In recent decades, human brain tumor detection has became one of the most
demanding issue in medical science. A brain tumor is the unnatural growth of
tissue that can interrupt the proper function of the brain. The early
detection an recognition of the brain tumor is a very crucial as it saves the
life of the humans. Brain tumor extraction an its analysis or challenging task
in medical image processing because brain image an its structure is
complicated that can be analyzed only by expert radiologists. MRI (Magnetic
Resonance Imaging) has became a particularly useful medical diagnostic tool
for diagnosis of brain and other medical images. The important challenge in
image processing is the presence of noise during the image capture process
which may result either under segmentation or over segmentation. Segmentation
of an image is the separation or division of the image into different regions
of similar feature. Segmentation plays an important role in the processing of
medical images. To improve the stability of segmentation an effective
clustering algorithm is proposed for segmenting the brain tumor. First filters
are utilized for diagnosing which effectively reduce the noise in images and
secondly clustering segmentation is implemented to segment the tumor from the
MRI images. The proposed technique is implemented on MATLAB. In addition, the
proposed algorithm was compared with other segmentation algorithms and the
results shows that proposed algorithm performs better compared with other
segmentation algorithm.
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