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A two-class support vector machine (SVM)-based image segmentation approach has been developed for the extraction of nasopharyngeal carcinoma (NPC) lesion from magnetic resonance (MR) images. By exploring two-class SVM, the developed method can learn the actual distribution of image data without prior knowledge and draw an optimal hyperplane for class separation, via an SVM parameters training procedure...
This paper describes an adaptive pyramid filtering method for increasing medical ultrasound image qualities. Most medical ultrasound scanners employ logarithmic function to reduce dynamic range of the echo envelop signals. In our method, a log-compressed speckle image is first decomposed into multi resolution representations using the Laplacian pyramid (LP). Each LP layer is then filtered with an...
Iterative reconstruction methods such as the expectation maximization maximum likelihood (EMML) method can be accelerated by using a rescaled block-iterative (RBI) algorithm. It was demonstrated that the space-alternating generalized expectation-maximization (SAGE) algorithm is superior to the EMML due to the following facts: (1) The hidden data spaces can be appropriately chosen and then be used...
A novel hierarchical image segmentation approach has been developed for the extraction of tongue carcinoma from magnetic resonance (MR) images. First, a genetic algorithm (GA)-induced fuzzy clustering is used for initial segmentation of MR images of head and neck. Then these segmented masses are refined to reduce the false-positives using an artificial neural network (ANN)-based symmetry detection...
This work presents a new iterative method for reconstructing positron emission tomography (PET) images. Unlike conventional maximum likelihood-expectation maximization (MLEM), this method intends to introduce the fuzzy set principle to MLEM algorithm. In this work, the noncognitive uncertainty of the observed projection data are described by their probability density function; whereas the cognitive...
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