Automated segmentation of the epidermis area in skin whole slide histopathological images. Issue 9 (1st September 2015)

• Record Type: Journal Article
• Authors: Lu, Cheng; Ma, Zhen; Mandal, Mrinal
• Is Part Of: IET image processing. Volume 9:Issue 9(2015)
• Page Start: 735
• Page End: 742
• Publication Date: 2015-09-01
• Subjects: image segmentation -- skin -- medical image processing -- image resolution -- cancer -- image matching -- probability -- biomedical optical imaging; automated segmentation -- skin whole slide histopathological images -- high‐speed whole slide histology digital scanners -- high‐resolution whole slide histology digital scanners -- glass slides -- tissue specimen -- quantitative image analysis -- pathologist -- cancer diagnosis -- computer‐aided diagnosis system -- computer‐aided epidermis segmentation technique -- whole slide skin histopathological image -- shape analysis -- template matching method -- adaptive template intensity value -- probability density function -- response value image; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2014.0192
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : With the development of high‐speed, high‐resolution whole slide histology digital scanners, glass slides of tissue specimen can now be digitised at high magnification to create the whole slide image. Quantitative image analysis tools are then desirable to help the pathologist for their routine examination. Epidermis area is a very important observation area for the cancer diagnosis. Therefore, in order to build up a computer‐aided diagnosis system, segmentation of the epidermis area is often the very first and crucial step. An improved computer‐aided epidermis segmentation technique for the whole slide skin histopathological image is proposed in this study. The proposed technique first obtains an initial segmentation result with the help of global thresholding and shape analysis. A template matching method, with adaptive template intensity value, is then applied. Finally, a threshold is calculated based on the probability density function of the response value image. Experimental results show that the proposed technique overcomes the limitation of the existing technique and provides superior performance, with sensitivity of 95.68%, specificity of 99.41% and precision of 93.13%. The performance of the proposed technique is satisfactory for future clinical use.