Computer-aided diagnosis of pigmented skin dermoscopic images

Asad Safi, Maximilian Baust, Olivier Pauly, Victor Castaneda, Tobias Lasser, Diana Mateus, Nassir Navab, Rüdliger Hein, Mahzad Ziai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations


Diagnosis of benign and malign skin lesions is currently mostly relying on visual assessment and frequent biopsies performed by dermatologists. As the timely and correct diagnosis of these skin lesions is one of the most important factors in the therapeutic outcome, leveraging new technologies to assist the dermatologist seems natural. In this paper we propose a machine learning approach to classify melanocytic lesions into malignant and benign from dermoscopic images. The dermoscopic image database is composed of 4240 benign lesions and 232 malignant melanoma. For segmentation we are using multiphase soft segmentation with total variation and H 1 regularization. Then, each lesion is characterized by a feature vector that contains shape, color and texture information, as well as local and global parameters that try to reflect structures used in medical diagnosis. The learning and classification stage is performed using SVM with polynomial kernels. The classification delivered accuracy of 98.57% with a true positive rate of 0.991% and a false positive rate of 0.019%.

Original languageEnglish
Title of host publicationMedical Content-Based Retrieval for Clinical Decision Support - Second MICCAI International Workshop, MCBR-CDS 2011, Revised Selected Papers
Number of pages11
StatePublished - 2012
Event2nd MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CDS 2011 - Toronto, ON, Canada
Duration: 22 Sep 201122 Sep 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7075 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CDS 2011
CityToronto, ON


  • Classification
  • Computer-Aided Diagnosis
  • Machine Learning
  • Melanoma
  • Supervised Learning


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