Image segmentation plays a crucial role in various biomedical applications. In general, the segmentation of brain Magnetic Resonance (MR) images is mainly used to represent the image with several homogeneous regions instead of pixels for surgical analyzing and planning. This paper proposes a new approach for segmenting MR brain images by using pseudo-color based segmentation with Non-symmetry and Anti-packing Model with Squares (NAMS). First of all, the NAMS model is presented. The model can represent the image with sub-patterns to keep the image content and largely reduce the data redundancy. Second, the key idea is proposed that convert the original gray-scale brain MR image into a pseudo-colored image and then segment the pseudo-colored image with NAMS model. The pseudo-colored image can enhance the color contrast in different tissues in brain MR images, which can improve the precision of segmentation as well as directly visual perceptional distinction. Experimental results indicate that compared with other brain MR image segmentation methods, the proposed NAMS based pseudo-color segmentation method performs more excellent in not only segmenting precisely but also saving storage.

图像分割在各种生物医学应用中起着至关重要的作用。通常，脑磁共振（MR）图像的分割主要用于表示具有多个同质区域的图像，而不是用于外科手术分析和计划的像素。本文提出了一种基于伪彩色的非对称分割和正方形反打包模型（NAMS）分割磁共振脑图像的新方法。首先，介绍了NAMS模型。该模型可以用子模式来表示图像，以保持图像内容并大大减少数据冗余。其次，提出了将原始灰度脑部MR图像转换为伪彩色图像，然后使用NAMS模型对伪彩色图像进行分割的关键思想。伪彩色图像可以增强大脑MR图像中不同组织的颜色对比度，从而可以提高分割的精度以及直接的视觉感知区别。实验结果表明，与其他脑部MR图像分割方法相比，基于NAMS的伪彩色分割方法不仅在分割精度上更出色，而且可以节省存储空间。