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FPGA implementation on MRI brain classification using support vector machine

Abdullah, Noramalina (2009) FPGA implementation on MRI brain classification using support vector machine. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering.



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Abstract

The field of medical imaging gains its importance with in crease in the need of automated and efficient diagnosis in a short period of time. Brain images have been selected for the image references since injuries to the brain tend to affect other organs. Magnetic Resonance Imaging (MRI) is an imaging technique that has been playing an important role in neuroscience research for studying brain images. The classifications of brain MRI data as normal and abnormal are important to prune the normal patient and to consider only those who have the possibility of having abnormalities or tumor. An advanced kernel based techniques such as Support Vector Machine (SVM) for the classification of volume of MRI data as normal and abnormal will be deployed. Image processing tasks are computationally intensive due to the vast amount of data that requires the processing of more than seven million pixels per second for typical images sources. To keep up with this, a careful and creative data management must be provided. Field Programmable Gate Array (FPGA) is one of the alternatives that offer custom computing platform, sufficiently flexible and fast enough for new algorithms to be implemented on existing hardware

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