ISSN (Online) : 2456 - 0774

Email : ijasret@gmail.com

ISSN (Online) 2456 - 0774


BRAINTUMOR SEGMENTATION USING K-MEANS CLUSTERING AND FUZZY C-MEANS ALGORITHMS ANDITS AREA CALCULATION AND DISEASE PREDICTION


Abstract

Abstract - This work dealswith the implementation of Simple Algorithm for detection of range and shape oftumor in brain MR images and predicts the disease risk details from the givenarea of tumor. Tumor is an uncontrolled growth of tissues in any part of thebody. Tumors are of different types and they have different Characteristics anddifferent treatment. As it is known, brain tumor is inherently serious andlife-threatening because of its character in the limited space of theintracranial cavity (space formed inside the skull).

            Most Research in developedcountries show that the number of people who have brain tumors were died due tothe fact of inaccurate detection. Generally, CT scan or MRI that is directedinto intracranial cavity produces a complete image of brain. After researchinga lot statistical analysis which is based on those people whose are affected inbrain tumor some general Risk factors and Symptoms have been discovered.  The development of technology in science daynight tries to develop new methods of treatment. This image is visuallyexamined by the physician for detection & diagnosis of brain tumor. Howeverthis method accurate determines the accurate of stage & size of tumor andalso predicts the disease details from the area of tumor. This work usessegmentation of brain tumor based on the k-means and fuzzy c-means algorithms.This method allows the segmentation of tumor tissue with accuracy andreproducibility comparable to manual segmentation. In addition, it also reducesthe time for analysis and predicts the disease details from the given area oftumor.

Finally implement asystem using java to predict Brain tumor risk level which is easier, costreducible and time savable.

KeyWords:Tumor, CT scan, MRI, k-meansalgorithms, Fuzzy -means algorithms


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