Abnormality Detection in Brain MR Images Using Minimum Error Thresholding Method

Document Type

Article

Publication Date

6-2006

Publication Title

International Journal of Computational Intelligence and Applications

Volume

6

Issue

2

First page number:

177

Abstract

Medical image segmentation plays an instrumental role in clinical diagnosis. An ideal medical image segmentation scheme should possess some preferred properties such as minimum user interaction, fast computation, and accurate and robust segmentation results. In this paper, an automated algorithm is proposed to enable the doctors to detect the presence of abnormal tissues in brain magnetic resonance images (MRIs). The merged image of different weighted images of each slice is obtained by averaging the intensities of pixels and is enhanced based on their local information by variance mapping. The abnormal regions are segmented by using minimum error thresholding method by formulating a criterion function. The segmentation is performed on the real data of MRI images for different abnormalities and the results are compared with radiologist labeled ground truth. Quantitative analysis between ground truth and segmented abnormal region is presented in terms of Percent Match and Correspondence Ratio. A maximum average percent match of 98.56% and correspondence ratio of 0.8892 of an MRI data is obtained.

Keywords

Algorithms--Data processing; Image analysis; Image segmentation; Magnetic resonance imaging; Minimum error thresholding

Disciplines

Bioimaging and Biomedical Optics | Diagnosis | Electrical and Electronics | Signal Processing | Systems and Communications

Language

English

Permissions

Use Find in Your Library, contact the author, or use interlibrary loan to garner a copy of the article. Publisher copyright policy allows author to archive post-print (author’s final manuscript). When post-print is available or publisher policy changes, the article will be deposited

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