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This paper presents a wavelet-based texture analysis method for classification of melanoma. The method applies tree-structured wavelet transform on different color channels of red, green, blue and luminance of dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. Feature extraction and a two-stage feature selection method, based on entropy and correlation,...
After analyzing the current wavelet threshold de-noising methods and independent component analysis (ICA) methods in EEG, this paper proposed a novel method for EEG de-noising which combines the new threshold de-noising method with ICA method and implicates it to deal with mental EEG of imaging left-right hands movement, and then classifies the signal by support vector machine (SVM). The correct classification...
This paper introduces an automatic liver parenchyma segmentation algorithm that can delineate liver in abdominal CT images. The proposed approach consists of three main steps. Firstly, a texture analysis is applied onto input abdominal CT images to extract pixel level features. Here, two main categories of features, namely wavelet coefficients and Haralick texture descriptors are investigated. Secondly,...
In this paper, we propose a robust method for the suppression of noise in medical ultrasound image by fusing the wavelet denoising technique with support vector regression (SVR). Based on the least squares support vector regression (LS- SVR), a new denoising operator and a new manipulation algorithm of wavelet coefficients are presented by incorporating neighboring coefficients. The proposed method...
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