A survey on Segmentation Techniques For Brain Tumor Detection and Classification

Image Processing

In this era Biomedical Image Processing is a growing and demanding field. It consists many different types of imaging methods likes CT scans, X-Ray and MRI. These techniques allow humans to identify even the smallest abnormalities in the human body. The primary goal of medical imaging is to extract meaningful and accurate information from the images with the least error possible. Out of the various types of medical imaging processes available to us, MRI is the most reliable and safe. MRI (Magnetic Resonance Imaging) is a medical technique, mainly used by the radiologist for visualization of internal structure of the human body. MRI provides useful information about the human soft tissue, which helps in the diagnosis of brain tumor. Image segmentation refers to partitioning of image into multiple regions or segments such that it can meaningfully represent the image through which information can be extracted. Accurate segmentation of MRI image is important for the diagnosis of brain tumor After appropriate segmentation of brain MR images, tumor is classified to malignant and benign, which is a difficult task due to complexity and variation in tumor tissue characteristics like its shape,size ,gray level intensities and location. Taking in to account these challenges, this research is focused towards highlighting the strength and limitations of earlier proposed segmentation and brain tumor detection techniques discussed in the contemporary literature.

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