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Ridge and furrow pattern classification for acral lentiginous melanoma using dermoscopic images

Title
Ridge and furrow pattern classification for acral lentiginous melanoma using dermoscopic images
Authors
Yang, SejungOh, ByunghoHahm, SungwonChung, Kee-YangLee, Byung-Uk
Ewha Authors
이병욱양세정
SCOPUS Author ID
이병욱scopus; 양세정scopus
Issue Date
2017
Journal Title
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
ISSN
1746-8094JCR Link1746-8108JCR Link
Citation
vol. 32, pp. 90 - 96
Keywords
Acral lentiginous melanomaImage analysisPattern classificationDermoscopic images
Publisher
ELSEVIER SCI LTD
Indexed
SCIE; SCOPUS WOS scopus
Abstract
Background/purpose: The development of an automatic diagnostic algorithm using characteristics of dermoscopic findings in acral lentiginou's melanoma (ALM) has been slow due to the rarity of melanoma in non-Caucasian populations. In this study, we present an automatic algorithm that can distinguish the "furrow" and "ridge" patterns of pigmentation on the palm and foot, and report its usefulness for the detection of ALM. Methods: To distinguish between ALM and nevus, the proposed image analysis is applied. From a dermoscopic image, edges having the steepest ascent or descent are detected through Gaussian derivative filtering. The widths between edges are then measured and the brightness of each stripe is tagged. The dark area is tagged as black and the bright area is tagged as white. The ratio of widths of dark to bright is calculated at eachstripe pair and the histogram of the width ratio in the dermoscopic image iszenerated. Results: A total of 297 dermoscopic images confirmed by histopathologic diagnoses are classified. All of the melanoma dermoscopic images were classified correctly using the proposed algorithm, while only one nevus image was misclassified. The proposed method achieved a sensitivity of 100%, a specificity of 99.1%, an accuracy of 99.7%, and a similarity of 99.7%. Conclusion: In this study, we propose a novel automatic algorithm that can precisely distinguish the "furrow" and "ridge" patterns of pigmentation on dermoscopic images using the width ratio of dark and bright patterns. It is expected that the proposed algorithm will contribute to the early diagnosis of ALM. (C) 2016 Elsevier Ltd. All rights reserved.
DOI
10.1016/j.bspc.2016.09.019
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엘텍공과대학 > 전자공학과 > Journal papers
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