A Review on Brain Tumor Detection Using Segmentation And Threshold Operations

The brain is the anterior most part of the central nervous system. The location of tumors in the brain is one of the factors that determine how a brain tumor effects an individual's functioning and what symptoms the tumor causes. Along with the Spinal cord, it forms the Central Nervous System (CNS). Brain tumor is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. Magnetic Resonance Imager (MRI) is the commonly used device for diagnosis. In MR images, the amount of data is too much for manual interpretation and analysis. During past few years, brain tumor segmentation in magnetic resonance imaging (MRI) has become an emergent research area in the field of medical imaging system. Accurate detection of size and location of brain tumor plays a vital role in the diagnosis of tumor. An efficient algorithm is proposed for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. We also propose an efficient wavelet based algorithm for tumor detection which utilizes the complementary and redundant information from the Computed Tomography (CT) image and Magnetic Resonance Imaging (MRI) images. Hence this algorithm effectively uses the information provided by the CT image and MRI images there by providing a resultant fused image which increases the efficiency of tumor detection. Keywords—Brain Tumor, MRI, Morphological & Watershed Operators, Threshold Operations & TextNoise Removal.

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