NDLI: Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns

Content Provider	IEEE Xplore Digital Library
Author	Gonzalez-Castro, V. Debayle, J. Wazaefi, Y. Rahim, M. Gaudy, C. Grob, J.-J. Fertil, B.
Copyright Year	2015
Description	Author affiliation: LGF, Ecole Nat. Sup. des Mines de St.-Etienne, St. Étienne, France (Gonzalez-Castro, V.; Debayle, J.) \|\| Service de Dermatologie, Hopital de la Timone, Marseille, France (Grob, J.-J.; Fertil, B.) \|\| Lab. des Sci. de l'Inf. et des Syst., Marseille, France (Wazaefi, Y.; Rahim, M.; Gaudy, C.)
Abstract	This paper introduces a method for characterizing and classifying skin lesions in dermoscopic color images with the goal of detecting which ones are melanoma (cancerous lesions). The images are described by means of the Local Binary Patterns (LBPs) computed on geometrical feature maps of each color component of the image. These maps are extracted from geometrical measurements of the General Adaptive Neighborhoods (GAN) of the pixels. The GAN of a pixel is a region surrounding it and fitting its local image spatial structure. The performance of the proposed texture descriptor has been evaluated by means of an Artificial Neural Network, and it has been compared with the classical LBPs. Experimental results using ROC curves show that the GAN-based method outperforms the classical one and the dermatologists' predictions.
Starting Page	1722
Ending Page	1726
File Size	3644118
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351095
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gallium nitride Lesions Image color analysis Malignant tumors Feature extraction Skin Color Local binary patterns Melanoma Texture description General adaptive neighborhoods
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns

Melanoma Classification Using Dermoscopy Imaging and Ensemble Learning

Quantitative features for computer-aided melanoma classification using spatial heterogeneity of eumelanin and pheomelanin concentrations

Automatic classification of skin lesions using color mathematical morphology-based texture descriptors

Classification of melanoma and Clark nevus skin lesions based on medical image processing techniques

A Mobile Automated Skin Lesion Classification System

Semi-automated Diagnosis of Melanoma through the Analysis of Dermatological Images

Detecting melanoma in dermoscopy images using scale adaptive local binary patterns

New Accurate Automated Melanoma Diagnosing Systems

Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns

Similar Documents

Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns

Melanoma Classification Using Dermoscopy Imaging and Ensemble Learning

Quantitative features for computer-aided melanoma classification using spatial heterogeneity of eumelanin and pheomelanin concentrations

Automatic classification of skin lesions using color mathematical morphology-based texture descriptors

Classification of melanoma and Clark nevus skin lesions based on medical image processing techniques

A Mobile Automated Skin Lesion Classification System

Semi-automated Diagnosis of Melanoma through the Analysis of Dermatological Images

Detecting melanoma in dermoscopy images using scale adaptive local binary patterns

New Accurate Automated Melanoma Diagnosing Systems

Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns