Automated Segmentation of the Melanocytes in Skin Histopathological Images

Cheng Lu, Muhammad Mahmood, Naresh Jha, Mrinal Mandal
2013 IEEE journal of biomedical and health informatics  
In the diagnosis of skin melanoma by analyzing histopathological images, the detection of the melanocytes in the epidermis area is an important step. However, the detection of melanocytes in the epidermis area is difficult because other keratinocytes that are very similar to the melanocytes are also present. This paper proposes a novel computer-aided technique for segmentation of the melanocytes in the skin histopathological images. In order to reduce the local intensity variant, a mean-shift
more » ... ant, a mean-shift algorithm is applied for the initial segmentation of the image. A local region recursive segmentation algorithm is then proposed to filter out the candidate nuclei regions based on the domain prior knowledge. To distinguish the melanocytes from other keratinocytes in the epidermis area, a novel descriptor, named local double ellipse descriptor (LDED), is proposed to measure the local features of the candidate regions. The LDED uses two parameters: region ellipticity and local pattern characteristics to distinguish the melanocytes from the candidate nuclei regions. Experimental results on 28 different histopathological images of skin tissue with different zooming factors show that the proposed technique provides a superior performance. Index Terms-Histopathological image analysis, image segmentation, local descriptor, object detection, pattern recognition.
doi:10.1109/titb.2012.2199595 pmid:22614727 fatcat:b3sgfrqinrgw3jd7w7i24nuimy