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24 June 1998 Multilevel thresholding selection by optimizing multiple constraint function
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Thresholding is one of the most common techniques for extracting objects from background because of its promising speed and simplicity. The images are usually ill-defined and the histograms are very noisy, therefore, the image thresholds are not easy to determine from the histogram directly. Based on fuzzy set theory, several fuzzy set algorithms have been reported to partition an image into objects and non-interesting regions to reduce the dimensionalities of the image. However, most of the existing approaches just choose the bandwidth of fuzzy membership functions experimentally/subjectively. In this paper, we propose a novel method to determine the bandwidth of fuzzy membership functions automatically. The optimal threshold is determined by choosing the proper bandwidth and minimizing the measure of fuzziness. The proposed approach has been tested on many images. The advantages of the proposed approach are: the bandwidth of fuzzy membership function is determined automatically and different bandwidths would be found between peaks so that the variations of the portions of the index-grey level graph can have different bandwidths. The images are well segmented, the details of the image are well preserved, and the segmented regions are homogeneous.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Heng-Da Cheng and Y. M. Lui "Multilevel thresholding selection by optimizing multiple constraint function", Proc. SPIE 3338, Medical Imaging 1998: Image Processing, (24 June 1998);

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