Abstract
Identification of skin disease has become a challenging task with the origination of various skin diseases. The architecture used in this work is content-based image retrieval to facilitate medical diagnosis. In this work, analysis of feature vectors based on fusion of colour and texture and shape features are carried out. The feature vector data are fed into an optimization framework. The results proved that using precision recall curve the shape feature vectors and Mahalanobis distance measure have high contribution to computer-aided diagnosis of skin lesions. Experiments on a set of 2420 images yielded a specificity of 97.04% and a sensitivity of 80.81%. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other works. The work is tested with 40 skin diseases.
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Notes on contributors
Ganasigamony W. Jiji
Ganasigamony W. Jiji is a professor of computer science and engineering at Dr. Sivanthi Aditanar College of Engineering, Tiruchendur. She has published more than 72 scientific research papers. She is a recipient of 10 national and two state awards. Her long-term research focuses on Computer-Aided Detection (CAD) and Measurement (CAM) of lesions in medical images. CAD research aims at discovering the fundamental perception processes of human vision in the image-based diagnosis of lesions, and developing mathematical/computational models that describes them. Her area of interests is computer-aided detection and diagnosis of abnormality using medical images and medical image analysis suchasimage enhancement, segmentation, feature extraction, object detection and pattern recognition.
Peter Raj J.D. Savariraj
Peter Savariraj J.D. Raj is a research fellow working for the project funded by DRDO, New Delhi, under the supervision of Professor Dr. G.W. Jiji. He received Master of Computer Application (MCA) degree from Anna University, Chennai, in 2011. He is doing Ph.D. degree under the supervision of his professor in Bharathiar University, Coimbatore. His research interest includes medical image processing and data mining.