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- W2186330213 abstract "Medical image analysis is an important bio- medical application Radiologists use medical images to diagnose diseases precisely. However, identification of brain tumor from medical images is still a critical and complicated job for a radiologist. Brain tumor identification from magnetic resonance imaging (MRI) consists of several stages. Segmentation is known to be an essential step in medical imaging classification and analysis. Performing the brain MR images segmentation manually is a difficult task as there are several challenges associated with it. Radiologist and medical experts spend plenty of time for manually segmenting brain MR images, and this is a non-repeatable task. In view of this, an automatic segmentation of brain MR images is needed to correctly segment the tumor part of the brain in a shorter span of time. The accurate segmentation is crucial as otherwise the wrong identification of disease can lead to severe consequences. In this paper we propose a method for automatic brain tumor diagnostic system from MR images. The system consists of three stages to detect and segment a brain tumor. In the first stage, MR image of brain is acquired and preprocessing is done to remove the noise and to sharpen the image. In the second stage, edges are detected by using gabor filter. In the third stage, threshold segmentation is done on the sharpened image to segment the brain tumor and the segmented image is post processed by morphological operations and tumor masking in order to remove the false segmented pixels. experiment show that technique accurately identifies and segments the brain tumor in MR images." @default.
- W2186330213 created "2016-06-24" @default.
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- W2186330213 date "2014-01-01" @default.
- W2186330213 modified "2023-09-27" @default.
- W2186330213 title "Gabor Wavelet Approach for Automatic Brain Tumor Detection" @default.
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