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Tumor Detection In Brain Using Morphological Image Processing

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Abstract:
In developing countries like India the causes of disease like cancer will degrade the economy of the country. Image processing is the field to detect these kind of unwanted cells and reviles the amount it spreads. In this paper the detection of tumor in brain, either malignant tumor or non- malignant tumor is done. The morphological image processing is to be used in order to locate and identify the size of tumor. The image from MRI scan will tell the presence of tumor in the brain, but we have to find the size of that tumor. The recent technology came for finding the size, shape, type and other important specification regarding the tumor like CT scan. This paper will show how the image from MRI scan is adjusted to suitable contrast and tumor is separated from the original image.
Keywords: malignant, tumor, MRI scan, CT scan, morphological image processing, separate
I.Introduction
Image processing is the field were the information from images can be retrieved using suitable algorithm. In this paper the morphological image processing is used to detect the tumors from the brain either malignant or non-malignant tumors. The brain tumors some times change to malignant will leads to cancer. There are several techniques to capture image of brain like MRI, CT scan etc… A tumor is a mass of tissue that grows out of control of the normal forces that regulates growth. The multifaceted brain tumors can be split into two common categories depending on the tumors beginning, their enlargement prototype and malignancy. Primary brain tumors are tumors that take place commencing cells in the brain or commencing the wrapper of the brain. In this paper, the morphological operations like dilation, erosion etc… was done to remove the tumor from the MRI Image. Recent techniques achieved in researches for detection of brain tumor can be broadly classified as
1. Histogram based method.
2. Morphological operation is applied to MRI images of Brain.
3. Edge base segmentation and color base segmentation.
4. Cohesion self-merging based partition K-mean Algorithm. We are going to use only the morphological operation.

References:

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