Ultrasonography is one of the most useful diagnostic tools for human soft tissue and is one of the methods that are in routine use for distinguishing benign and malignant breast tumors. But its diagnosis is operator dependent. In previous researches texture analysis fo More
Ultrasonography is one of the most useful diagnostic tools for human soft tissue and is one of the methods that are in routine use for distinguishing benign and malignant breast tumors. But its diagnosis is operator dependent. In previous researches texture analysis for solid breast mass classification is used. In those works texture features of the tumor are used, but sonologists notice to the features of the surrounding area of the tumors for their diagnosis. In this research as well as the morphological features of the mass the features of the surrounding area of the mass are also considered. MLP neural network is used for classification. 36 breast sonography images are used that 18 of them proved to be benign and 18 of them proved to be malignant through biopsy. The features are used in different combinations and it is shown that using the texture features of behind the tumor area and the same depth near the tumor provide meaningful result and also compensate the different adjustments of the systems.
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Cardiac diseases are the major causes of death throughout the world. The study of left ventricular (LV) function is very important in the diagnosis of heart diseases. Automatic tracking of the boundaries of the LV wall during a cardiac cycle is used for quantification o More
Cardiac diseases are the major causes of death throughout the world. The study of left ventricular (LV) function is very important in the diagnosis of heart diseases. Automatic tracking of the boundaries of the LV wall during a cardiac cycle is used for quantification of LV myocardial function in order to diagnose various heart diseases including ischemic disease. In this paper, a new automatic method for segmentation of the LV in echocardiography images of one cardiac cycle by combination of manifold learning and active contour based dynamic directed vector field convolution (DDVFC) is proposed. In this method, first echocardiography images of one cardiac cycle have been embedded in a two dimensional (2-D) space using one of the most popular manifold learning algorithms named Locally Linear Embeddings. In this new space, relationship between these images is well represented. Then, segmentation of the LV wall during a cardiac cycle is done using active contour based DDVFC. In this method, final contour of each segmented frame is used as the initial contour of the next frame. In addition, in order to increase the accuracy of the LV segmentation and also prevent the boundary distortion, maximum range of the active contour motion is limited by Euclidean distances between consequent frames in resultant 2-D manifold. To quantitatively evaluate the proposed method, echoacardiography images of 5 healthy volunteers and 4 patients are used. The results obtained by our method are quantitatively compared to those obtained manually by the highly experienced echocardiographer (gold standard) which depicts the high accuracy of the presented method.
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