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An Accurate, Fast and Noise-Adaptive segmentation of Brain MR Images for clinical Analysis is a challenging problem. An improved Hybrid Clustering Algorithm is presented here, which integrates the concept of recently popularized Rough Sets and that of Fuzzy Sets. The concept of lower and upper approximations of rough sets is incorporated to handle uncertainty, vagueness, and incompleteness in class...
In present study attempt has been taken to determine the degree of malignancy of brain tumors using artificial intelligence. The suspicious regions in brain as suggested by the radiologists have been segmented using fuzzy c-means clustering technique. Fourier descriptors are utilized for precise extraction of boundary features of the tumor region. As Fourier descriptors introduce a large number of...
In present work generalized notion of fuzzy sets is applied to achieve more flexible and intelligent contrast enhancement system for medical images of section of human bain. Applying wavelet transform two images are obtained, one is containing low frequency components (LF) other one is containing high frequency components (HF). LF image is fuzzified by fuzzy c-means clustering (FCM) technique with...
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