Medical Decision-Making System of Ultrasound Carotid Artery Intima–Media Thickness Using Neural Networks

被引：0
作者：
N. Santhiyakumari
P. Rajendran
M. Madheswaran
机构：
[1] K.S.R. College of Technology,Department of ECE
[2] K.S.R. College of Technology,Department of CSE
[3] Muthayammal Engineering College,Center for Advanced Research, Department of Electronics and Communication Engineering
来源：
Journal of Digital Imaging | 2011年 / 24卷
关键词：
US carotid artery image analysis; Contour extraction; Multilayer back propagation network; Neural network classifier; Carotid artery classification; Medical decision-making system; Digital image processing; Image segmentation; Decision support techniques; Neural networks; Carotid artery;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The objective of this work is to develop and implement a medical decision-making system for an automated diagnosis and classification of ultrasound carotid artery images. The proposed method categorizes the subjects into normal, cerebrovascular, and cardiovascular diseases. Two contours are extracted for each and every preprocessed ultrasound carotid artery image. Two types of contour extraction techniques and multilayer back propagation network (MBPN) system have been developed for classifying carotid artery categories. The results obtained show that MBPN system provides higher classification efficiency, with minimum training and testing time. The outputs of decision support system are validated with medical expert to measure the actual efficiency. MBPN system with contour extraction algorithms and preprocessing scheme helps in developing medical decision-making system for ultrasound carotid artery images. It can be used as secondary observer in clinical decision making.
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页码：1112 / 1125
页数：13
相关论文
共 112 条
[1]  
Troccaz J(2006)Medical image computing and computer-aided medical interventions applied to soft tissues: Work in progress in urology Proc IEEE 94 1665-1677
[2]  
Baumann M(2003)Road maps for advancement of radiologic computer-aided detection in the 21st century Radiology 229 11-13
[3]  
Berkelman P(2001)Computer-aided diagnosis in medical imaging IEEE Trans Med Imag 20 1205-1208
[4]  
Cinquin P(2005)Current status and future potential of computer-aided diagnosis in medical imaging Br J Radiol 78 S3-S19
[5]  
Daanen V(2002)Computer-aided detection and diagnosis at the start of the third millennium J Digit Imaging 15 59-68
[6]  
Leroy A(1963)Computer diagnosis of primary bone tumor Radiology 80 273-275
[7]  
Marchal M(2008)A Hybrid Fuzzy-Neural system for Computer-Aided Diagnosis of Ultrasound Kidney Images Using Prominent Features J Med Syst 32 65-83
[8]  
Payan Y(1995)Computer-aided detection of clustered microcalcifications on digital mammograms Med Biol Eng Comput 33 174-178
[9]  
Promayon E(1998)Automated computerized classification of malignant and benign mass lesions on digitized mammograms Acad Radiol 15 155-168
[10]  
Voros S(2002)Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images Med Phys 29 701-708
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