Investigation of edge detection techniques Based on brain tumor images
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Date
2018-06
Authors
Murni Nur Athirah Rosnan
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Abstract
Medical image processing has become an important technique that can visualize the
interior of a human body for better diagnosis and extraction of an anatomical structure.
Image processing has an advantage which reproduced original data repetitively without any
changes that helps radiologist for analysis. Magnetic Resonance Imaging(MRI) is one of
the medical imaging modalities that depend on computer technology to create detailed
images of the brain. The output image by MRI need to undergo several imaging techniques
to extract the important information accurately. In this work, all input MRI brain images are
in DICOM format. The images undergo three fundamental steps of edge detection
techniques. The edge detection operators used to detect the brain tumor are Robert
zero-crossing, Sobel operator, Prewitt operator, Canny operator and modified Canny
algorithm. The visual results from each operators are analyzed using quantitative and
qualitative measurement. The quantitative parameters used to evaluate the operators
performances are PSNR, MSE and SSIM. Based on the quantitative analysis, the new
Canny algorithm successfully produced high quality image with less error. However, from
visual perspective, Sobel operator produced better edge maps of the brain tumor compared
to the Modified Canny algorithm.