Creating and sharing knowledge for telecommunications

Dermoscopic skin lesion image segmentation based on Local Binary Pattern Clustering: Comparative study

Pereira, Pedro M. M. ; Pinto, R. ; Paiva, R. ; Assunção, P.A. ; Távora, L. ; Thomaz, L. A. ; Faria, S.M.M.

Biomedical Signal Processing and Control Vol. 59, Nº 101924, pp. 101924 - 101924, May, 2020.

ISSN (print): 1746-8094
ISSN (online):

Journal Impact Factor: (in )

Digital Object Identifier: 10.1016/j.bspc.2020.101924

Abstract
Accurate skin lesion segmentation is important for identification and classification through computational methods. However, when performed by dermatologists, the results of clinical segmentation are affected by a certain margin of inaccuracy (which exists since dermatologist do not delineate lesions for segmentation but for extraction) and also significant inter- and intra-individual variability, such segmentation is not sufficiently accurate for segmentation studies. This work addresses these limitations to enable detailed analysis of lesions’ geometry along with extraction of non-linear characteristics of region-of-interest border lines. A comprehensive review of 39 segmentation methods is carried out and a contribution to improve dermoscopic image segmentation is presented to determine the regions-of-interest of skin lesions, through accurate border lines with fine geometric details. This approach resorts to Local Binary Patterns and k-means clustering for precise identification of lesions boundaries, particularly the melanocytic. A comparative evaluation study is carried out using three different datasets and reviewed algorithms are grouped according to their approach. Results show that algorithms from the same group tend to perform similarly. Nevertheless, their performance does not depend uniquely on the algorithm itself but also on the underlying dataset characteristics. Throughout several evaluations, the proposed Local Binary Patterns method presents, consistently, better average performance than the current state-of-the-art techniques across the three different datasets without the need of training or supervised learning steps. Overall, apart from presenting a new segmentation method capable of outperforming the current state-of-the-art, this paper provides insightful information about the behaviour and performance of different image segmentation algorithms.