Abstract
Background and ObjectiveMagnetic resonance (MR) technology enables physicians to employ digital imaging as a tool to identify and analyze brain tumors as well as to differentiate between healthy and tumor tissues. Since precisely defining the tumor position is a challenging task, segmentation is necessary stage in medical image processing. Segmentation is defined as the division of a given image into non-overlapping regions with relatively similar features. Recently, various techniques have been proposed for MR image segmentation. MethodsIn this paper, a new ensemble clustering technique based on neural network algorithm (NNA) was introduced for segmentation of MRI Brain tissues, which consists of two main phases: training phase and testing phase. In the former, pixels with the same amount of light intensity were put into one group via simple linear iterative clustering (SLIC) algorithm, creating superpixels. Then, the combination of GrowCut methods and Zernik’s feature extraction method and clustering and classification ensemble learning, including multi-layer perceptron neural network algorithm (MLP), Support vector machine (SVM) and K nearest neighbor (KNN), are based on geometric features in order to detect the division of MRI Brain tissues. The improved GrowCut method, based on the geometric properties of the images, has been used to segmentation the image of the brain from MRI images. Then, with the help of Zernik’s technique, image features were extracted based on the features of the edges and the texture of the images. Finally, the ensemble learning methods such as KNN, SVM, and MLP combine together for attempted to detect MRI Brain tissues. Results and ConclusionsThe performance of the proposed method was evaluated on the IBSR20 dataset and the obtained results were compared separately with the six basic clustering methods (that together are creating the ensemble clustering technique). The results demonstrated that the proposed technique outperforms other clustering algorithms in terms of segmentation accuracy in most of the experimental cases. Therefore, by presenting experiments to diagnose MRI Brain tissues, it was observed that the proposed method detected the MR with acceptable accuracy and the combination of ensemble learning methods and GrowCut and Zernik’s feature extraction methods was successful.
An Exploratory Study of the Inputs for Ensemble Clustering Technique as a Subset Selection Problem