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Characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic) that analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a...
In this work, we present a Computer Aided Diagnostic (CAD) technique (a class of Atheromatic systems) that classifies the automatically segmented carotid far wall Intima-Media Thickness (IMT) regions along the common carotid artery into symptomatic and asymptomatic classes. We extracted texture features based on Local Binary Patterns (LBP) and Law's Texture Energy (LTE) and used the significant features...
In this work, we have developed an adjunct Computer Aided Diagnostic (CAD) technique that uses 3D acquired ultrasound images of the ovary and data mining algorithms to accurately characterize and classify benign and malignant ovarian tumors. In this technique, we extracted image-texture based and Higher Order Spectra (HOS) based features from the images. The significant features were then selected...
In this paper, we present a Computer Aided Diagnosis (CAD) based technique (Atheromatic system) for classification of carotid plaques in B-mode ultrasound images into symptomatic or asymptomatic classes. This system, called Atheromatic, has two steps: (i) feature extraction using a combination of Discrete Wavelet Transform (DWT) and averaging algorithms and (ii) classification using Support Vector...
In this work, we present a Computer Aided Diagnosis (CAD) based technique for automatic classification of benign and malignant thyroid lesions in 3D contrast-enhanced ultrasound images. The images were obtained from 20 patients. Fine needle aspiration biopsy and histology confirmed malignancy. Discrete Wavelet Transform (DWT) and texture based features were extracted from the thyroid images. The resulting...
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