Segmentation of tumors from the brain Magnetic Resonance Images (MRIs) is very important for the analysis and right treatment. Tumors treated at early stages improve the survival time. This paper proposes an advanced method named SOMACM which is a combination of Self -Organizing -Map (SOM) and Active Contour Model (ACM) for the efficient segmentation of brain MRIs to detect tumors. ACM is an energy-based segmentation method and treats the segmentation as an optimization issue. It can model complex shapes and handles topological changes in the object boundary. The customary ACMs rely upon the intensities of the pixels and are very vulnerable to parameter tuning hence it is very difficult to segment the images of distinct pixel intensities. ACMs will evolve from the object boundary for the images consisting of Intensity Inhomogeneity (IIH). Neural Networks (NNs) are exceptionally compelling in processing the images of inhomogeneities. Furthermore, image segmentation can be done by NNs without the use of an objective function. The proposed SOMACM method works by precisely incorporating the global information extracted from the weights of the trained SOM neurons which helps in modeling complex shapes and distinct intensity distributions. It can handle images with noise, intensity similarity and IIH. The proposed segmentation technique is not sensitive to parameter tuning. The outcomes of the proposed SOMACM demonstrate the improved accuracy in the segmentation results of different types of tumor images, in contrast with the individual SOM, ACM, Fuzzy-C- Means (FCM), Particle Swarm Optimization (PSO) and Probabilistic Neural Networks (PNN) segmentation methods.

从大脑磁共振图像 (MRI) 中分割肿瘤对于分析和正确治疗非常重要。早期治疗的肿瘤可提高生存时间。本文提出了一种名为 SOMACM 的先进方法，它结合了自组织映射 (SOM) 和主动轮廓模型 (ACM)，用于脑部 MRI 的有效分割以检测肿瘤。ACM 是一种基于能量的分割方法，并将分割视为优化问题。它可以建模复杂的形状并处理对象边界中的拓扑变化。传统的 ACM 依赖于像素的强度并且非常容易受到参数调整的影响，因此很难分割不同像素强度的图像。ACM 将从对象边界演变为由强度不均匀性 (IIH) 组成的图像。神经网络 (NN) 在处理不均匀性图像方面格外引人注目。此外，图像分割可以由神经网络在不使用目标函数的情况下完成。所提出的 SOMACM 方法通过精确结合从受过训练的 SOM 神经元的权重中提取的全局信息来工作，这有助于对复杂的形状和不同的强度分布进行建模。它可以处理带有噪声、强度相似性和 IIH 的图像。所提出的分割技术对参数调整不敏感。与单独的 SOM、ACM、模糊 C 均值 (FCM)、粒子群优化 (PSO) 和概率神经网络 ( PNN）分割方法。