Machine Learning
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Design-based texture feature fusion using Gabor filters and co-occurrence probabilities
IEEE Transactions on Image Processing
Generalizing Common Tasks in Automated Skin Lesion Diagnosis
IEEE Transactions on Information Technology in Biomedicine
Automatic Skin Lesion Segmentation via Iterative Stochastic Region Merging
IEEE Transactions on Information Technology in Biomedicine
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Automatic skin lesion detection is a key step in computer-aided diagnosis (CAD) of Skin cancers, since the accuracy of the subsequent steps in CAD crucially depends on it. In this paper, a novel method of automatic skin lesion segmentation based on texture analysis and supervised learning is proposed. It firstly involve the clustering of training image into homogeneous regions using Mean-shift; then fusion texture feature are extracted from each clustered region based on Gabor and GLCM feature; next, the classifier model is generated through supervised learning base on LIBSVM; finally, lesion regions of the unseen image are automatically predicted out by produced classifier. Comprehensive experiments have been performed on a dataset of 125 dermoscopy images. The proposed method is compared with three state-of-the-art methods and results demonstrate that the presented method achieves both robust and accurate lesion segmentation in dermoscopy images.