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The tissue-like P Systems, which are based on the methodology of cell and tissue behavior in a human body, are used in various areas of computation. Segmentation of medical images is one such area where these systems can be used to identify various details and objects in those images. It is a highly challenging process, especially when dealing with blood smear images, which have a very complex cell structure. In order to analyze each object individually and to avoid the cumbersome and error-prone existing manual methods, images can be segmented using appropriate automated segmentation techniques. The proposed work aims at segmenting the nuclei of the White Blood Cells (WBCs) of the peripheral blood smear images, using tissue-like P Systems, which can help to identify various pathological conditions. In the first approach, segmentation is color based. The second approach is intensity based. In the third approach, morphology is used to strengthen the findings from the results.

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