NISTIR 7738

Automated Extraction of Cellular 

Features for a Potentially Robust 

Classification Scheme

Shwetadwip Chowdhury 

August 2010
NISTIR 7738

Automated Extraction of Cellular 

Features for a Potentially Robust 

Classification Scheme

Shwetadwip Chowdhury 1, 2 

1 Information Technology Laboratory

2 Duke University

August 2010

U.S. Department of Commerce 

Gary Locke, Secretary 

National Institute of Standards and Technology 

 Patrick D. Gallagher, Director 
Abstract

The advent of new imaging technologies has allowed faster and higher-resolution cellular image acquisition by 

light microscopy. This has directly aided clinical research by allowing doctors and other researchers to better 

visualize cells and cellular components, which improves their ability to provide accurate prognosis, diagnosis, 

and treatment of a variety of diseases. However, a major bottleneck now is the manual evaluation of such 

images. Especially with the sheer amount of data being generated from these images, manual evaluation and 

analysis is becoming increasingly cumbersome. To address this concern, there has been extensive work on 

researching methods to automatically extract cellular and subcellular features that give morphological and 

functional insight into the cell. Some examples include extracting size/shape of cell boundaries, density of 

mitochondria, expression level of proteins, nuclear/cell size ratio, etc. 

Here, we specifically focus on techniques to robustly extract information about the cellular proteins - actin, 

myosin, and phosphotyrosine - from images of cells specifically labeled with fluorescent moieties localized to 

the specific proteins. These proteins are known to be important components in the signaling pathways 

regulating cellular proliferation. Being able to quickly and automatically extract information about such 

proteins from raw images will greatly aid biological research, especially on topics related to understanding the 

role of the microenvironment in disease. Such extracted features can then be used for cellular 

classification. Future work can incorporate our feature extraction capabilities to classify a cell into categories 

with known feature parameters. 

 
Acknowledgements 

I would like to thank my advisor, Afzal Godil of NIST, for his guidance and support throughout this 

project. I would also like to thank Antonio Cardone of NIST, for his expertise on many relevant image 

processing topics as well as Kiran Bhadriraju of NIST for providing biomedical guidance, support, and 

direction to this project. Finally, I would like to acknowledge NIST’s Summer Undergraduate 

Research Fellowship (SURF), which provided the funding and support for me to work on the project.
Introduction 

Importance and Project Aim 

In the past few decades, biomedical research has revolutionized healthcare by addressing many issues 

posed by limitations in medical knowledge or technology. Throughout its growth, however, much 

emphasis has still been placed on the ability to generate and analyze quantitative/visual information 

from a biological system. In such pursuits, medical imaging science has rapidly developed and, 

especially with dynamic and in-vivo organ/tissue systems, given birth to imaging techniques such as 

microscopy, Magnetic Resonance Imaging (MRI), ultrasound, and nuclear medicine which are now 

crucial in many modern diagnostic and prognostic procedures. 

However, as medical imaging technology develops, allowing faster and higher-resolution image 

acquisition, acquisition speeds are steadily surpassing the rate at which experts can feasibly do 

accurate analysis. As such, a major bottleneck in the course from image acquisition to disease 

diagnosis is leaning increasingly towards image interpretation. This leads directly to a need for an 

automated image analysis ability that can keep up with the speeds of modern medical data generation 

techniques. 

Generalized automated image analysis has received much attention from computer vision research, and 

many techniques developed in that field are applicable for our use. In particular, we focus on featurebased methods for object recognition. Feature-based methods are quite popular in computer vision, 

and several methods to extract features and cluster objects based on similarity are available [1-4]. 

However, many of these algorithms extract features describing mainly the physical appearance of the 

corresponding object. In biomedical settings, however, such features may often be too superficial. 

Especially in work to recognize and classify cells, the physical shape of the cell is heavily influenced 

by the sample’s preparation, medium, and other extrinsic factors and cannot be attributed solely to the 

cell’s identity or its other intrinsic properties. In this work, we aim to provide and discuss a few 

features for automatic cellular classification that have direct biological relevance to the cell itself, 

which can later potentially allow for a more robust cellular classification ability. Also, because of their 

biological bearing, these features should be able to be easily communicated to and understood by 

medical experts. 

Paper Outline 

In this paper, we will begin by giving a brief background. Because of the biomedical setting of this 

work, some emphasis will be placed on discussing relevant topics that are routinely handled by the 

biologists, though not directly associated with the main feature descriptor portions of the project. We 

will then proceed to describe features that may be useful for classification and discuss their possible 

extraction methods. We will then go over the limitations of this work and discuss possible future 

directions. 

