Brain tumor segmentation and its area calculation in brain MR images using K-mean clustering and Fuzzy C-mean algorithm

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...

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Bibliographic Details
Published in2012 International Conference on Advances in Engineering, Science and Management pp. 186 - 190
Main Authors Selvakumar, J., Lakshmi, A., Arivoli, T.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2012
Subjects
Abnormalities
Algorithm design and analysis
Brain tumor
Clustering algorithms
Convergence
Filtering algorithms
Fuzzy C-means
Image edge detection
Image segmentation
K-means
Magnetic Resonance Imaging (MRI)
Pre-processing
Thresholding
Tumors
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ISBN9781467302135
1467302139

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Summary: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 project uses computer aided method for segmentation (detection) of brain tumor based on the combination of two 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. At the end of the process the tumor is extracted from the MR image and its exact position and the shape also determined. The stage of the tumor is displayed based on the amount of area calculated from the cluster.
ISBN:9781467302135
1467302139