Comparative Analysis of Automatic Skin Lesion Segmentation with Two Different Implementations

TL;DR

This paper compares two automatic skin lesion segmentation methods, watershed and mean-shift, demonstrating that watershed generally performs better on challenging images, with promising results for skin cancer diagnosis.

Contribution

The paper presents a comparative analysis of watershed and mean-shift segmentation approaches, including pre-processing pipelines and evaluation on a large dataset.

Findings

Watershed approach achieved higher average Jaccard Index (89.16%)

Mean-shift approach achieved an average Jaccard Index of 76.94%

Pre-processing steps improved segmentation performance

Abstract

Lesion segmentation from the surrounding skin is the first task for developing automatic Computer-Aided Diagnosis of skin cancer. Variant features of lesion like uneven distribution of color, irregular shape, border and texture make this task challenging. The contribution of this paper is to present and compare two different approaches to skin lesion segmentation. The first approach uses watershed, while the second approach uses mean-shift. Pre-processing steps were performed in both approaches for removing hair and dark borders of microscopic images. The Evaluation of the proposed approaches was performed using Jaccard Index (Intersection over Union or IoU). An additional contribution of this paper is to present pipelines for performing pre-processing and segmentation applying existing segmentation and morphological algorithms which led to promising results. On average, the first approach showed better performance than the second one with average Jaccard Index over 200 ISIC-2017 challenge images are 89.16% and 76.94% respectively.