The Automatic Brain Tumor Segmentation Based on MRI Using Optimal Morphology Thresholding Methods

Abstract

This article presents the segmentation of the brain tumors in the MRI images by using the optimal morphology thresholding methods. The number of patients with brain diseases is increased. Therefore, the needs for MRI are increased. Accordingly, the accurate and quick segmentation and identification methods for the brain tumors are really necessary. These also include planning and diagnosis tools for the automatic segmentation of the brain tumors in the MRI images. The optimal morphology thresholding methods are new methods that can automatically solve problems and diagnose the diseases. These consist of the RGB to Grayscale conversion process, the image quality improvement process and the optimum thresholding process, respectively. By testing the methods with the MRI images, it was found that the proposed methods could automatically segment the brain tumors in the MRI images with the shapes similar to that from the public databases. The highest absolute error was 3.96%, and the accuracy was 98.00% as compared to the other methods.

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“…Brain tumor segmentation in MRI images using the optimal morphology thresholding methods proposed by Pongsatitpat et al [25] The optimal morphology thresholding methods are novel methods for solving problems and diagnosing diseases. These are the RGB to Grayscale conversion, the image quality improvement, and the optimum thresholding processes.…”