A fast BEMD algorithm based on multi-scale extrema

Abstract

Abstract-Severe melanoma is potentially life-threatening. A novel methodology for automatic feature extraction from histopathological images and subsequent classification is presented. The proposed automated system uses a number of features extracted from images of skin lesions through image processing techniques which consisted of a spatially winner and adaptive median filter then applied Gabor filter bank to improve diagnostic accuracy. Histogram equalization to enhance the contrast of the images prior to segmentation is used. Then, a wavelet approach is used to extract the features; more specifically Wavelet Packet Transform (WPT).This article introduces a novel melanoma detection strategy using a hybrid particle swarm -based support vector machine (SVM-WLG -PSO) technique. The extracted features are reduced by using a particle swarm optimization (PSO), this was used to optimize the SVM parameters as a feature selection and finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier to diagnose skin biopsies from patients as either malignant melanoma or benign nevi. The obtained classification accuracies show better performance in comparison to similar approaches for feature extraction. The proposed system is able to achieve one of the best results with classification accuracy of 87.13%, sensitivity of 94.1% and specificity of 80.22%.Keywords-Lesion, histo-pathological images, winner, adaptive median filter (AMF), Gabor filter bank, Histogram Equalization, particle swarm optimization (PSO), support vector machine (SVM).

PSO-SVM hybrid system for melanoma detection from histo-pathological images

“…In this study, we extend our work in [17] and provide a full explanation of the PSO-SVM novel melanoma detection strategy introduced there. It is based on a hybrid Particle Swarm Optimization-Support Vector Machine (PSO-SVM) framework that aims to enable improving image features selection and SVM parameters optimization simultaneously.…”

“…In their efforts to address the same problem, the authors in [17] used additional features extracted from the Wavelet Packet Transformation (WPT) of the pre-processed Histo-pathological image along with the features obtained from the histogram and the co-occurrence matrix used in [16]. The paper introduced a PSO-SVM framework that enabled simultaneous feature selection and SVM parameters optimization.…”