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Clustered micro calcifications (MCs) are one of the early signs of breast cancer. In this paper, we propose a new computer aided diagnosis (CAD) system for automatic detection of MCs in two steps. First, pixels corresponding to potential micro calcifications are found using a multilayer feed-forward neural network. The input of this network consists of 4 wavelet and 2 gray-level features. The output...
Identifying relevant, representative and more important, discriminant image features for analysis and proper image classification purpose is one of the main tasks in image processing and pattern recognition field. In this paper, Gabor wavelets based features are extracted from medical mammogram images representing normal tissues, or benign and malign tumors. Once features are detected, Principal Component...
This paper presents an automated system for grading pathological images of prostatic carcinoma based on a set of texture features extracted by multi-categories of methods including multi-wavelets, Gabor-filters, GLCM, and fractal dimensions. We apply 5-fold cross-validation procedure to a set of 205 pathological prostate images for training and testing. Experimental results show that the fractal dimension...
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,...
This paper presents an automatic liver parenchyma segmentation algorithm that can segment liver in abdominal CT images. There are three major steps in the proposed approach. Firstly, a texture analysis is applied to input abdominal CT images to extract pixel level features. In this step, wavelet coefficients are used as texture descriptors. Secondly, support vector machines (SVMs) are implemented...
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