Automated analysis and classification of melanocytic tumor on skin whole slide images. (June 2018)

• Record Type: Journal Article
• Authors: Xu, Hongming; Lu, Cheng; Berendt, Richard; Jha, Naresh; Mandal, Mrinal
• Is Part Of: Computerized medical imaging and graphics. Volume 66(2018)
• Page Start: 124
• Page End: 134
• Publication Date: 2018-06
• Subjects: Melanoma -- Nuclei segmentation -- Biopsy classification -- Epidermis and dermis; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2018.01.008
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: A framework for whole slide skin image analysis. A multiresolution framework to generate skin epidermis and dermis image tiles. Both skin epidermis and dermis analyses are integrated for skin melanoma diagnosis. Both cytological and textural features are used for skin image classification. More than 95% classification accuracy is achieved. Abstract: This paper presents a computer-aided technique for automated analysis and classification of melanocytic tumor on skin whole slide biopsy images. The proposed technique consists of four main modules. First, skin epidermis and dermis regions are segmented by a multi-resolution framework. Next, epidermis analysis is performed, where a set of epidermis features reflecting nuclear morphologies and spatial distributions is computed. In parallel with epidermis analysis, dermis analysis is also performed, where dermal cell nuclei are segmented and a set of textural and cytological features are computed. Finally, the skin melanocytic image is classified into different categories such as melanoma, nevus or normal tissue by using a multi-class support vector machine (mSVM) with extracted epidermis and dermis features. Experimental results on 66 skin whole slide images indicate that the proposed technique achieves more than 95% classification accuracy, which suggests that the technique has the potential to be used for assisting pathologists on skin biopsy image analysis and classification.