TY - JOUR
T1 - Ridge and furrow pattern classification for acral lentiginous melanoma using dermoscopic images
AU - Yang, Sejung
AU - Oh, Byungho
AU - Hahm, Sungwon
AU - Chung, Kee Yang
AU - Lee, Byung Uk
N1 - Funding Information:
This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2013R1A2A2A04015894 ) and by the Bio & Medical Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2012M3A9B6055379 ) and by Disaster-Safety Platform Technology Development Program of the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning ( No. 2015M3C8A7074199 ).
Publisher Copyright:
© 2016 Elsevier Ltd
PY - 2017/2/1
Y1 - 2017/2/1
N2 - 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.
AB - 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.
KW - Acral lentiginous melanoma
KW - Dermoscopic images
KW - Image analysis
KW - Pattern classification
UR - http://www.scopus.com/inward/record.url?scp=85006091108&partnerID=8YFLogxK
U2 - 10.1016/j.bspc.2016.09.019
DO - 10.1016/j.bspc.2016.09.019
M3 - Article
AN - SCOPUS:85006091108
SN - 1746-8094
VL - 32
SP - 90
EP - 96
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
ER -