Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind

Vimal K Shrivastava; Narendra D Londhe; Rajendra S Sonawane; Jasjit S Suri

doi:10.1016/j.cmpb.2015.11.013

Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind

Comput Methods Programs Biomed. 2016 Apr:126:98-109. doi: 10.1016/j.cmpb.2015.11.013. Epub 2016 Jan 20.

Authors

Vimal K Shrivastava¹, Narendra D Londhe², Rajendra S Sonawane³, Jasjit S Suri⁴

Affiliations

¹ Electrical Engineering Department, National Institute of Technology, Raipur, India. Electronic address: lky.vml@gmail.com.
² 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.
³ Psoriasis Clinic and Research Centre, Psoriatreat, Pune, Maharashtra, India. Electronic address: drrajss@gmail.com.
⁴ 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.

PMID: 26830378
DOI: 10.1016/j.cmpb.2015.11.013

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.

Keywords: Classification; Color features; Higher order spectra features; Psoriasis skin disease; Reliability; Texture features.

MeSH terms

Algorithms
Case-Control Studies
Color
Dermatology / methods
Diagnosis, Computer-Assisted / methods*
Fourier Analysis
Humans
Models, Statistical
Models, Theoretical
Pattern Recognition, Automated
Principal Component Analysis
Psoriasis / diagnostic imaging*
ROC Curve
Radiographic Image Interpretation, Computer-Assisted / methods
Reproducibility of Results
Risk
Sensitivity and Specificity
Skin / pathology
Support Vector Machine