Patch Based Deep Local Feature Learning and Self Similarity Multi Level Clustering for Neonatal Brain Segmentation in MR Images

Abstract

The main purpose of this work is to develop a new scheme to profoundly retrieving features to perform the process of identifying a brain regions from the MR neonatal brain image. First the input MR neonatal brain image is denoised by using the Modified Fuzzy Adaptive Non Local Mean Filter (FANLMF) and then the contrast of the image is enhanced using the Adaptive Average Intensity Based Histogram Equalization (AAIHE). After pre-processing the input MR image, the next step is to retrieve the features of a similar image. To capture the features from the pre-processed image, this project offers a new technique for retrieving features called Patch Based Deep Local Feature Learning (PBDLFL). After retrieving the deep features, the next step is to divide the brain regions based on these retrieved features. To implement this process, the supervised segmentation scheme is employed. Among several supervised segmentation scheme this works employs proposed approach named Self Similarity Multi Level Clustering (SSMLC). Finally, the retrieved features are given as an input to these SSMLC approach for separating the regions of the brain. To understand the effectiveness of the proposed deep feature retrieval and proposed segmentation scheme, four performance metrics are employed namely, Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), Jaccard Index (JI) and Sensitivity (SEN). The experimental results show that the new PBDLFL and SSMLC perform better than other existing approaches.