An overview of MRI brain classification using FPGA implementation

The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. Brain images have been selected for the image references because; the injuries to the brain tend to affect large areas of the organ. 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 can be characterized as being computationally intensive. One reason for this is 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 data rates, a careful and creative data management must be provided. Field Programmable Gate Array (FPGA) is one of the alternative that offer custom computing platforms, sufficiently flexible that new algorithms can be implemented on existing hardware and fast enough.