背景:在图像处理中,由于形状不同,图像分割是一项更具挑战性的任务,地点,图像强度,等。脑肿瘤是世界上最常见的疾病之一。所以,脑肿瘤的检测和分割在医学领域具有重要意义。
目的:这项工作的主要目标是使用所提出的方法将脑部MRI图像分割为肿瘤和非肿瘤段或像素。
方法:在这项工作中,我们首先从BraTS2020数据库中选择MRI医学图像,并将其转移到对比增强阶段.然后,我们应用阈值来增强对比度,以增强血管等结构的可见性,肿瘤,或异常。在对比度增强过程之后,将图像转化为图像去噪阶段。在这个阶段,四阶偏微分方程用于图像去噪。经过图像去噪处理后,这些图像被传递到分割阶段。在这个分割阶段,我们使用大象羊群算法进行质心优化,然后将多核模糊c均值聚类应用于图像分割。
结果:峰值信噪比,均方误差,灵敏度,特异性,和准确性被用来评估所提出的方法的性能。根据调查结果,提出的策略比传统方法产生了更好的结果。
结论:据报道,我们提出的方法是一种比现有技术更有效的技术。
BACKGROUND: In image processing, image segmentation is a more challenging task due to different shapes, locations, image intensities, etc. Brain tumors are one of the most common diseases in the world. So, the detection and segmentation of brain tumors are important in the medical field.
OBJECTIVE: The primary goal of this work is to use the proposed methodology to segment brain MRI images into tumor and non-tumor segments or pixels.
METHODS: In this work, we first selected the MRI medical images from the BraTS2020 database and transferred them to the contrast enhancement phase. Then, we applied thresholding for contrast enhancement to enhance the visibility of structures like blood arteries, tumors, or abnormalities. After the contrast enhancement process, the images were transformed into the image denoising phase. In this phase, a fourth-order partial differential equation was used for image denoising. After the image denoising process, these images were passed on to the segmentation phase. In this segmentation phase, we used an elephant herding algorithm for centroid optimization and then applied the multi-kernel fuzzy c-means clustering for image segmentation.
RESULTS: Peak signal-to-noise ratio, mean square error, sensitivity, specificity, and accuracy were used to assess the performance of the proposed methods. According to the findings, the proposed strategy produced better outcomes than the conventional methods.
CONCLUSIONS: Our proposed methodology was reported to be a more effective technique than existing techniques.