Abstract
In image processing, edge detection is an important venture. Fuzzy logic plays a vital role in image processing to deal with lacking in quality of an image or imprecise in nature. This present study contributes an authentic method of fuzzy edge detection through image segmentation. Gradient of the image is done by triangular norms to extract the information. Triangular norms (T norms) and triangular conorms (T conorms) are specialized in dealing uncertainty. Therefore triangular norms are chosen with minimum and maximum operators for the purpose of morphological operations. Also, mathematical properties of aggregation operator to represent the role of morphological operations using Triangular Interval Type-2 Fuzzy Yager Weighted Geometric (TIT2FYWG) and Triangular Interval Type-2 Fuzzy Yager Weighted Arithmetic (TIT2FYWA) operators are derived. These properties represent the components of image processing. Here Edge detection is done for DICOM image by converting into 2D gray scale image, using Type-2 fuzzy MATLAB and which is the novelty of this work.
Highlights
In the field of optimization problems in Mathematics, Statistics, Economics and Information Science, the max and min operators are very useful for any dimension
Incomplete data and errors in the analyzing stage will be the reason for getting vague situation and this can be dealt with fuzzy theory
The largest and the smallest elements of a precise set of real numbers is the maximum and the minimum and so Yager triangular norm is chosen for this work [6,7,8,9,10]
Summary
In the field of optimization problems in Mathematics, Statistics, Economics and Information Science, the max and min operators are very useful for any dimension. When the images with low brightness, the structure will not be visible In this situation, the sets which have better and naturally include different types of uncertainties might be useful for image analysis in any field. The sets which have better and naturally include different types of uncertainties might be useful for image analysis in any field To deal this complication Fuzzy Sets and their advanced extensions like T2FS Sets are suitable since it handles the uncertainty in a better way.
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