Local segmentation of biomedical images.
In this paper, a new algorithm for local segmentation of biomedical images is presented. First, a relatively small region is selected for segmentation on the basis of dispersion measurement of local gray values. This small region is then segmented using a segmentation algorithm based on quantization approach. While quantizing a signal, the range of input signal is divided into a number of segments. All signal values within a segment are assigned a unique reconstruction value. In segmentation of gray level images, the problem is to classify or code gray values of the pixels into two or more groups. An N-level threshold selection method for segmentation thus becomes the design of an N-level optimal quantizer. This new approach is suitable for a number of biomedical applications where the objects of interest appear as small and localized in the images. Some experimental results are also provided which illustrate the success of the new scheme.
Kundu
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Department of Electrical Engineering, State University of New York, Buffalo 14260.
D000465:Algorithms; D006257:Head / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008168:Lung / Q000000981:diagnostic imaging; D011009:Pneumoconiosis / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed / Q000379:methods