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Computer-aided diagnosis (CAD) technology can improve the detection of abnormal. Such as calcifications, masses, and architectural distortion. Among the three abnormals, architectural distortion is the most difficult one to detect for both radiologists and CAD systems. In this article, we use automatic architectural distortion detection method to locate initial suspicious areas. Then, combine the...
A new nonparallel twin support vector machine is presented in this article which combine l2,1 -norm minimizing on both loss function and regularization which called (TWSVM_21). Our TWSVM_21 is formulated aiming to separate class more efficiently. Different from other nonparallel classifier, such as the presented TBSVM, the classifier we proposed in this paper joint l2,1 -norm. Experiments on DDSM...
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, 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...
Image enhancement technologies have been widely used for improving the quality of the images for screening mammograms. In this paper, we will focus on the enhancement of breast calcifications, which are the deposits of calcium that can be seen on a mammogram of the breast. In the proposed method, the original image and the normalized gradient image of the original image are first decomposed into a...
Mammography is currently regarded as one of the best ways to detect breast cancer in the early stage. However, due to the limitation in imaging condition and the subtleness of the difference between normal and abnormal features, it is generally difficult to interpret the mammograms. Thus, image enhancement techniques have been widely used in screening mammograms. In this paper, a multiscale contrast...
In breast cancer diagnosis, the radiologists mainly use their eyes to discern cancer when they screen the mammograms. However, in many cases, cancer is not easily detected by the eyes because of the bad imaging conditions. In order to improve the correct diagnosis rate of cancer, image-enhancement technology is often used to enhance the image and aid the radiologists. In this paper, we develop a new...
In this paper, we develop an image enhancement technology in the DCT domain for radiologists to screen mammograms. The proposed algorithm is an improved version of the algorithm developed. The improved algorithm overcomes the shortcomings of the old algorithm which requires manual adjustment of multiple parameters to realize non-uniform enhancement in different frequency bands. The proposed image...
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