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Research Paper | Electronics Communication Engineering | Volume 3 Issue 4, April 2014 | Pages: 814 - 817 | India
Brain Tumor Detection and Identification from T1 Post Contrast MR Images Using Cluster Based Segmentation
Abstract: This paper consists of implementation of a simple algorithm for detection and identification of brain tumor in brain MR Images. Traditional method for medical resonance brain images classification and tumors detection is by human inspection also segmentation is performed manually in clinical environment that is operator dependent and very tedious and time consuming work. Brain Magnetic Resonance Image (MRI) segmentation is a complex problem in the field of medical imaging despite various presented methods. MR image of human brain can be divided into several sub-regions especially soft tissues such as gray matter, white matter and cerebrospinal fluid. And using this segmentation, exact location and type of brain tumor can be found out. System implemented here uses cluster based segmentation i. e. Fuzzy C-Means clustering to divide original MRI in number of clusters. Then tumor is extracted using thresholding and type of tumor is decided based on its area.
Keywords: Magnetic Resonance Imaging MRI, Image segmentation, Fuzzy C-Means, T1 MRI, Clustering
How to Cite?: Gauri Anandgaonkar, Ganesh Sable, "Brain Tumor Detection and Identification from T1 Post Contrast MR Images Using Cluster Based Segmentation", Volume 3 Issue 4, April 2014, International Journal of Science and Research (IJSR), Pages: 814-817, https://www.ijsr.net/getabstract.php?paperid=20131623, DOI: https://dx.doi.org/10.21275/20131623