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To improve the accuracy and sensitivity of the breast tumor classification based on ultrasound images, a computer-aided classification algorithm is proposed using the Affinity Propagation (AP) clustering. Five morphologic features and three texture features are extracted from each breast ultrasound image. The AP clustering with an empirical value of "preference" is used as the primary classification...
Cell segmentation from microscopic images is the first stage of the automatic biomedical image processing, which plays a crucial role in the study of cell behavior and cell structure. In this paper, a novel approach is proposed to segment cells which are characterized as elliptical objects. Cell segmentation is implemented using an ellipse detection algorithm based on ellipse curve segmentation and...
According to texture analysis in low frequency sub-band of DWT, an adaptive image hiding algorithm is proposed. The low-frequency part of DWT is classified as smooth, edge and texture region by entropy and standard deviation, and different regions are assigned with different hiding bits, which will increase the hiding capacity. The blind detection is accomplished by resetting low bits in DWT low-frequency...
A computerized classification based on morphologic and texture features is proposed to increase the accuracy of the ultrasonic diagnosis of breast tumors. Firstly, tumor boundaries are obtained with the gray-level threshold segmentation algorithm and the dynamic programming method. Then five morphologic features and two texture features are extracted. Finally, an artificial neural network with the...
The real vision system has a well-developed ability to detect multiple contours and recognize various objects in images. Previous simulation models to perform this process often employ image segmentation or contour integration algorithms. In this paper a new model is proposed to separate individual object contours from the background by the feature clustering. The model is inspired by the contrast...
The classification of the uterine myoma and adenomyosis from their ultrasound images mainly depends on doctors' experience and lacks objective criterions. Here a novel classification method is proposed using the multiresolution analysis and the orientational fractal analysis. Firstly, texture features under various resolutions and orientational fractal features are obtained from ultrasound images...
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