Multispectral segmentation of whole-brain MRI

Magnetic Resonance Imaging (MRI) is a widely used medical technology for diagnosis and detection of various tissue abnormalities, tumor detection, and in evaluation of either residual or recurrent tumors. This thesis work exploits MRI information acquired on brain tumor structure and physiological properties and uses a novel image segmentation technique to better delineate tissue differences.;MR image segmentation will be important in distinguishing between boundaries of different tissues in the brain. A segmentation software tool was developed that combines the different types of clinical MR images and presents them as a single colored image. This technique is based on the fuzzy c-means (FCM) clustering algorithm. The MR data sets are used to form five-dimensional feature vectors. These vectors are segmented by FCM into six tissue classes for normal brains and nine tissue classes for human brains with tumors. The segmented images are then compared with segmentation performed using Statistical Parametric Mapping (SPM2)---software that is commonly used for brain tissue segmentation. The results from segmenting the whole volume MRI using FCM show better distinction between tumor tissues than SPM2.

Name thesis:degree_name

MS

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Lane Department of Computer Science and Electrical Engineering

Year dc:date.available

2004

Author dc:creator

• Krishnan, Nitya

Contributors dc:contributor

• Susan K. Lemieux
• Mark Jerabek

• Electrical engineering
• Biomedical engineering
• Medical imaging

Identifier

https://researchrepository.wvu.edu/etd/1548

OAI identifier oai:identifier

oai:researchrepository.wvu.edu:etd-2551