Background 
Microscopy 

When attempting to visualize biological systems smaller than what can be seen with the unaided eye, 

microscopy becomes a popular imaging technique. Optical microscopy, which uses visible light and a 

setup of lenses to magnify images of the sample, has rapidly gained use in this regard since its 

introduction via the simple light microscope several years ago. With the invention of digital cameras 

and other optical detectors, the analog signal from the sample image’s magnification can be converted 

into a digital form. From here, established techniques from computer vision and digital signal 

processing can be applied to gain deeper insight into the data as well as pave the way towards 

automated image analysis. Especially along with its speed, ability to extract spectral information, and 

a minimal requirement for sample preparation, optical microscopy is a preferred, and in many cases 

more appropriate, microscopic technique than its electron or scanning-probe microscopy counterparts 

for many situations. 

Fluorescence microscopy is a widely popular optical imaging technique used to image biological 

samples such as tissues sections [5]. For this technique, the biologist prepares the sample by tagging 

proteins of interest with fluorescent markers. The fluorescence excitation source is exposed to the 

sample, and at each point, the emitted fluorescence from the markers is captured by a CCD detector. 

The resulting image is then processed for image interpretation after feature extraction. 

Biologically Relevant Molecules: Actin, Myosin, Focal Adhesion 

In this particular project, we focus on visualizing actin, myosin, and focal adhesion protein patterns, so 

we briefly describe their roles in cellular function below [6]: 

Actin is a protein with a critical role in the makeup of the cell’s cytoskeleton. Important cellular 

processes that actin takes part in include maintenance of cell shape and structure, cell motility, and cell 

division. They appear as long strands in the cell. 

Myosin is a protein that also contributes to cell motility and has importance in muscle fiber cells as 

well as all other cell types in the body, where it interacts with actin to provide contractile force. 

Focal adhesions are protein assemblies that link an individual cell to the extracellular matrix, which 

provides basic structure and organization to cells in tissues and organs. As such, focal adhesions are 

important in allowing inter-cell communications, which form the basis for biological synchrony and 

cooperation. Biologically, focal adhesions are expected to appear at the ends of actin strands. 

Materials 

Data 

Cell images were taken by a fluorescence microscope and saved as 16-bit TIFF format gray scale (14-

bit wide gray channel). Cells were fluorescently tagged for actin, myosin, and focal adhesions and 

before imaging. Cell body was also imaged using its natural intrinsic fluorescence at near infra-red 

excitation wavelengths. The final data set contained 60 cell objects, with each object having been 
imaged for its individual tags and body. It is assumed that there was no cellular movement while 

imaging the different tags so that image registration is not an issue. 

Tools 

MATLABi,ii was used for the image processing and feature extraction techniques applied to the cellular 

images. The computer used was a 4 Gb, 3.2 GHz Dell PC. 

Methods 

Methods Introduction 

This work is a subset in an umbrella project, which ultimately aims to extract robust, localized, and 

biologically-grounded features from cellular images. The over-arching project is divided into two 

parts: 

1. Segmentation of localized regions within individual cells based on characteristics of interest 

2. Extraction of features from the localized regions of interest (ROIs) outputted from part (1) 

described above. 

The work described throughout the rest of this paper seeks to explore possibilities for part (2), and 

results for part (1) are saved for a future paper. Because part (2) requires a ROI from which to extract 

features from, for the sake of explanation, we consider our ROI to be a whole individual cell and most 

of our extracted features are taken as cellular, NOT sub-cellular, descriptors. Of course, most of the 

techniques described later can be applied equally as easily to localized regions in cells. 

Image Preprocessing Steps 

1. Each TIF image had its image intensity histogram equalized, which converted the original 

image with any predominant gray-scale pixel-values into an image where the full range of grayscale values were used relatively equally among its pixels. This has the effect of increasing the 

global contrast of the image. 

