Tumor Segmentation by a Self-Organizing-Map based Active Contour Model (SOMACM) from the Brain MRIs

摘要

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.