Segmentation Method of Deterministic Feature Clustering for Identification of Brain Tumor Using MRI

Abstract

The features play an important role for identification of the region of interest. Different kind of features exist, it is also essential to identify the accurate class of the features, challenging dataset like MICCAI BraTs brain tumor contains many tumor images. Features are helpful to detect the region of tumor with some of their characteristic. But due to many images and their information, the issue of data complexity is raised. because the data was found to be complex. Thus, due to the complexity, higher dimension features are reduced to low dimension features. Hence, there is a need for improved feature selection method. Furthermore, it is also essential to enhance the method for the SOM Map for the selection of deterministic feature after the extraction. The goal of the work is not only to select the accurate feature of tumor but also to segment the tumor intensity with the confidence element. The objective under umbrella of this work is to improve the feature selection method by using confidence element of interest through the determination of the best feature using the SOFM with FCM. The method works with the selection of the best features with higher accuracy. Those higher accurate Features are called the deterministic Features. These deterministic features are selected through improved weighted SOM. This improved SOM is further combined with FCM to cluster the Confidence element. Evaluation is made with comparison to ground truth reality images; Results show; DOI is 0.94, JI is 0.91, MSE is 0.058db and PSNR is 17.94db; MSE with small number highlights the performance of method. It can be compared with the state of the art or it can be compared with benchmark studies. Testing parameters from benchmark studies were JI, DOI, MSE and PSNR: JI accuracy value was 31.5%, DOI accuracy value was 47.3%, MSE value was 2.5dB and PSNR value was 40dB.A better region of interest is proposed method to determine the confidence element. The average accuracy over the dataset is determined in form of confidence element (ROI), overlap is for complex cases and average value is 94 percent.

Keywords

• SOFM
• feature
• clustering
• dice over index
• Jaccard index
• feature selection