medical image segmentation has received tremendous amount of response from the hospitals and the research due to its various practical applications of segmentation results. Algorithms used for medical image segmentation can applied identify various pathological changes in brain and especially to recognize tumors and lesions in the brain area.
This can be performed by first separating the recognizable neuro-anatomical structures. Further, these algorithms can also be used to determine specific disease of human brain. In order to perform this, identifying the genesis of the disease is quite important to decide the cause and to workout the options for treatment depending on the affected anatomical structre of the brain where the pathologis lies. Applications of medical image segmentation depends on the specific disease, imaging techniques and the other factors.
Most of the image processing applications such as medical image segmentation algorithms uses FPGAs to segment the desired object with a faster rate. These systems are are typically programmed with Hardware Description Languages (HDL) and microprocessor-based DSP design methodologies . The hardware configuaration is designed in such a way that, all the required functions of image processing applications can be transferred to FPGA that allows faster processing of the task
The Xilinx platform studio based EDK code is developed on the FPGA Vertex 5 series and the tumor boundary detection techniques were used to find the brain tumor on the MRI images. Matlab based program is used to convert the image to the bit stream array which would be used as the header file on the Xilinx platform studio. This bit stream is taken and the calculation for the tumour detection using the level set based technique is developed and the output is sent back to matlab. The tumour detected image is given to the matlab for displaying the final results. The results of the proposed technique proves that, there is a signican improvements in the segmentation accuracy and reduction of computational time as compared the existing medical image segmentation algorithms.
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