Abstract
There are numerous studies on brain imaging applications. The statistics in Malaysia showed that glioma is one of the most common type disease in brain tumor. Glioma brain tumor is an abnormal growth of glial cells inside the brain tissues which known as cerebral tissues. Radiologist commonly used Magnetic Resonance Imaging (MRI) image sequences to diagnose the brain tumor. However, manual examination of the brain tumor diagnosis by radiologist is difficult and time-consuming task as tumors are occurred in variability of shape and appearance. They will also inject a gadolinium contrast agent to enhance the image modality which will give the side effects to the patients. Therefore, this paper presents an automated segmentation and detection of MRI brain images using Sobel edge detection and mathematical morphology operations. The total of 30 glioma T1-Weighted MRI brain images are obtained from Brain Tumor Image Segmentation Benchmark (BRATS). The results of segmentation and detection are quantitatively evaluated by using Area Overlap which produced the accuracy rate of 80.2% and shows that the presented methods are promising.
Highlights
There are numerous studies in brain imaging domain such as brain abnormalities detection, brain mapping, brain stroke analysis, brain tumor volume analysis, brain tissue classification, epilepsy analysis, and brain tumor segmentation in a near future [1,2,3,4,5,6,7,8]
This paper presents an automated segmentation and detection of T1-weighted Magnetic Resonance Imaging (MRI) brain images of glioma brain tumor using the combination of Sobel edge detection and mathematical morphological operations
This paper experimented on the Sobel edge detection and mathematical morphology operations of closing, dilation, erosion and filling holes
Summary
There are numerous studies in brain imaging domain such as brain abnormalities detection, brain mapping, brain stroke analysis, brain tumor volume analysis, brain tissue classification, epilepsy analysis , and brain tumor segmentation in a near future [1,2,3,4,5,6,7,8]. There are several methods that can be applied in brain t umor applications such as image processing, deep learning, mathematical modeling and genetic algorithm [3,4,5,6,7,8,9,10,11,12,13,14,15,16]. It is challenging task in image processing when to segment the brain tumor as the homogeneity intensity of tumor, cerebral and non-cerebral tissues.
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