Automated Clump Splitting for Biological Cell Segmentation in Microscopy Using Image Analysis

Abstract

Formation of clumps due to touching or overlapping of individual objects in an image is common. The process is natural in some cell cultures, for instance, yeast cells typically grow in clumps. Automated analysis of images containing such clumps requires the capability to split them into their constituent objects. Failure of the segmentation methods to split the clumps leads to the requirement of developing clump splitting methods to be used as post-processing step towards overall segmentation. The goal of this thesis work is to study and develop an automated method for splitting cell clumps in images of biological cells. To achieve this goal we studied previous clump splitting methods found in the literature. One of the best methods is based on defining split lines by detecting and linking concavity points. We found that this method has deficiencies in it and first modified it to achieve improved clump splitting results. We also developed a novel method for clump splitting following a similar approach.

Like any other concavity point-based clump splitting method, both these methods start with finding all the concavity points on the contour of the clumps. Contrary to the original method, these methods look for every possible valid concavity point in a concavity region using curvature analysis, thus minimizing false split lines as well as under-segmentation. The modified method then uses Delaunay triangulation to narrow down the list of all the possible split lines between all the concavity points to a list of candidate split lines. Finally, it uses a set of features such as saliency and alignment to define a cost function. The best split line is found for each concavity point yielding the minimum value for the cost function. On the other hand, the novel method uses variable size rectangular window to search for the concavity point-pairs forming the split lines. This makes the method less dependent on user-defined parameters. We also propose some post-processing steps that remove some non-cellular objects based on a priori information on cell shapes.

We compared the performance of these two methods with the performance of the original method and of a widely used method that is based on the watershed transform. Three different sets of images of yeast cells were used. Precision and recall analysis was used to show that the two methods proposed in this thesis outperform the two methods taken from the literature. Although the targeted application of the methods is splitting of cell clumps, it can be applied to split clumps of other convex objects as well. /Kir10