Segmentation of Melanoma Skin Lesions Using Anisotropic Diffusion and Adaptive Thresholding

Segmentation is the first and most important task in the diagnosis of skin cancer using computer-aided systems and due to complex structure of skin lesions, the automated process may lead to a completely different diagnosis. In this paper, a novel segmentation method of skin lesions is proposed which is both effective and simple to implement. Smoothing of skin lesions in original image plays a pivotal role to secure an accurate segmented image. Anisotropic Diffusion Filter (ADF) is used in the initial stage to smooth images with preserved edges. Adaptive thresholding is then applied to segment the skin lesion of the image by binarizing it. The morphological operations are applied for further enhancement and final segmented image is obtained by applying proposed boundary conditions in which objects are selected on basis of distance. The proposed technique is tested on over 300 images and averaged results are compared with existing methods like L-SRM, Otsu-R, Otsu-RGB and TDLS. The proposed method achieved an average accuracy of 96.6%. Visual results for selected images also depicted better performance of proposed method even in the presence of bad illumination and rough skin lesions in the image

Enhanced MR Image Classification Using Hybrid Statistical and Wavelets Features

T Classification of brain tumor is one of the most vital tasks within medical image processing. Classification of images greatly depends on the features extracted from the image, and thus, feature extraction plays a great role in the correct classification of images. In this paper, an enhanced method is presented for glioma MR images classification using hybrid statistical and wavelet features. In the proposed method, 52 features are extracted using the first-order and second-order statistical features (based on the four MRI modalities: Flair, T1, T1c, and T2) in addition to the discrete wavelet transform producing a total of 152 features. The extracted features are applied to the multilayer perceptron (MLP) classifier. The results using the MLP were compared with various known classifiers. The method was tested on the dataset MICCAI BraTS 2015 which is a standard dataset used for research purposes. The proposed hybrid statistical and wavelet features produced 96.72% accuracy for high-grade glioma and 96.04% accuracy for low-grade glioma, which are relatively better compared to the existing studie

Towards Semantic Clustering : Grouping Image Visual Features Through Exploratory Factor Analysis

Current image clustering schemes tend to cluster images based on similarity of low-level image visual features. Our previous work has demonstrated the need for organizing groups of low-level image visual features into composite feature sets that can then be mapped to semantically relevant abstractions. Symbolic terms such as wing ratio and tailed-wings and many more have been obtained from mapping clusters from a single-feature clustering and visual knowledge acquisition. Current focus is the explorations on the extraction and transformation of groupings of low-level image visual features into factor space before mapped to these meaningful terms. Preliminary results from exploratory factor analyses with different settings suggested the solution of forming four groups of features. The selected visual feature groupings have also been shown to correspond to the user-relevant symbolic terms. We hope to highlight these mapped relationships at the conference