2. Much of the background was cropped off so that the cropped image would contain pixels 

predominantly inside the cell 

3. The image was converted to gray scale 

Cell Segmentation 

We first choose a ROI to conduct later feature extractions on. For preliminary results, we choose a 

whole individual cell to be our ROI and we seek to get an accurate boundary for it. To do so, we use 

_____________________ 

i Registered trademark of The Mathworks, Inc. 3 Apple Hill Drive Natick, MA 01760-2098. 

ii Any mention of commercial products within this report is for information only; it does not imply recommendation or 

endorsement by NIST 
Otsu’s 2D segmentation method [7] with n = 3 levels. After completing Otsu, the background pixels 

will predominantly be grouped into class 0, while the cellular pixels will be classified into classes 1 

and 2. We mask for pixels that were not grouped into class 0 (thus capturing cellular pixels). We then 

perform basic binary morphological operations to smooth out the boundaries of the binary mask. After 

this processing, drawing the boundary on this binary image is a simple problem. From this boundary, 

typical features such as area, perimeter, and principal axes can be easily extracted. We find that the 

boundary extraction process works relatively well on various cellular images. Especially because this 

is to obtain only preliminary ROIs, this process is satisfactory for our goal. This process is graphically 

traced in Figure 1 below: 

Figure 1 We start off with a raw image of the cell body (a). We run Otsu algorithm (n=3) to 

group the pixel intensities into 3 classes, where the background pixels fall mostly into one 

class, shown in black in (b). We mask for all non-background pixels (1c) and smooth out 

edges using standard morphological operations (1d). The border extracted from (1d) is 

shown superimposed on the original raw image of the cell body in (1e). Again, we stress that 

this border is used just to obtain a preliminary ROI to work further with. In the future, this 

ROI will often divided into smaller ROIs for more localized analysis. 

Nuclear Segmentation 

Extracting the cell nucleus boundary is an interesting extension to extracting the cell body boundary. 

It is medically known that many diseases and afflictions physically manifest in an abnormal 

nucleus/cell-body size ratio. Being able to accurately detect this ratio would give medical experts 

some power to detect for these afflictions. As explained above, we can compute a cell’s area relatively 

accurately from its extracted boundary. We now seek to extract the cell nucleus’s boundary, which 

will allow us to directly compute nucleus area. Furthermore, because nuclei are guaranteed to be well 

separated, extracting nuclei boundary would be a more robust method for counting the number of cells 
in an image than extracting cell boundaries. The process described below details the procedure for 

nuclei extraction and Figure 2 provides an illustrative outline. 

1. We start with an image of the cell that illuminates the nuclei well. For our purposes, we choose to start 

with the image showing the cellular focal adhesion fluorescent tags. 

2. We blur the image. The purpose of this is to preserve mean intensities of significant portions of the 

cells while disregarding localized intensity spikes or dips that could result from segregate proteins or 

random signal variation. A typical blur kernel is an 11x11 array of ones. 

3. We raise each image pixel intensity value to its 4th power. Because the nuclei pixels will have higher 

average intensity from the rest of the cell, this will accentuate the intensity difference between the 

nucleus and the rest of the cell. 

4. We convolve the image from step 3 with a 2D Gaussian profile. The resulting convolution will have 

maxima corresponding roughly to the centers of the nuclei. This allows us to locate centers of nuclei. 

Some maxima may have to be thresholded to account for localized intensity spikes in the original image 

not eliminated by the blurring in step 2. 

5. We threshold the image from step 3. Because the nuclei have been accentuated from the rest of the cell, 

the threshold value should robustly work across many similar images. This step should output a mask 

that clearly shows the nuclei, though it may perhaps show some other disconnected and subsidiary 

structures from the original image. 

6. From the nuclei centers computed from step 4, we flood fill to extract only the portions of the mask that 

are “connected” to nuclei-center pixels. This will output a binary mask containing only the nuclei. 

7. Extract the boundary from the binary mask from step 6. Nuclear features like size, principle axis, etc 

can be easily extracted 

Figure 2 We start with the focal-adhesion image of two cells (a). Blurring the image and 

raising each pixel intensity to the 4th power distinguishes the nuclei from surroundings (b), 

and centers are located by convolving with a Gaussian profile (c). Figure (b) is thresholded 

(d) and a flood-fill is carried out from the located nuclei centers, yielding a mask for the 

nuclei (e). Note that the maxima marked with a green X from (c) is thresholded out. Also 

note that the cell boundary locates only one “cell” while there are actually two cells in close 

proximity (f). Hence, number of nuclei boundaries is a more robust way of counting cells in 

an image. 
Inter-Protein Correlation 

Proteins are tailored for highly specific and tightly-controlled functions within a cell. As such, each 

protein’s shape, location, and affinity for other proteins is at least partly determined by its function. 

