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Breast tumor detection in digital mammography is one of the most important methods of breast cancer prevention. Computer-aided diagnosis (CAD) based on extreme learning machine (ELM) has significant meanings for breast tumor detection as it has good generalization abilities and a high learning efficiency. In this paper, a breast tumor detection algorithm in digital mammography based on ELM is proposed...
Medical image segmentation is an essential step for most subsequent image analysis tasks. In this paper a hybrid image segmentation algorithm is proposed, which combines the morphological method of watershed and fuzzy c-means (FCM) clustering. A dilation-erosion contrast enhancement approach is used as a preprocessing stage in order to obtain an accurate estimation of the image borders. Then an initial...
An image segmentation approach is presented that merges watershed segmentation regions with the nearest neighbor connecting tree (NNCT). Firstly a dilation-erosion contrast enhancement processing is used as a preprocessing stage to obtain an accurate estimate of the image borders. Then the maker-controlled watershed transform is applied to produce an initial partitioning of the image into primitive...
Based on K-means and a two-layer pyramid structure, a fast algorithm is proposed for color image segmentation. The algorithm employs two strategies. Firstly, a two-layer structure of a color image is established. Then, an improved K-means with integer based lookup table implementation is applied to each layer. The clustering result on the upper layer (lower resolution) is used to guide the clustering...
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