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Multithreshold segmentation algorithm is time-consuming, and the time complexity will increase exponentially with the increase of thresholds. In order to reduce the time complexity, a novel multithreshold segmentation algorithm is proposed in this paper. First, all gray levels are used as thresholds, so the histogram of the original image is divided into 256 small regions, and each region corresponds to one gray level.
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Automatic image segmentation by dynamic region merging.

IEEE Trans Image Process 2011 Dec 23;20(12):3592-605. Epub 2011 May 23.
Bo Peng, Lei Zhang, David Zhang
This paper addresses the automatic image segmentation problem in a region merging style. With an initially oversegmented image, in which many regions (or superpixels) with homogeneous color are detected, an image segmentation is performed by iteratively merging the regions according to a statistical test. There are two essential issues in a region-merging algorithm: order of merging and the stopping criterion.

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Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, such as the selection of a suitable threshold value for the histogram-based method and the over-segmentation followed by the time-consuming merge processing in the region-based algorithm.

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Image segmentation is the key step for quantitative analysis of brain tissues (white matter, gray matter and cerebrospinal fluid). Based on genetic algorithm and fuzzy C-means (FCM) approach, a fast and fully automatic segmentation method of brain tissues named genetic fuzzy clustering algorithm is introduced in this paper. The method operates slice by slice based on three main steps: The non-brain tissues are removed from the original head MR images at first using an auto-threshold method; then the initial cluster centers of FCM are determined by genetic algorithm; and finally brain tissues are segmented into white matter, grey matter and cerebrospinal fluid by FCM via only one iteration computation.

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Here we proposed an automatic segmentation method based on a decision tree to classify the brain tissues in magnetic resonance (MR) images. Two types of data - phantom MR images obtained from IBSR (http://www.cma.

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