1}{2\pi r}\frac{dK}{dr}(r).
\tag{11.12}
\end{equation}\]

For a stationary Poisson process, the pair correlation function is identically equal to 1. Values \(g(r) < 1\) suggest inhibition between points; values greater than 1 suggest clustering.

The **spatstat** package allows computing estimates of \(g\) even for inhomogeneous processes, if we call `pcf`

as below, the definition (11.12) is applied to the estimate of \(K_{\scriptsize \mbox{inhom}}\).

Figure 11.31: Estimate of the pair correlation function, Equation (11.12), of the T cell pattern.

As we see in Figure 11.31, the T cells cluster, although at very short distances, there is also evidence for avoidance.

► Question 11.14

The sampling resolution in the plot of the pair correlation function in the bottom panel of Figure 11.31 is low; how can it be increased?

► Solution

We learned to work with image data in R. Images are basically just arrays, and we can use familiar idioms to manipulate them. We can extract quantitative features from images, and then many of the analytical questions are not unlike those with other high-throughput data: we summarize the features into statistics such as means and variances, do hypothesis testing for differences between conditions, perform analysis of variance, apply dimension reduction, clustering and classification.

Often we want to compute such quantitative features not on the whole image, but for individual objects shown in the image, and then we need to first segment the image to demarcate the boundaries of the objects of interest. We saw how to do this for images of nuclei and cells.

When the interest is on the positions of the objects and how these positions relate to each other, we enter the realm of *spatial statistics*. We have explored some of the functionality of the **spatstat** package, have encountered the point process class, and we learned some of the specific diagnostic statistics used for point patterns, like Ripley’s \(K\) function.

There is a vast amount of literature on image analysis. When navigating it, it is helpful to realize that the field is driven by two forces: specific application domains (we saw the analysis of high-throughput cell-based assays) and available computer hardware. Some algorithms and concepts that were developed in the 1970s are still relevant, others have been superseeded by more systematic and perhaps computationally more intensive methods. Many algorithms imply certain assumptions about the nature of the data and and scientific questions asked, which may be fine for one application, but need a fresh look in another. A classic introduction is

*The Image Processing Handbook*(Russ and Neal 2015), which now is its seventh edition.For spatial point pattern analysis, Diggle (2013; Ripley 1988; Cressie 1991; Chiu et al. 2013).

Load some images from your personal photo library into R and try out the manipulations from Section 11.6 on them.

▢

Explore the effect of the parameter `lambda`

in the `propagate`

function (Sections 11.12, 11.13) using a **shiny** app that displays the `cellbodies`

image as in Figure 11.19.

▢

Use the `fft`

function (package **stats**) to compute the periodogram of some of your images from Exercise 11.1.

How do the periodograms of different images compare?

How does it change when you apply a filter like in Section 11.8.2?

Save an image in

`jpeg`

format with a high compression (low quality) parameter, and load the image again. How did the periodogram change?

▢

Compute and display the Voronoi tesselation for the European cities from Chapter 9. You can either use their PCA- oder MDS-coordinates in a 2D plane with Euclidean distances, or the latitudes and longitudes using the great circle distance (Haversine formula). Optionally, you can add data for further places from your favorite parts of the world, and overlay with political boundaries.

▢

Download 3D image data from light sheet microscopy150 For instance, http://www.digital-embryo.org, load it into an **EBImage** *Image* object and explore the data.

▢

Baddeley, Adrain, Jesper Moller, and Rasmus Waagepetersen. 2000. “Non- and Semiparametric Estimation of Interaction in Inhomogeneous Point Patterns.” *Statistica Neerlandica* 54: 329–50.

Baddeley, Adrian J. 1998. “Spatial Sampling and Censoring.” In *Stochastic Geometry: Likelihood and Computation*, edited by O. E. Barndorff-Nielsen, W. S. Kendall, and M. N. M. van Lieshout, 37–78. Chapman; Hall.

Boland, Michael V., and Robert F. Murphy. 2001. “A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells.” *Bioinformatics* 17 (12): 1213–23.

Carpenter, Anne E, Thouis R Jones, Michael R Lamprecht, Colin Clarke, In Han Kang, Ola Friman, David A Guertin, Joo Han Chang, Robert A Lindquist, and Jason Moffat. 2006. “CellProfiler: Image Analysis Software for Identifying and Quantifying Cell Phenotypes.” *Genome Biology* 7: R100.

Chaumont, Fabrice de, Stéphane Dallongeville, Nicolas Chenouard, Nicolas Hervé, Sorin Pop, Thomas Provoost, Vannary Meas-Yedid, et al. 2012. “Icy: an open bioimage informatics platform for extended reproducible research.” *Nature Methods* 9: 690–96.

Chiu, Sung Nok, Dietrich Stoyan, Wilfrid S. Kendall, and Joseph Mecke. 2013. *Stochastic Geometry and Its Applications*. Springer.

Cressie, Noel A. 1991. *Statistics for Spatial Data*. John Wiley; Sons.

Diggle, Peter J. 2013. *Statistical Analysis of Spatial and Spatio-Temporal Point Patterns*. Chapman; Hall/CRCs.

Held, M., M. H. A. Schmitz, B. Fischer, T. Walter, B. Neumann, M. H. Olma, M. Peter, J. Ellenberg, and D. W. Gerlich. 2010. “CellCognition: Time-Resolved Phenotype Annotation in High-Throughput Live Cell Imaging.” *Nature Methods* 7: 747.

Holmes, Susan, Adam Kapelner, and Peter P Lee. 2009. “An Interactive Java Statistical Image Segmentation System: GemIdent.” *Journal of Statistical Software* 30 (10).

Jones, T., A. Carpenter, and P. Golland. 2005. “Voronoi-Based Segmentation of Cells on Image Manifolds.” *Computer Vision for Biomedical Image Applications*, 535.

Laufer, Christina, Bernd Fischer, Maximilian Billmann, Wolfgang Huber, and Michael Boutros. 2013. “Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping.” *Nature Methods* 10: 427–31.

Pau, Grégoire, Florian Fuchs, Oleg Sklyar, Michael Boutros, and Wolfgang Huber. 2010. “EBImage R Package for Image Processing with Applications to Cellular Phenotypes.” *Bioinformatics* 26 (7): 979–81.

Rajaram, S., B. Pavie, L. F. Wu, and S. J. Altschuler. 2012. “PhenoRipper: software for rapidly profiling microscopy images.” *Nature Methods* 9: 635–37.

Ripley, B. D. 1988. *Statistical Inference for Spatial Processes.* Cambridge University Press.

Russ, John C., and F. Brent Neal. 2015. *The Image Processing Handbook*. 7th ed. CRC Press;

Schindelin, Johannes, Ignacio Arganda-Carreras, Erwin Frise, Verena Kaynig, Mark Longair, Tobias Pietzsch, Stephan Preibisch, et al. 2012. “Fiji: an open-source platform for biological-image analysis.” *Nature Methods* 9: 676–82.

Serra, Jean. 1983. *Image Analysis and Mathematical Morphology*. Academic Press.

Setiadi, A Francesca, Nelson C Ray, Holbrook E Kohrt, Adam Kapelner, Valeria Carcamo-Cavazos, Edina B Levic, Sina Yadegarynia, et al. 2010. “Quantitative, Architectural Analysis of Immune Cell Subsets in Tumor-Draining Lymph Nodes from Breast Cancer Patients and Healthy Lymph Nodes.” *PLoS One* 5 (8): e12420.

Vetterli, Martin, Jelena Kovačević, and Vivek Goyal. 2014. *Foundations of Signal Processing*. Cambridge University Press.

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