ABSTRACT Tumours, one of the pathologies of the brain, are diverse in shape and appearance and overlap with the normal brain tissues making accurate automatic segmentation a challenge. This work proposes a semi- automatic, unsupervised method for brain tumour segmentation, using magnetic resonance images in a simple Bayesian framework. Pixel is classified as a tumour class by taking into account the knowledge of different brain tissue classes, grey level of pixels and their neighbourhood. For the Bayesian frame work, the likelihood of the different brain tissue classes is assumed as Gaussian and Gaussian density weights of the pixel neighbourhood serve as the prior information for accurate tumour segmentation. Experiments conducted on the publically available BRATS database result in an overall accuracy of 98% for tumour core and 96% for oedema.

中文翻译：

---

使用简单贝叶斯框架的半自动无监督 MR 脑肿瘤分割

摘要 肿瘤是大脑的一种病理，其形状和外观多种多样，并与正常脑组织重叠，这使得准确的自动分割成为一项挑战。这项工作提出了一种半自动、无监督的脑肿瘤分割方法，在简单的贝叶斯框架中使用磁共振图像。通过考虑不同脑组织类的知识、像素的灰度级及其邻域，将像素归类为肿瘤类。对于贝叶斯框架工作，假设不同脑组织类别的可能性为像素邻域的高斯和高斯密度权重作为准确肿瘤分割的先验信息。在公开可用的 BRATS 数据库上进行的实验导致肿瘤核心的总体准确率为 98%，水肿的总体准确率为 96%。