Using fluorescent microscopy, protein location can be directly targeted, and a high correlation between 

cellular locations of two distinct proteins can imply, at least roughly, a similarity or association in 

function. In terms of image analysis, a simple pixel-by-pixel intensity correlation between fluorescent 

stains of the proteins in question will provide this metric and can be a useful feature. 

 

We show below the correlation between actin/myosin and actin/focal adhesion (FA) images of a 

sample cell. Biologically, actin and myosin have associated functions and appear in similar cell 

locations. On the other hand, FAs link cells to the extracellular matrix and therefore their correlation 

with actin is not expected to be as high as actin/myosin correlation. Figure 3 below shows this. 

Figure 3 Myosin and actin are biologically known to have associated functions. 

As expected, the correlation between actin and myosin shown above is much 

higher than between actin and FA. 

Bulk Texture 

Another common metric used to describe an ROI is its texture features. Intuitively, these features 

describe properties such as image smoothness, roughness, homogeneity, etc. Statistical texture 

features are calculated from the image histogram, and they provide bulk measures for the image with 

no directional information. These features are well established [8] and are listed below. 
Table 1 Bulk texture features calculated from the image histogram. L is the number of pixel 

intensities in the image. zi is the number of pixels with intensity i. p(zi) is the probability that 

an image pixel has intensity i.

Directional Texture 

Bulk texture values calculated from the image histogram do not contain any information about relative 

orientations of pixels. Some proteins, particularly actin, occur in highly directional patterns. In 

situations like these, bulk texture features will not capture many specific properties, and a textureanalysis procedure that considers distribution of intensities as well as relative positions of pixels is 

needed. 

A popular method [9] to do this uses a co-occurrence matrix to calculate directional texture values. 

For an image with L pixel intensities, ranging from 0 to ZL-1, co-occurrence matrix M is of size LxL. 

The entry at position (i,j) in the matrix is the number of times that image pixel pairs at a user-specified 

offset occur simultaneously with intensity Zi and Zj. Different co-occurrence matrices will be 

calculated for different offsets. Mathematically, the matrix for image I of size X by Y pixels can be 

written as: 

ܯሺΔ௫,Δ௬ሻሺ݅, ݆ሻ ൌ ෍ ෍ቄ

1 ݂݅ ܫሺ݉, ݊ሻ ൌ ݅ ܽ݊݀ ܫሺ݉ ൅ Δݔ, ݊ ൅ Δݕሻ ൌ ݆

݁ݏ݈݁ 0

௒

௡

௑

௠

where ܯሺΔ௫,Δ௬ሻ is the particular co-occurrence matrix at the offset specified by ሺΔݔ, Δݕሻ. From the cooccurrence matrix, texture features along the offset can be found: 
Table 2 Equations to calculate directional texture features 

from co-occurrence matrix 

where (i,j) iterate through the co-occurrence matrix and 

݌ሺ݅, ݆ሻ ൌ 

ܯሺ݅, ݆ሻ

∑௠,௡ ܯሺ݉, ݊ሻ

Extracting Orientations and Coherence 

With cells containing highly directional information, it is useful to extract features that describe 

prominent orientations. Particularly in terms of this work, extraction and analysis of the orientations of 

cellular actin fibers can allow study into cytoskeleton structure, cell phase, cell motility, etc. We 

briefly describe an approach for extracting image orientations proposed in [10] that is based on taking 

pixel-by-pixel intensity gradients rom f the image. 

We define gradient vector ࡳ ൌ ܩۃ௫, ܩ௬ۄ at each position in image I, such that: 

൤

ሻݕ ,ݔ௫ሺܩ

ൌ ሻ൨ݕ ,ݔ௬ሺܩ

ۏ

ێ

ێ

ۍ

ߜ

ሻݕ ,ݔሺܫ ݔߜ

ߜ

ےሻݕ ,ݔሺܫ ݕߜ

ۑ

ۑ

ې

For each location in I, we use a surrounding region W (often a weighted Gaussian region) to compute 

ࡳ vector

෢࢙ ൌ ܩۃ௫௫ െ ܩ௬௬, 2 ܩ௫௬ۄ such that: 

ܩ௫௫ ൌ ෍ ܩଶ

ௐ

 ܩ௬௬ ൌ ෍ ܩ௬

ଶ

ௐ

 ܩ௫௬ ൌ ෍ ܩ௫ܩ௬

ௐ ௫

It is shown in [10] that orientation ሺ ߠ ሻ is robustly computed at the pixel level by: 

