A Comparative Study for Brain Tumor Detection Using Segmentation Based on Soft Computing and Thresholding Methods

Gulhan Ustabas Kaya, Tugba Ozge Onur


Early diagnosis of any tumor in the brain is vital because the brain is the most important organ for human life. In medical imaging, while analyzing internal structures magnetic resonance imaging (MRI) is often preferred because of making it easier to detect the desired regions. Brain tumor segmentation in MRI has become a popular area of research in image segmentation which is one of the main approaches of digital image processing in recent years. Segmentation with thresholding is a simple approach that can be used in brain tumor detection, however, for some cases, all tumors cannot be detected successfully by these techniques.

In this study, we propose the usage of artificial neural networks (ANN) topology based on Canny edge detection (CED) and the thresholding process to detect and segment the brain tumors in early diagnosis. The comparison of ANN with traditional segmentation methods were also revealed. We performed the comparison between proposed ANN, iterative, and Otsu’s thresholding segmentation methods. The results demonstrate that the proposed ANN topology with CED is a very efficient way to detect the tumors in MRI brain images and can be used instead of thresholding techniques. The performance of tumor detection is conducted with high performance (98%, 96%, 97%, and 96% for the tumor images of the brain, metastasis head scan, benign- malignant and glioma brain, respectively) by using one or two images instead of a very large database. The results confirm the observation that the ANN has better performance for processing MRI images because of its simple learning structure.  

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