Research on ARKFCM Algorithm Based on Membership Constraint and Bias Field Correction in Neonatal HIE Image Segmentation Method

Chao Huang,Jihua Wang,Suzanne M Shontz

doi:10.1155/2021/4683609

Abstract

First, this paper presents the algorithm of adaptively regularized kernel-based fuzzy C-means based on membership constraint (G-ARKFCM). Under the idea of competitive learning based on penalizing opponents, a new membership constraint function penalty item is introduced for each sample point in the segmented image, so that the ARKFCM algorithm is no longer limited to the fuzzy index m = 2. Secondly, the multiplicative intrinsic component optimization (MICO) is introduced into G-ARKFCM to obtain the GM-ARKFCM algorithm, which can correct the bias field when segmenting neonatal HIE images. Compared with other algorithms, the GM-ARKFCM algorithm has better segmentation quality and robustness. The GM-ARKFCM algorithm can more completely segment the neonatal ventricles and surrounding white matter and can retain more information of the original image.

Highlights

In the field of medical images and image analysis, image segmentation is one of the most important techniques
We introduce the penalty term of the algorithm membership constraint function into the adaptively regularized kernel-based fuzzy C-means (ARKFCM) algorithm to form a new objective function: Nc
In order to verify the performance of the GM-ARKFCM algorithm, the segmentation experiment of the synthetic brain MR image is analyzed. e synthetic brain MR image comes from the simulated brain database (SBD) [28], which contains two kinds of simulated brain MR data and provides the ground truth image for reference, from which we select the normal simulated brain MR data

Summary

Chao Huang and Jihua Wang

Received 1 August 2020; Revised 2 January 2021; Accepted 1 March 2021; Published 21 May 2021. This paper presents the algorithm of adaptively regularized kernel-based fuzzy C-means based on membership constraint (GARKFCM). Under the idea of competitive learning based on penalizing opponents, a new membership constraint function penalty item is introduced for each sample point in the segmented image, so that the ARKFCM algorithm is no longer limited to the fuzzy index m 2. The multiplicative intrinsic component optimization (MICO) is introduced into G-ARKFCM to obtain the GM-ARKFCM algorithm, which can correct the bias field when segmenting neonatal HIE images. The GM-ARKFCM algorithm has better segmentation quality and robustness. E GM-ARKFCM algorithm can more completely segment the neonatal ventricles and surrounding white matter and can retain more information of the original image The GM-ARKFCM algorithm has better segmentation quality and robustness. e GM-ARKFCM algorithm can more completely segment the neonatal ventricles and surrounding white matter and can retain more information of the original image

Introduction

Experimental Study of Synthetic Brain MR Images

Image Quality

Algorithm MICO ARKFCM

Algorithm Accuracy