Implementation of K-Means Clustering and Fuzzy C-Means Algorithm for Brain Tumor Segmentation

• Published in: International journal of advanced research in computer science Vol. 4; no. 8
• Main Authors: Gupta, Pritee, Gupta, Jitendra, Shringirishi, Mrinalini, Bhattnagar, Deepak
• Format: Journal Article
• Language: English
• Published: Udaipur International Journal of Advanced Research in Computer Science 01.05.2013
• Subjects: Algorithms; Brain; Brain cancer; Brain research; Cluster analysis; Computer science; Datasets; Engineering; Fuzzy; Image detection; Intracranial cavity; Magnetic resonance imaging; Medical imaging; Segmentation; Tumors; India
• ISSN: 0976-5697

This paper deals with the implementation of Simple Algorithm for detection of range and shape of tumor in brain MR images. Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different Characteristics and different treatment. As it is known, brain tumor is inherently serious and life-threatening because of its character in the limited space of the intracranial cavity (space formed inside the skull). Most Research in developed countries show that the number of people who have brain tumors were died due to the fact of inaccurate detection. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection & diagnosis of brain tumor. However this method of detection resists the accurate determination of stage & size of tumor. To avoid that, this work uses computer aided method for segmentation (detection) of brain tumor based on the k.means algorithms. This method allows the segmentation of tumor tissue with accuracy and reproducibility comparable to manual segmentation. In addition, it also reduces the time for analysis.