൞ ൌ ߠ

߶ ൅

1

0 ൑ ߶ ߨ 2

߶ െ

1

0 ൒ ߶ ߨ 2

 where ߶ ൌ

1

࢙෢ࡳ ס 2
The coherence Coh, a metric for how unidirectional orientations are in localized area W, is also 

included in [10]: 

݋ܥ݄ ൌ ห∑ௐ ࡳ

ห࢙෢

ห࢙෢ࡳௐห∑

In our case, when applied to images of cellular actin, ߠሺݔ, ݕሻ and ݋ܥ݄ሺݔ, ݕሻ gives the orientation and 

directional uniformity respectively of actin fibers at each point (x,y) in image I. We construct a 

histogram of oriented gradients (HOG) directly from ߠሺݔ, ݕሻ, which allows analysis of features such as 

predominant actin directions and uniformity of actin directions directly from typical histogram 

descriptors. We show an example of calculated orientations, coherence, and HOG in Figure 4 below: 

Figure 4 Raw image of cellular actin (a) is processed to 

extract the coherence (b) and histogram of oriented gradients 

(c) from the pixel by pixel orientations (d). 

There has been work done to distinguish regions of a cell based on differences in orientation of actin 

fiber. A potential scheme to do this can be based on segmenting between regions with significant actin 

orientation differences. Features that can directly be extracted from this can include average sizes of 

regions with relative actin uniformity, number and locations of such regions, etc. Figure 5 below 

shows an example relating to this method: 
Figure 5 Raw image of cellular actin (a) is processed to extract the 

orientations (b). The orientations are then segmented based on significant 

value changes (c) to extract borders bounding regions of relative uniform 

Importance of Extracting Actin 

As explained before, actin is an important biomolecule responsible for making up the cytoskeleton and 

critical for cell movement. To a specialized biologist, it may be important to have access to features 

describing the actin at single-fiber level. For such cases, it is imperative that actin can be robustly 

extracted from surroundings, which will in turn improve the accuracy of extracted actin features 

described later. 

We present below a preliminary actin extraction scheme. However, we stress that this extraction 

scheme needs further development before public use. 

Potential Actin Extraction Scheme 

The actin extraction procedure used for this work has been adapted from [11] and we outline it below. 

Extraction Procedure

1. Define berscore ge to be fi ima ܫ௕௙ሺݔ, ݕሻ to be same size as input image ܫሺݔ, ݕሻ. Initialize ܫ ௕௙to contain only zeros 

2. Define ernel a k ܭሺߠሻ to be a sparse array of size ሺ2݈ ൅ 1ሻ ൈ ሺ2݈ ൅ 1ሻ with ones through the center at angle ߠ

3. Iterate ߠ ௠from 0 Æ గ

ଶ

4. For each iteration ne kernels an గ , defi ܭሺߠ௠ሻ d ܭሺߠ ൅ ௠ଶ

ሻ 

ݔሺ

a. Define im breg n ze ሺ2݈ ൅ 1ሻ ൈ ሺ2݈ ൅ 1ሻ so that ܫ௥ሺݔ, ݕ = ݈ ൅1 െ݈൅ݕ

5. For each location ሺ݅, ݆ሻ i image n ܫ , ݕሻ

age su io ܫ ௥of si ሻ ܫሺ݅െ ݔെ ,݆ െ1ሻ

b. Define ܮ௜,௝ሺߠ௠ሻ to be a vector containing nonzero elements of the element-by-element product of ܫ௥ሺݔ, ݕሻ

and ܭሺߠ௠ሻ. Define ܮ௜,௝ሺߠ ൅ ௠గ

ଶ

ሻ to be the corresponding vector for ܫ௥ሺݔ, ݕሻ and ܭሺߠ ൅ ௠గ

ଶ

ሻ. 
c. From ܮ௜,௝ሺߠ௠ሻ, compute mean, standard deviation, normalized standard deviation, and correlation to nonzero entries in ܭሺߠ௠ሻ. Denote these as ܯ௜,௝ሺߠ௠ሻ, ܵܦ௜,௝ሺߠ௠ሻ, ܰܵܦ௜,௝ሺߠ௠ሻ, ݎݎ݋ܥ௜,௝ሺߠ௠ሻ, respectively. 

