Epidermis segmentation in skin histopathological images based on thickness measurement and k-means algorithm

• Published in: EURASIP journal on image and video processing Vol. 2015; no. 1; pp. 1 - 14
• Main Authors: Xu, Hongming, Mandal, Mrinal
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 23.06.2015; Springer Nature B.V
• Subjects: Algorithms; Biometrics; Cancer; Engineering; Epidermis; Image processing; Image Processing and Computer Vision; Masks; Pattern Recognition; Segmentation; Segments; Signal,Image and Speech Processing; Histopathological image analysis; Epidermis thickness; Global threshold; Epidermis segmentation
• ISSN: 1687-5281; 1687-5176; 1687-5281
• DOI: 10.1186/s13640-015-0076-3

Automatic segmentation of the epidermis area in skin histopathological images is an essential step for computer-aided diagnosis of various skin cancers. This paper presents a robust technique for epidermis segmentation in the whole slide skin histopathological images. The proposed technique first performs a coarse epidermis segmentation using global thresholding and shape analysis. The epidermis thickness is then measured by a series of line segments perpendicular to the main axis of the initially segmented epidermis mask. If the segmented epidermis mask has a thickness greater than a predefined threshold, the segmentation is assumed to be inaccurate. A second pass of fine segmentation using k-means algorithm is then carried out over these coarsely segmented result to enhance the performance. Experimental results on 64 different skin histopathological images show that the proposed technique provides a superior performance compared to the existing techniques.