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Correct mass diagnosis in mammogram can reduce the unnecessary biopsy without increasing false negatives. In this paper, we investigated the usage of random forest classifier for the classification of masses with geometry and texture features. Before extracting features, the mass regions need to be extracted. Based on the initial contour guided by radiologist, level set segmentation is used to deform...
In this paper, an automatically method for mass detection was introduced, which combines multiple layers concentric (MLC) and narrow band region-based active contour (NBAC) technique. We used an improved level set method to segment the mass for contour refinement, after the boundary of a mass is found, texture features from Gray Level Cooccurrence Matrix (GLCM) are extracted from the surrounding area...
In this paper, a new method for the estimation of 3D human body poses from monocular images is proposed. Histograms of oriented gradients are used as the features for modeling human body poses. Human body poses are represented as 3D limb angles, which can remove the structure information from pose vector. Relevance Vector Machine is used to infer the mapping from image features to body poses. Experiments...
In this paper, we investigate mass classification using an improved local binary pattern operator. In the proposed classification algorithm, the improved local binary pattern operator is used to extract the features of masses and is used to determine whether the mass is benign or malignant. For classifier, support vector machine is adopted. 309 images from the DDSM database were used and the experimental...
In this paper, we investigate the classification of masses with texture features. We propose an improved level set method to find the boundary of a mass, based on the initial contour provided by radiologists. After the boundary of a mass is found, texture features from Gray Level Co-occurrence Matrix (GLCM) are extracted from the surrounding area of the boundary of the mass. The extracted texture...
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