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N. cDNA Microarray Image Processing using Fuzzy Vector Filtering Frameworks. Fuzzy Sets Syst. 2005, 152 (1), pp 17–35. 41. ; Ramponi, G. Nonlinear Fuzzy Operators for Image Processing. Signal Proc. 1994, 38, pp 429–440. 42. ; Ramponi, G. A fuzzy Operator for the Enhancement of Blurred and Noise Images. IEEE Trans. Image Proc. 1995, 4(8), pp 1169–1174. 43. ; Pitas, I. Fuzzy Scalar and Vector Median Filters based on Fuzzy Distances. IEEE Trans. Image Proc. 1999, 8(5), pp 731–734. 44. Senel, H. ; Peters II, R.
Let us denote the average, maximum, and minimum values by Avg, Max, and Min, respectively. Now deﬁne the following parameters. A π-type membership function (Fig. 5 and µ(B) = 1. 5. Such a µxy , therefore, characterizes a fuzzy set “pixel intensity close 3 to its average value,” averaged over Nx,y . , they are within the same region), such a transformation will make all µxy = 1 or close to 1. In other words, if no edge exists, pixel values will be close to each other and the µ values will be close to one (1); thus resulting in a low value of H 1 .
Kundu, M. ; Chanda, B. Segmentation of Brain MR Images Using Fuzzy Sets and Modiﬁed Co-occurrence Matrix; IEEE Proc. International Conference on Visual Information Engineering (VIE-06);India, September, 2006. 116. ; Dougherty, E. ; Batman, S. Design and Analysis of Fuzzy Morphological Algorithm for Image Processing. IEEE Trans. Fuzz. Syst. 1997, 5(4), pp 570–584. 117. ; Tsalides, P. Fuzzy Soft Mathematical Morphology. - Vision, Image and Signal Processing; 1998, 145(1), pp 41–49. 118. Bloch, I.