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Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet...
Magnetic resonance (MR) imaging is widely used in daily medical treatment. It could help in pre-surgical, diagnosis, prognosis, and postsurgical processes. It could be beneficial for diagnosis to classify MR images of brain into healthy or abnormal automatically and accurately, since the information set MRIs generate is too large to interpret with manual methods. We propose a new approach with wavelet-entropy...
Blood cell images are difficult for segmentation because of their intrinsic characteristics. We proposed a dual-layer structure to solve the problem: the 1st layer introduces and simplifies pulse coupled neural network to split the images based on traditional Split-Merge method, and Mumford-Shah model was utilized to merge the split areas. The output of the first layer is a coarse segmented image...
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