Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Segmentation of Dense Leukocyte Clusters
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
Cell Cluster Image Segmentation on Form Analysis
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Touching Cells Splitting by Using Concave Points and Ellipse Fitting
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
IEEE Transactions on Image Processing
Biological interpretation of morphological patterns in histopathological whole-slide images
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Computers in Biology and Medicine
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This paper presents a novel, fast and semi-automatic method for accurate cell cluster segmentation and cell counting of digital tissue image samples. In pathological conditions, complex cell clusters are a prominent feature in tissue samples. Segmentation of these clusters is a major challenge for development of an accurate cell counting methodology. We address the issue of cluster segmentation by following a three step process. The first step involves pre-processing required to obtain the appropriate nuclei cluster boundary image from the RGB tissue samples. The second step involves concavity detection at the edge of a cluster to find the points of overlap between two nuclei. The third step involves segmentation at these concavities by using an ellipse-fitting technique. Once the clusters are segmented, individual nuclei are counted to give the cell count. The method was tested on four different types of cancerous tissue samples and shows promising results with a low percentage error, high true positive rate and low false discovery rate.