Diagnosis system for the detection of abnormal tissues from brain MRI

Arshad, Javed and Abdulhameed, Rakan Alenezi and Wang, Yin Chai and Narayanan, Kulathuramaiyer (2013) Diagnosis system for the detection of abnormal tissues from brain MRI. Life Science Journal, 10 (2). pp. 1949-1955. ISSN 1097-8135

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The brain tumor is widely disseminating disease all over the world and causing the increasing death rates. If the tumor is diagnosed at early stages, the increasing death rate can be decreased to some extent. Manual segmentation of brain MR images by experts is very expensive, non-repeatable and time consuming task. The computer-aided diagnosis system assists experts to take the opinion to diagnose the disease severity. The diagnosis process can be affected if the images are low contrast or poor quality and wrong diagnoses chances become high. The objective of this paper is to establish an automatic, accurate, fast and reliable diagnosis system which could be able to diagnose the brain tumor and also extract the region of the brain tumor from brain MR images. The median filter is used for enhancing the poor quality image, fuzzy c-means clustering technique for segmentation of images and mathematical morphological operations are performed to extract the abnormal portion from images. The proposed technique is applied on different brain MR images for both visual evaluations and quantitative. Experimental results of the proposed method showed, the proposed approach provides a fast, effective and promising method for the brain tumor extraction from MR images with high accuracy.

Item Type: Article
Uncontrolled Keywords: Fuzzy c-means clustering; Image segmentation; Mathematical morphological operators, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > RD Surgery
Divisions: Academic Faculties, Institutes and Centres > Faculty of Computer Science and Information Technology
Depositing User: Saman
Date Deposited: 17 Apr 2017 01:26
Last Modified: 24 Jan 2022 07:22
URI: http://ir.unimas.my/id/eprint/15951

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