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

Sejung Yang, Byungho Oh, Sungwon Hahm, Kee Yang Chung, Byung Uk Lee

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Background/purpose The development of an automatic diagnostic algorithm using characteristics of dermoscopic findings in acral lentiginous 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 each stripe pair and the histogram of the width ratio in the dermoscopic image is generated. 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.

Original languageEnglish
Pages (from-to)90-96
Number of pages7
JournalBiomedical Signal Processing and Control
Volume32
DOIs
StatePublished - 1 Feb 2017

Keywords

  • Acral lentiginous melanoma
  • Dermoscopic images
  • Image analysis
  • Pattern classification

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