Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind.
Identifieur interne : 000260 ( Main/Merge ); précédent : 000259; suivant : 000261Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind.
Auteurs : Vimal K. Shrivastava [Inde] ; Narendra D. Londhe [États-Unis] ; Rajendra S. Sonawane [Inde] ; Jasjit S. Suri [États-Unis]Source :
- Computer methods and programs in biomedicine [ 1872-7565 ] ; 2016.
Abstract
Psoriasis is an autoimmune skin disease with red and scaly plaques on skin and affecting about 125 million people worldwide. Currently, dermatologist use visual and haptic methods for diagnosis the disease severity. This does not help them in stratification and risk assessment of the lesion stage and grade. Further, current methods add complexity during monitoring and follow-up phase. The current diagnostic tools lead to subjectivity in decision making and are unreliable and laborious. This paper presents a first comparative performance study of its kind using principal component analysis (PCA) based CADx system for psoriasis risk stratification and image classification utilizing: (i) 11 higher order spectra (HOS) features, (ii) 60 texture features, and (iii) 86 color feature sets and their seven combinations. Aggregate 540 image samples (270 healthy and 270 diseased) from 30 psoriasis patients of Indian ethnic origin are used in our database. Machine learning using PCA is used for dominant feature selection which is then fed to support vector machine classifier (SVM) to obtain optimized performance. Three different protocols are implemented using three kinds of feature sets. Reliability index of the CADx is computed. Among all feature combinations, the CADx system shows optimal performance of 100% accuracy, 100% sensitivity and specificity, when all three sets of feature are combined. Further, our experimental result with increasing data size shows that all feature combinations yield high reliability index throughout the PCA-cutoffs except color feature set and combination of color and texture feature sets. HOS features are powerful in psoriasis disease classification and stratification. Even though, independently, all three set of features HOS, texture, and color perform competitively, but when combined, the machine learning system performs the best. The system is fully automated, reliable and accurate.
DOI: 10.1016/j.cmpb.2015.11.013
PubMed: 26830378
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Londhe</name><affiliation wicri:level="2"><nlm:affiliation>Electrical Engineering Department, National Institute of Technology, Raipur, India; Skin Point-of-Care Division, Global Biomedical Technologies, Inc., Roseville, CA, USA. Electronic address: nlondhe.ele@nitrr.ac.in.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Electrical Engineering Department, National Institute of Technology, Raipur, India; Skin Point-of-Care Division, Global Biomedical Technologies, Inc., Roseville, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Sonawane, Rajendra S" sort="Sonawane, Rajendra S" uniqKey="Sonawane R" first="Rajendra S" last="Sonawane">Rajendra S. Sonawane</name><affiliation wicri:level="1"><nlm:affiliation>Psoriasis Clinic and Research Centre, Psoriatreat, Pune, Maharashtra, India. Electronic address: drrajss@gmail.com.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Psoriasis Clinic and Research Centre, Psoriatreat, Pune, Maharashtra</wicri:regionArea><wicri:noRegion>Maharashtra</wicri:noRegion></affiliation></author><author><name sortKey="Suri, Jasjit S" sort="Suri, Jasjit S" uniqKey="Suri J" first="Jasjit S" last="Suri">Jasjit S. Suri</name><affiliation wicri:level="2"><nlm:affiliation>Skin Point-of-Care Division, Global Biomedical Technologies, Inc., Roseville, CA, USA; Electrical Engineering Department, Idaho State University (Aff.), ID, USA. 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Shrivastava</name><affiliation wicri:level="1"><nlm:affiliation>Electrical Engineering Department, National Institute of Technology, Raipur, India. Electronic address: lky.vml@gmail.com.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Electrical Engineering Department, National Institute of Technology, Raipur</wicri:regionArea><wicri:noRegion>Raipur</wicri:noRegion></affiliation></author><author><name sortKey="Londhe, Narendra D" sort="Londhe, Narendra D" uniqKey="Londhe N" first="Narendra D" last="Londhe">Narendra D. Londhe</name><affiliation wicri:level="2"><nlm:affiliation>Electrical Engineering Department, National Institute of Technology, Raipur, India; Skin Point-of-Care Division, Global Biomedical Technologies, Inc., Roseville, CA, USA. Electronic address: nlondhe.ele@nitrr.ac.in.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Electrical Engineering Department, National Institute of Technology, Raipur, India; Skin Point-of-Care Division, Global Biomedical Technologies, Inc., Roseville, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Sonawane, Rajendra S" sort="Sonawane, Rajendra S" uniqKey="Sonawane R" first="Rajendra S" last="Sonawane">Rajendra S. Sonawane</name><affiliation wicri:level="1"><nlm:affiliation>Psoriasis Clinic and Research Centre, Psoriatreat, Pune, Maharashtra, India. Electronic address: drrajss@gmail.com.</nlm:affiliation><country xml:lang="fr">Inde</country><wicri:regionArea>Psoriasis Clinic and Research Centre, Psoriatreat, Pune, Maharashtra</wicri:regionArea><wicri:noRegion>Maharashtra</wicri:noRegion></affiliation></author><author><name sortKey="Suri, Jasjit S" sort="Suri, Jasjit S" uniqKey="Suri J" first="Jasjit S" last="Suri">Jasjit S. Suri</name><affiliation wicri:level="2"><nlm:affiliation>Skin Point-of-Care Division, Global Biomedical Technologies, Inc., Roseville, CA, USA; Electrical Engineering Department, Idaho State University (Aff.), ID, USA. Electronic address: jsuri@comcast.net.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Skin Point-of-Care Division, Global Biomedical Technologies, Inc., Roseville, CA, USA; Electrical Engineering Department, Idaho State University (Aff.), ID</wicri:regionArea><placeName><region type="state">Idaho</region></placeName></affiliation></author></analytic><series><title level="j">Computer methods and programs in biomedicine</title><idno type="eISSN">1872-7565</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Psoriasis is an autoimmune skin disease with red and scaly plaques on skin and affecting about 125 million people worldwide. Currently, dermatologist use visual and haptic methods for diagnosis the disease severity. This does not help them in stratification and risk assessment of the lesion stage and grade. Further, current methods add complexity during monitoring and follow-up phase. The current diagnostic tools lead to subjectivity in decision making and are unreliable and laborious. This paper presents a first comparative performance study of its kind using principal component analysis (PCA) based CADx system for psoriasis risk stratification and image classification utilizing: (i) 11 higher order spectra (HOS) features, (ii) 60 texture features, and (iii) 86 color feature sets and their seven combinations. Aggregate 540 image samples (270 healthy and 270 diseased) from 30 psoriasis patients of Indian ethnic origin are used in our database. Machine learning using PCA is used for dominant feature selection which is then fed to support vector machine classifier (SVM) to obtain optimized performance. Three different protocols are implemented using three kinds of feature sets. Reliability index of the CADx is computed. Among all feature combinations, the CADx system shows optimal performance of 100% accuracy, 100% sensitivity and specificity, when all three sets of feature are combined. Further, our experimental result with increasing data size shows that all feature combinations yield high reliability index throughout the PCA-cutoffs except color feature set and combination of color and texture feature sets. HOS features are powerful in psoriasis disease classification and stratification. Even though, independently, all three set of features HOS, texture, and color perform competitively, but when combined, the machine learning system performs the best. The system is fully automated, reliable and accurate.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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