|Table of Contents|

Graph-cut Algorithm for Bone Boundary Enhancement Filtering(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2023年04期
Page:
91-102
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Graph-cut Algorithm for Bone Boundary Enhancement Filtering
Author(s):
Shi Zhiliang1; Fan Weinan1; Gan Zibo1; Yuan Qiong2
(1.School of Mechanical and Electrical Engineering,Wuhan University of Technology,Wuhan 430070,China)
(2.College of Computer,Hubei University of Education,Wuhan 430205,China)
Keywords:
bone tissue;  automatic segmentation;  graph-cuts;  bone boundary enhancement filtering;  Hessian matrix
PACS:
TP391.41
DOI:
10.3969/j.issn.1001-4616.2023.04.013
Abstract:
Accurate bone tissue segmentation is a necessary step to assist orthopedic disease diagnosis and surgical planning. Automatic segmentation of bone tissue has always been a difficult problem in medical image segmentation due to blurred bone boundaries,low contrast,and narrow bone joint interval. The traditional graph-cut algorithm is only based on intensity feature of the image,which not only requires manual setting seed points,but also has some problems such as unclear bone boundary at the joint. Therefore,a new image segmentation algorithm is proposed by combining bone boundary enhancement filtering,morphological theory and graph cut algorithm. Taking the graph-cut algorithm as the framework,bone boundary enhancement filtering is designed based on the Hessian matrix,and the filtering result is converted into a constraint term and added to the energy function of the graph-cut,and the graph-cut model is solved to obtain the preliminary segmentation results. The preliminary result is subjected to a post-processing method of first eroding and then simple graph-cut. The corrosion image is used to replace the manual input to initialize the graph-cut model to realize the automatic separation of adjacent bone tissue. Algorithm validation was performed using an internal femur dataset,the Cancer Image Archive(TCIA)public dataset. The experimental results show that compared with threshold segmentation,region growth,and traditional graph-cut,the algorithm achieves better results in Dice,accuracy,F1 score and other indicators,and can be used as a reliable basis for clinical diagnosis.

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Last Update: 2023-12-15