Intelligent content-based dermoscopic image retrieval with relevance feedback for computer-aided melanoma diagnosis

被引：6

作者：

Belattar K. ^{[1
]}

Mostefai S. ^{[1
]}

Draa A. ^{[1
]}

机构：

[1] Constantine 2 University, Constantine

来源：

Journal of Information Technology Research | 2017年 / 10卷 / 01期

关键词：

Content-based dermoscopic image retrieval; Histogram intersection intelligent; Melanoma diagnosis; Relevance feedback; SVM;

D O I：

10.4018/JITR.2017010106

中图分类号：

学科分类号：

摘要：

The use of Computer-Aided Diagnosis in dermatology raises the necessity of integrating Content-Based Image Retrieval (CBIR) technologies. The latter could be helpful to untrained users as a decision support system for skin lesion diagnosis. However, classical CBIR systems perform poorly due to semantic gap. To alleviate this problem, we propose in this paper an intelligent Content-Based Dermoscopic Image Retrieval (CBDIR) system with Relevance Feedback (RF) for melanoma diagnosis that exhibits: efficient and accurate image retrieval as well as visual features extraction that is independent of any specific diagnostic method. After submitting a query image, the proposed system uses linear kernel-based active SVM, combined with histogram intersection-based similarity measure to retrieve the K most similar skin lesion images. The dominant (melanoma, benign) class in this set will be identified as the image query diagnosis. Extensive experiments conducted on our system using a 1097 image database show that the proposed scheme is more effective than CBDIR without the assistance of RF. Copyright © 2017, IGI Global.

引用

页码：85 / 108

页数：23

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