A novel myocardium segmentation approach based on neutrosophic active contour model.
OBJECTIVE
Automatic delineation of the myocardium in echocardiography can assist radiologists to diagnosis heart problems. However, it is still challenging to distinguish myocardium from other tissue due to a low signal-to-noise ratio, low contrast, vague boundary, and speckle noise. The purpose of this study is to automatically detect myocardium region in left ventricle myocardial contrast echocardiography (LVMCE) images to help radiologists' diagnosis and further measurement on infarction size.


METHODS
The LVMCE image is firstly mapped into neutrosophic similarity (NS) domain using the intensity and homogeneity features. Then, a neutrosophic active contour model (NACM) is proposed and the energy function is defined by the NS values. Finally, the ventricle is detected using the curve evolving results. The ventricle's boundary is identified as the endocardium. To speed up the evolution procedure and increase the detection accuracy, a clustering algorithm is employed to obtain the initial ventricle region. The curve evolution procedure in NACM is utilized again to obtain the epicardium, where the initial contour uses the detected endocardium and the anatomy knowledge on the thickness of the myocardium.


RESULTS
Echocardiographic studies are performed on 10 male Sprague-Dawley rats using a Vivid 7 system including 5 normal cases and 5 rats with myocardial infarction. The myocardium boundaries manually outlined by an experienced radiologist are used as the reference standard for the performance evaluation. Two metrics, Hdist and AvgDist, are employed to evaluate the detection results. The NACM method was compared with those from the eliminated particle swarm optimization (EPSO) and active contour model without edges (ACMWE) methods. The mean and standard deviation of the Hdist and AvgDist on endocardium are 6.83 ± 1.12mm and 0.79 ± 0.28mm using EPSO method, 7.12 ± 0.98mm and 0.82 ± 0.32mm using ACMWE method, and 4.55 ± 0.9mm and 0.58 ± 0.18mm using NACM method, respectively. The improvement on epicardium is much more significant, and two metrics are decreased from 7.45 ± 1.24mm, and 1.47 ± 0.34mm using EPSO method, and 8.21±0.43mm, and 1.73±0.47mm using ACMWE method, to 4.94 ± 0.82mm, and 0.84 ± 0.22mm using NACM method, respectively.


CONCLUSIONS
The proposed method can automatically detect myocardium accurately, and is helpful for clinical therapeutics to measure myocardial perfusion and infarct size.
Department of Computer Science, University of Illinois at Springfield, Springfield, IL USA. Electronic address: yguo56@uis.edu.
Yanhui
Y
Guo
Department of Ultrasound, Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Guo-Qing
GQ
Du
Department of Cardiology, First Affiliated Hospital of Harbin Medical University, Harbin, China.
Jing-Yi
JY
Xue
Oracle Corporation, Westminster, CO, USA.
Rong
R
Xia
Department of Ultrasound, Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Yu-Hang
YH
Wang
D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D004452:Echocardiography / Q000379:methods*; D004699:Endocardium / Q000473:pathology*; D006352:Heart Ventricles / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D009203:Myocardial Infarction; D009206:Myocardium / Q000473:pathology*; D010496:Pericardium / Q000473:pathology; D051381:Rats; D017207:Rats, Sprague-Dawley; D059629:Signal-To-Noise Ratio
Active contour model; Myocardial contrast echocardiography (MCE); Myocardium detection; Neutrosophic similarity