A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the use of fuzzy sets in histogram equalization
Fuzzy Sets and Systems
Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets
Fuzzy Sets and Systems
Two theorems for intuitionistic fuzzy sets
Fuzzy Sets and Systems
Color image enhancement by fuzzy intensification
Pattern Recognition Letters
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
A new measure using intuitionistic fuzzy set theory and its application to edge detection
Applied Soft Computing
The Role of Entropy in Intuitionistic Fuzzy Contrast Enhancement
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
A method based on rank-ordered filter to detect edges in cellular image
Pattern Recognition Letters
| Hi-index | 0.00 |
This paper gives a novel scheme using intuitionistic fuzzy set theory to enhance the edges of medical images. Medical images contain lots of uncertainties, as they are poorly illuminated and fuzzy/vague in nature. So, direct segmentation techniques will not produce better results. There are lots of researches on edge enhancement starting from non-fuzzy to fuzzy set, but proper enhancement (highlighting important structures) is not obtained. Enhancement of edges helps in recovering the important structures that are not visible properly. Even minute pathological blood vessels/cells are not visible properly and in that case edge enhancement will enhance these blood vessels/cells. Intuitionistic fuzzy set theory is found suitable in medical image processing as it considers more (two) uncertainties as compared to fuzzy set theory. In the processing phase, image is initially converted to intuitionistic fuzzy image and intuitionistic fuzzy entropy is used to obtain the optimum value of the parameter in the membership and non-membership functions. Then it computes the total variation of the pixels with respect to the median value of the image window (rank order filtering). This enhances the borders or the edges of the image. The resulting image is then segmented (edge detected) using standard Canny's edge detector, when simply using Canny's edge detector does not give better result. From the result it is observed that on comparing with non-fuzzy and fuzzy methods, the proposed method gives better information about the images, which is helpful to the pathologists in accurate diagnosing of diseases.