Novel lymph node segmentation and proliferation index measurement for skin melanoma biopsy images. (April 2019)

• Record Type: Journal Article
• Authors: Alheejawi, Salah; Xu, Hongming; Berendt, Richard; Jha, Naresh; Mandal, Mrinal
• Is Part Of: Computerized medical imaging and graphics. Volume 73(2019)
• Page Start: 19
• Page End: 29
• Publication Date: 2019-04
• Subjects: Histopathological image analysis -- Lymph node segmentation -- Proliferation index measurement; 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.2019.01.006
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: Fully automated technique for lymph node segmentation & PI Calculation. Technique is robust to several stains: H&E, MART-1, S-100, KI-67. More than 90% segmentation accuracy is achieved. Automatically calculated PI values close match with Ground truth. An important step for automated melanoma grading. Abstract: The lymphatic system is the immune system of the human body, and includes networks of vessels spread over the body, lymph nodes, and lymph fluid. The lymph nodes are considered as purification units that collect the lymph fluid from the lymph vessels. Since the lymph nodes collect the cancer cells that escape from a malignant tumor and try to spread to the rest of the body, the lymph node analysis is important for staging many types skin and breast cancers. In this paper, we propose a Computer Aided Diagnosis (CAD) method that segments the lymph nodes and melanoma regions in a biopsy image and measure the proliferation index. The proposed method contains two stages. First, an automated technique is used to segment the lymph nodes in a biopsy image based on histogram and high frequency features. In the second stage, the proliferation index for the melanoma regions is calculated by comparing the number of active and passive nuclei. Experimental results on 76 different lymph node images show that the proposed segmentation technique can robustly segment the lymph nodes with more than 90% accuracy. The proposed proliferation index calculation has low complexity and has an average error rate of less than 1.5%.