Segmentation of haematopoeitic cells in bone marrow using circle detection and splitting techniques

pre-printBone marrow evaluation is indicated when peripheral blood abnormalities are not explained by clinical, physical, or laboratory findings. In this paper, we propose a novel method for segmentation of haematopoietic cells in the bone marrow from scanned slide images. Segmentation of clumped cells is a challenging problem for this application. We first use color information and morphology to eliminate red blood cells and the background. Clumped haematopoietic cells are then segmented using circle detection and a splitting algorithm based on the detected circle centers. The Hough Transform is used for circle detection and to find the number and positions of circle centers in each region. The splitting algorithm is based on detecting the maximum curvature points, and partitioning them based on information obtained from the centers of the circles in each region. The performance of the segmentation algorithm for haematopoietic cells is evaluated by comparing our proposed method with a hematologist's visual segmentation in a set of 3748 cells

Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy Images

Cataloged from PDF version of article.More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms

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

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. /Kir1

Image analysis and statistical modeling for applications in cytometry and bioprocess control

Today, signal processing has a central role in many of the advancements in systems biology. Modern signal processing is required to provide efficient computational solutions to unravel complex problems that are either arduous or impossible to obtain using conventional approaches. For example, imaging-based high-throughput experiments enable cells to be examined at even subcellular level yielding huge amount of image data. Cytometry is an integral part of such experiments and involves measurement of different cell parameters which requires extraction of quantitative experimental values from cell microscopy images. In order to do that for such large number of images, fast and accurate automated image analysis methods are required. In another example, modeling of bioprocesses and their scale-up is a challenging task where different scales have different parameters and often there are more variables than the available number of observations thus requiring special methodology. In many biomedical cell microscopy studies, it is necessary to analyze the images at single cell or even subcellular level since owing to the heterogeneity of cell populations the population-averaged measurements are often inconclusive. Moreover, the emergence of imaging-based high-content screening experiments, especially for drug design, has put single cell analysis at the forefront since it is required to study the dynamics of single-cell gene expressions for tracking and quantification of cell phenotypic variations. The ability to perform single cell analysis depends on the accuracy of image segmentation in detecting individual cells from images. However, clumping of cells at both nuclei and cytoplasm level hinders accurate cell image segmentation. Part of this thesis work concentrates on developing accurate automated methods for segmentation of bright field as well as multichannel fluorescence microscopy images of cells with an emphasis on clump splitting so that cells are separated from each other as well as from background. The complexity in bioprocess development and control crave for the usage of computational modeling and data analysis approaches for process optimization and scale-up. This is also asserted by the fact that obtaining a priori knowledge needed for the development of traditional scale-up criteria may at times be difficult. Moreover, employment of efficient process modeling may provide the added advantage of automatic identification of influential control parameters. Determination of the values of the identified parameters and the ability to predict them at different scales help in process control and in achieving their scale-up. Bioprocess modeling and control can also benefit from single cell analysis where the latter could add a new dimension to the former once imaging-based in-line sensors allow for monitoring of key variables governing the processes. In this thesis we exploited signal processing techniques for statistical modeling of bioprocess and its scale-up as well as for development of fully automated methods for biomedical cell microscopy image segmentation beginning from image pre-processing and initial segmentation to clump splitting and image post-processing with the goal to facilitate the high-throughput analysis. In order to highlight the contribution of this work, we present three application case studies where we applied the developed methods to solve the problems of cell image segmentation and bioprocess modeling and scale-up