Article
المجلة العلمية :
Journal of Information Technology Research (JITR)
ISSN : 1938-7857
الناشر :
معلومات
الفترة : January 2017
المجلد : 10 العدد : 1
الصفحات : 85-108
التفاصيل
Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis
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.
Texte intégral
ACM :
K. Belattar, S. Mostefai and A. Draa. 2017. Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis. Journal of Information Technology Research (JITR), 10, 1 (January 2017), IGI Global, 85-108. DOI: https://doi.org/10.4018/JITR.2017010106.
APA :
Belattar, K., Mostefai, S. & Draa, A. (2017, January). Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis. Journal of Information Technology Research (JITR), 10(1), IGI Global, 85-108. DOI: https://doi.org/10.4018/JITR.2017010106
IEEE :
K. Belattar, S. Mostefai and A. Draa, "Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis". Journal of Information Technology Research (JITR), vol. 10, no. 1, IGI Global, pp. 85-108, January, 2017. DOI: https://doi.org/10.4018/JITR.2017010106.
BibTeX :
@article{misc-lab-200,
author = {Belattar, Khadidja and Mostefai, Sihem and Draa, Amer},
title = {Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis},
journal = {Journal of Information Technology Research (JITR)},
volume = {10},
number = {1},
issn = {1938-7857},
pages = {85--108},
publisher = {IGI Global},
year = {2017},
month = {January},
doi = {10.4018/JITR.2017010106},
url = {https://www.igi-global.com/article/intelligent-content-based-dermoscopic-image-retrieval-with-relevance-feedback-for-computer-aided-melanoma-diagnosis/176375}
}
RIS :
TI  - Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis
AU - K. Belattar
AU - S. Mostefai
AU - A. Draa
PY - 2017
SN - 1938-7857
JO - Journal of Information Technology Research (JITR)
VL - 10
IS - 1
SP - 85
EP - 108
PB - IGI Global
AB - 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.
DO - 10.4018/JITR.2017010106
UR - https://www.igi-global.com/article/intelligent-content-based-dermoscopic-image-retrieval-with-relevance-feedback-for-computer-aided-melanoma-diagnosis/176375
ID - misc-lab-200
ER -