Brain tumors are one of the most deadly diseases with a high mortality rate. The shape and size of the tumor are random during the growth process. Brain tumor segmentation is a brain tumor assisted diagnosis technology that separates different brain tumor structures such as edema and active and tumor necrosis tissues from normal brain tissue. Magnetic resonance imaging (MRI) technology has the advantages of no radiation impact on the human body, good imaging effect on structural tissues, and an ability to realize tomographic imaging of any orientation. Therefore, doctors often use MRI brain tumor images to analyze and process brain tumors. In these images, the tumor structure is only characterized by grayscale changes, and the developed images obtained by different equipment and different conditions may also be different. This makes it difficult for traditional image segmentation methods to deal well with the segmentation of brain tumor images. Considering that the traditional single-mode MRI brain tumor images contain incomplete brain tumor information, it is difficult to segment the single-mode brain tumor images to meet clinical needs. In this paper, a sparse subspace clustering (SSC) algorithm is introduced to process the diagnosis of multimodal MRI brain tumor images. In the absence of added noise, the proposed algorithm has better advantages than traditional methods. Compared with the top 15 in the Brats 2015 competition, the accuracy is not much different, being basically stable between 10 and 15. In order to verify the noise resistance of the proposed algorithm, this paper adds 5%, 10%, 15%, and 20% Gaussian noise to the test image. Experimental results show that the proposed algorithm has better noise immunity than a comparable algorithm.

中文翻译：

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使用稀疏子空间聚类算法的多模式MRI脑肿瘤图像分割。

脑肿瘤是死亡率最高的最致命的疾病之一。在生长过程中，肿瘤的形状和大小是随机的。脑肿瘤分割是一种脑肿瘤辅助诊断技术，可将不同的脑肿瘤结构（例如水肿，活动组织和肿瘤坏死组织）与正常脑组织分开。磁共振成像（MRI）技术的优点是不会对人体造成辐射影响，对结构组织具有良好的成像效果，并且能够实现任何方向的层析成像。因此，医生经常使用MRI脑肿瘤图像来分析和处理脑肿瘤。在这些图像中，肿瘤结构仅以灰度变化为特征，并且通过不同设备和不同条件获得的显影图像也可能不同。这使得传统的图像分割方法难以很好地处理脑肿瘤图像的分割。考虑到传统的单模式MRI脑肿瘤图像包含不完整的脑肿瘤信息，因此很难对单模式脑肿瘤图像进行分割以满足临床需求。本文介绍了一种稀疏子空间聚类（SSC）算法来处理多模式MRI脑肿瘤图像的诊断。在没有增加噪声的情况下，该算法比传统方法具有更好的优势。与Brats 2015竞赛中的前15名相比，准确性没有太大差别，在10至15之间基本稳定。为验证所提出算法的抗噪性，本文将5％，10％，15％，测试图像的高斯噪声为20％。