Nooshin Moradi1, Nezam Mahdavi-Amiri2. 1. Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran. Electronic address: nooshin.moradi@student.sharif.edu. 2. Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran. Electronic address: nezamm@sharif.edu.
Abstract
BACKGROUND AND OBJECTIVES: Melanoma is a dangerous kind of skin disease with a high death rate, and its prevalence has increased rapidly in recent years. Diagnosis of melanoma in a primary phase can be helpful for its cure. Due to costs for dermatology, we need an automatic system to diagnose melanoma through lesion images. METHODS: Here, we propose a sparse representation based method for segmentation and classification of lesion images. The main idea of our framework is based on a kernel sparse representation, which produces discriminative sparse codes to represent features in a high-dimensional feature space. Our novel formulation for discriminative kernel sparse coding jointly learns a kernel-based dictionary and a linear classifier. We also present an adaptive K-SVD algorithm for kernel dictionary and classifier learning. RESULTS: We test our approach for both segmentation and classification tasks. The evaluation results on both dermoscopic and digital datasets demonstrate our approach to be competitive as compared to the available state-of-the-art methods, with the advantage of not needing any pre-processing. CONCLUSIONS: Our method is insensitive to noise and image conditions and can be used effectively for challenging skin lesions. Our approach is so extensive to be adapted to various medical image segmentations.
BACKGROUND AND OBJECTIVES:Melanoma is a dangerous kind of skin disease with a high death rate, and its prevalence has increased rapidly in recent years. Diagnosis of melanoma in a primary phase can be helpful for its cure. Due to costs for dermatology, we need an automatic system to diagnose melanoma through lesion images. METHODS: Here, we propose a sparse representation based method for segmentation and classification of lesion images. The main idea of our framework is based on a kernel sparse representation, which produces discriminative sparse codes to represent features in a high-dimensional feature space. Our novel formulation for discriminative kernel sparse coding jointly learns a kernel-based dictionary and a linear classifier. We also present an adaptive K-SVD algorithm for kernel dictionary and classifier learning. RESULTS: We test our approach for both segmentation and classification tasks. The evaluation results on both dermoscopic and digital datasets demonstrate our approach to be competitive as compared to the available state-of-the-art methods, with the advantage of not needing any pre-processing. CONCLUSIONS: Our method is insensitive to noise and image conditions and can be used effectively for challenging skin lesions. Our approach is so extensive to be adapted to various medical image segmentations.