Comparison of GWO-SVM and Random Forest Classifiers in a LevelSet based approach for Bladder wall segmentation and characterisation using MR images

被引：0

作者：

Trigui, Rania ^{[1
]}

Adel, Mouloud ^{[1
]}

Di Bisceglie, Mathieu ^{[2
]}

Wojak, Julien ^{[1
]}

Pinol, Jessica ^{[3
]}

Faure, Alice ^{[3
]}

Chaumoitre, Katia ^{[2
]}

机构：

[1] Aix Marseille Univ, CNRS, Cent Marseille, Inst Fresnel, Marseille, France

[2] Marseille Univ, Hosp Ctr, Paediat Surg Dept, Marseille, France

[3] Aix Marseille Univ, North Hosp, Med Imaging Serv, Marseille, France

来源：

2022 ELEVENTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA) | 2022年

关键词：

Bladder wall segmentation; Classification; Optimization; texture analysis; Magnetic Resonance Imaging;

D O I：

10.1109/IPTA54936.2022.9784127

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In order to characterize the bladder state and functioning, it is necessary to succeed the segmentation of its wall in MR images. In this context, we propose a computeraided diagnosis system based on segmentation and classification applied to the Bladder Wall (BW), as a part of spina bifida disease study. The proposed system starts with the BW extraction using an improved levelSet based algorithm. Then an optimized classification is proposed using some selected features. Obtained results proves the efficiency of the proposed system, which can be significantly helpful for radiologist avoiding the fastidious manual segmentation and providing a precise idea about the spina bifida severity

引用

页数：4

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