Computer aided system for brain abnormalities segmentation / Shafaf Ibrahim, Noor Elaiza Abdul Khalid and Mazani Manaf

Detection of abnormalities in brain tissue area in different medical images is inspired by the necessity of high accuracy when dealing with human life. A Variety of diseases occur in brain tissue area such as brain tumour, stroke, infarction, haemorrhage and others. At the present time, the current...

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Bibliographic Details
Main Authors: Ibrahim, Shafaf, Abdul Khalid, Noor Elaiza, Manaf, Mazani
Format: Article
Language:English
Published: Faculty of Computer and Mathematical Sciences 2010
Online Access:https://ir.uitm.edu.my/id/eprint/11104/1/11104.pdf
https://ir.uitm.edu.my/id/eprint/11104/
https://mjoc.uitm.edu.my/
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Institution: Universiti Teknologi Mara
Language: English
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Summary:Detection of abnormalities in brain tissue area in different medical images is inspired by the necessity of high accuracy when dealing with human life. A Variety of diseases occur in brain tissue area such as brain tumour, stroke, infarction, haemorrhage and others. At the present time, the current method that is used for diagnosing those diseases is using a well known digital imaging technique which is Magnetic Resonance Imaging (MRI), though the brain diseases are still difficult to diagnose due to certain circumstances. Thus, Computer Aided System (CAS) is significantly useful due to the fact that it could enhance the results of humans in such domain. It is also important that the false negative cases must be kept at a very low rate. This paper proposes a development of a CAD that implement image processing techniques for segmenting any kind of abnormalities that occur in human brain tissue area. The system is able to determine the patterns and characteristics for each part of particular brain tissue in order to identify any brain abnormalities. The behind idea is that the local textures in the images can reveal the characteristic of abnormalities of the biological structures. Therefore, the system is expected to detect threats in patients and planning for early treatment strategies in the future.