title: An efficient neural network based method for medical image segmentation.
abstract: The aim of this research is to propose a new neural network based method for medical image segmentation. Firstly, a modified self-organizing map (SOM) network, named moving average SOM (MA-SOM), is utilized to segment medical images. After the initial segmentation stage, a merging process is designed to connect the objects of a joint cluster together. A two-dimensional (2D) discrete wavelet transform (DWT) is used to build the input feature space of the network. The experimental results show that MA-SOM is robust to noise and it determines the input image pattern properly. The segmentation results of breast ultrasound images (BUS) demonstrate that there is a significant correlation between the tumor region selected by a physician and the tumor region segmented by our proposed method. In addition, the proposed method segments X-ray computerized tomography (CT) and magnetic resonance (MR) head images much better than the incremental supervised neural network (ISNN) and SOM-based methods.
authors.affiliation: Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
authors.forename: Nima
authors.initials: N
authors.lastname: Torbati
authors.affiliation: Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran. Electronic address: ayatollahi@iust.ac.ir.
authors.forename: Ahmad
authors.initials: A
authors.lastname: Ayatollahi
authors.affiliation: Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
authors.forename: Ali
authors.initials: A
authors.lastname: Kermani
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Artificial neural network (ANN); Computer aided diagnosis (CAD) systems; Medical image segmentation; Pattern recognition