Abstract
A technique for automating the detection of lymphocytes in histopathological images is presented. The proposed system takes Hematoxylin and Eosin (H&E) stained digital color images as input to identify lymphocytes. The process involves segmentation of cells from extracellular matrix, feature extraction, classification and overlap resolution. Extracellular matrix segmentation is a two step process carried out on the HSV-equivalent of the image, using mean shift based clustering for color approximation followed by thresholding in the HSV space. Texture features extracted from the cells are used to train a SVM classifier that is used to classify lymphocytes and non-lymphocytes. A contour based overlap resolution technique is used to resolve overlapping lymphocytes.
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Kuse, M., Sharma, T., Gupta, S. (2010). A Classification Scheme for Lymphocyte Segmentation in H&E Stained Histology Images. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds) Recognizing Patterns in Signals, Speech, Images and Videos. ICPR 2010. Lecture Notes in Computer Science, vol 6388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17711-8_24
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DOI: https://doi.org/10.1007/978-3-642-17711-8_24
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