NDLI: Automatic Segmenting Structures in MRI’s Based on Texture Analysis and Fuzzy Logic

Content Provider	Springer Nature Link
Author	Kaur, Mandeep Rattan, Munish Singh, Pushpinder
Copyright Year	2016
Abstract	The purpose of this paper is to present the variational method for geometric contours which helps the level set function remain close to the sign distance function, therefor it remove the need of expensive re-initialization procedure and thus, level set method is applied on magnetic resonance images (MRI) to track the irregularities in them as medical imaging plays a substantial part in the treatment, therapy and diagnosis of various organs, tumors and various abnormalities. It favors the patient with more speedy and decisive disease controlling with lesser side effects. The geometrical shape, the tumor’s size and tissue’s abnormal growth can be calculated by the segmentation of that particular image. It is still a great challenge for the researchers to tackle with an automatic segmentation in the medical imaging. Based on the texture analysis, different images are processed by optimization of level set segmentation. Traditionally, optimization was manual for every image where each parameter is selected one after another. By applying fuzzy logic, the segmentation of image is correlated based on texture features, to make it automatic and more effective. There is no initialization of parameters and it works like an intelligent system. It segments the different MRI images without tuning the level set parameters and give optimized results for all MRI’s.
Starting Page	1
Ending Page	19
Page Count	19
File Format	PDF
ISSN	15572064
Journal	Sensing and Imaging: An International Journal
Volume Number	18
Issue Number	1
e-ISSN	15572072
Language	English
Publisher	Springer US
Publisher Date	2016-12-19
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fuzzy logic Magnetic resonance image Segmentation Texture analysis Electrical Engineering Microwaves, RF and Optical Engineering Imaging Radiology
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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