Unsupervised Segmentation and Categorization of Skin Lesions Using Adaptative Thresholds and Stochastic Features

  • Eliezer Emanuel Bernart
  • Maciel Zortea
  • Jacob Scharcanski
  • Sergio Bampi

Abstract

This work presents a novel unsupervised method to segment skin
lesions in macroscopic images, grouping the pixels into three disjoint
categories, namely ’skin lesion’, ’suspicious region’ and ’healthy
skin’. These skin region categories are obtained by analyzing the
agreement of adaptative thresholds applied to the different skin image
color channels. In the sequence we use stochastic texture features
to refine the suspicious regions. Our preliminary results are
promising, and suggest that skin lesions can be segmented successfully
with the proposed approach. Also, ’suspicious regions’
are identified correctly, where it is uncertain if they belong to skin
lesions or to the surrounding healthy skin.

Published
2015-10-31
How to Cite
Bernart, E., Zortea, M., Scharcanski, J., & Bampi, S. (2015). Unsupervised Segmentation and Categorization of Skin Lesions Using Adaptative Thresholds and Stochastic Features. Journal of Computational Vision and Imaging Systems, 1(1). https://doi.org/10.15353/vsnl.v1i1.62
Section
Articles