Define and compute corresponding values of ܯ௜, ௝ቀߠ ൅ ௠గ

ଶ

ቁ , ܵܦ௜, ௝ቀߠ ൅ ௠గ

ଶ

ቁ , ܰܵܦ௜, ௝ቀߠ ൅ ௠గ

ଶ

ቁ ,

గ௠ ൅ ߠቀ௝ ,௜ݎݎ݋ܥ

ଶ

ቁ from ܮ ቀߠ ൅ గ

௜,௝ ௠ ଶ

ቁ and ܭሺߠ ൅ ௠గ

ଶ

ሻ.

d. IF ܯ௜,௝ሺߠ௠ሻ, ܰܵܦ൅ ߨ 2⁄ ሻ , and ݎݎ݋ܥ ሺߠ௠ሻ pass user-set thresholds ௜, ሺߠ ሻ, ܰܵܦ ሺߠ ሻ⁄ܰܵܦ ሺߠ

௕௙ܫ THEN

௝ ௠ ௜,௝ ௠ ௜,௝ ௠ ௜,௝

ሺ݅, ݆ሻ ൌ max ቀݎݎ݋ܥ௜,௝ሺߠ௠ሻ, ܫ௕௙ሺ݅, ݆ሻቁ 

ሻ 2⁄ ߨ൅ ௠ߠሺ௝,௜ݎݎ݋ܥ and , ሻ௠ߠሺ ⁄௝ ,௜ܦܰܵ ሻ 2⁄ ߨ൅ ௠ߠሺ௝,௜ܦܰܵ ,ሻ 2⁄ ߨ൅ ߠሺ ܦܰܵ ,ሻ 2⁄ ߨ൅ ߠሺ ܯ IF ELSE .e

pass use

௜,௝ ௠ ௜,௝ ௠

r-set thresholds

THEN ܫ௕௙ሺ݅, ݆ሻ ൌ max ൬ݎݎ݋ܥ ߠ ൅ గ

ଶ ௜,௝ ቀ ௠ ቁ,ܫ௕௙ሺ݅, ݆ሻ൰

6. Repeat teps 4 s -5 with next iteration of θ୫

7. Output image ܫ௢௨௧ሺݔ, ݕሻ ൌ OTSU൫ܫ௕௙ሺݔ, ݕሻ, 2൯ where function OTSUሺܫ, nሻ performs the Otsu segmentation on 

image ܫ with n levels 

Figure 6 below shows actin extraction via this procedure: 

Figure 6 Raw image of cellular actin (a) is processed using the described procedure to extract a fiberscore image (b). 

The fiberscore image is thresholded to yield the final outputted image (c).

Actin Width and Density 

After binarizing the cell with respect to its actin, it becomes a simple matter to calculate features 

describing actin widths and density. 

By scanning horizontally across the binary image of actin, horizontal cuts of individual actin fibers 

(assumed to be connected white pixels) are directly extracted. By recalling that we also have 

orientation information, calculating actin cross-sectional width becomes simple trigonometry (Figure 

7). Thus, we can construct a histogram of pixel-by-pixel cross sectional widths, and features 

describing actin mean-width and uniformity can be extracted. 
Figure 7 A close up of an actin fiber from Figure 6c is 

shown. Cross-sectional actin width ܹ can be found by 

extracting a horizontal cut ܹԢ and using previously 

calculated orientation ߠ values to calculate ܹൌܹᇱ sinሺߠሻ

If we skeletonize the binary actin image, we reduce each actin fiber to single pixel widths. By doing 

so, actin density can be computed by simply taking the ratio of white (actin) pixels to total pixels in the 

skeletonized image. This computed density would have units of # actin fibers/pixel and can be used to 

estimate the number of actin fibers in a given region of known size. We note again that the accuracy 

of this computation as well as the widths-extraction mentioned above critically depends on the 

robustness of the actin extraction algorithm. 

Concluding Remarks and Future Work 

We have provided above a preliminary set of biological features that can be extracted with relative 

ease from cellular images, be easily communicated to biologists, and contribute to a more robust 

classification scheme. Though we have focused exclusively on very specific types of images, we hope 

that the methods presented here can be modified and extended to a wide range of cellular images. 

However, for this work in particular, there is still much room for growth, and we mention below a few 

potential features that could offer more biological and quantitative insight, as well as added power to 

the classification process. 

1. Descriptors for the distribution of actin fiber lengths 

2. Metric for how physically localized focal adhesion spots are to the ends of actin fibers 

3. Descriptors for the relationship between localized inter-protein correlations and localized actin 

densities. 

4. Descriptors for the relationship between localized inter-protein correlations and orientation 

coherence 

 
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