Fully automatic segmentation of ultrasound common carotid artery images based on machine learning

Abstract Atherosclerosis is responsible for a large proportion of cardiovascular diseases (CVD), which are the leading cause of death in the world. The atherosclerotic process is a complex degenerative condition mainly affecting the medium- and large-size arteries, which begins in childhood and may remain unnoticed during decades. It causes thickening and the reduction of elasticity in the blood vessels. An early diagnosis of this condition is crucial to prevent patients from suffering more serious pathologies (heart attacks and strokes). The evaluation of the Intima-Media Thickness (IMT) of the Common Carotid Artery (CCA) in B-mode ultrasound images is considered the most useful tool for the investigation of preclinical atherosclerosis. Usually, it is manually measured by the radiologists. This paper proposes a fully automatic segmentation technique based on Machine Learning and Statistical Pattern Recognition to measure IMT from ultrasound CCA images. The pixels are classified by means of artificial neural networks to identify the IMT boundaries. Moreover, the concepts of Auto-Encoders (AE) and Deep Learning have been included in the classification strategy. The suggested approach is tested on a set of 55 longitudinal ultrasound images of the CCA by comparing the automatic segmentation with four manual tracings.

[1]  A. Pietrosanto,et al.  An automatic measurement system for the evaluation of carotid intima-media thickness , 2000, Proceedings of the 17th IEEE Instrumentation and Measurement Technology Conference [Cat. No. 00CH37066].

[2]  B. Norrving,et al.  Global atlas on cardiovascular disease prevention and control. , 2011 .

[3]  Hans Burkhardt,et al.  Using snakes to detect the intimal and adventitial layers of the common carotid artery wall in sonographic images , 2002, Comput. Methods Programs Biomed..

[4]  Sergio Shiguemi Furuie,et al.  Automatic measurement of carotid diameter and wall thickness in ultrasound images , 2002, Computers in Cardiology.

[5]  Jasjit S. Suri,et al.  A state of the art review on intima-media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound , 2010, Comput. Methods Programs Biomed..

[6]  Jean Meunier,et al.  Segmentation in Ultrasonic B-Mode Images of Healthy Carotid Arteries Using Mixtures of Nakagami Distributions and Stochastic Optimization , 2009, IEEE Transactions on Medical Imaging.

[7]  Emmanouil G. Sifakis,et al.  Using the Hough transform to segment ultrasound images of longitudinal and transverse sections of the carotid artery. , 2007, Ultrasound in medicine & biology.

[8]  Quan Liang,et al.  A dynamic programming procedure for automated ultrasonic measurement of the carotid artery , 1994, Computers in Cardiology 1994.

[9]  Vicente Gilsanz,et al.  Reproducibility of carotid intima-media thickness measurements in young adults. , 2008, Radiology.

[10]  F. Faita,et al.  Real‐time Measurement System for Evaluation of the Carotid Intima‐Media Thickness With a Robust Edge Operator , 2008, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[11]  R H Selzer,et al.  Improved common carotid elasticity and intima-media thickness measurements from computer analysis of sequential ultrasound frames. , 2001, Atherosclerosis.

[12]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[13]  E. Vicaut,et al.  Mannheim Carotid Intima-Media Thickness Consensus (2004–2006) , 2006, Cerebrovascular Diseases.

[14]  Bo Wang,et al.  Detection of Intima-Media Layer of Common Carotid Artery with Dynamic Programming Based Active Contour Model , 2008, 2008 Chinese Conference on Pattern Recognition.

[15]  Myoung-Hee Kim,et al.  Boundary detection in carotid ultrasound images using dynamic programming and a directional Haar-like filter , 2010, Comput. Biol. Medicine.

[16]  Juan Morales-Sánchez,et al.  Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks , 2013, Medical & Biological Engineering & Computing.

[17]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[18]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[19]  J. Polak,et al.  Intima-media thickness: a tool for atherosclerosis imaging and event prediction. , 2002, The American journal of cardiology.

[20]  Rafael Verdú,et al.  Segmentation of the Common Carotid Artery Walls Based on a Frequency Implementation of Active Contours , 2013, Journal of Digital Imaging.

[21]  U. Rajendra Acharya,et al.  Constrained snake vs. conventional snake for carotid ultrasound automated IMT measurements on multi-center data sets. , 2012, Ultrasonics.

[22]  Dipankar Das,et al.  Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining , 2013, IEEE Intelligent Systems.

[23]  Diederick E Grobbee,et al.  Carotid Intima-Media Thickness Measurements in Intervention Studies: Design Options, Progression Rates, and Sample Size Considerations: A Point of View , 2003, Stroke.

[24]  N. Santhiyakumari,et al.  Non-invasive evaluation of carotid artery wall thickness using improved dynamic programming technique , 2008, Signal Image Video Process..

[25]  Raymond Chan,et al.  Anisotropic edge-preserving smoothing in carotid B-mode ultrasound for improved segmentation and intima-media thickness (IMT) measurement , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[26]  Filippo Molinari,et al.  Intima-media thickness: setting a standard for a completely automated method of ultrasound measurement , 2010, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[27]  Yuan Zhou,et al.  Ultrasound intima-media segmentation using Hough transform and dual snake model , 2012, Comput. Medical Imaging Graph..

[28]  Tomas Gustavsson,et al.  A multiscale dynamic programming procedure for boundary detection in ultrasonic artery images , 2000, IEEE Transactions on Medical Imaging.

[29]  Christos P. Loizou,et al.  Snakes based segmentation of the common carotid artery intima media , 2007, Medical & Biological Engineering & Computing.

[30]  Christos P. Loizou,et al.  Segmentation of the Common Carotid Intima-Media Complex in Ultrasound Images Using Active Contours , 2012, IEEE Transactions on Biomedical Engineering.

[31]  Paul F. Whelan,et al.  An automatic 2D CAD algorithm for the segmentation of the IMT in ultrasound carotid artery images , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Michele Ceccarelli,et al.  An Active Contour Approach To Automatic Detection Of The Intima-Media Thickness , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[33]  Jasjit S. Suri,et al.  Characterization of a Completely User-Independent Algorithm for Carotid Artery Segmentation in 2-D Ultrasound Images , 2007, IEEE Transactions on Instrumentation and Measurement.

[34]  Liexiang Fan,et al.  A semiautomated ultrasound border detection program that facilitates clinical measurement of ultrasound carotid intima-media thickness. , 2005, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[35]  Xiaoyi Jiang,et al.  Detections of Arterial Wall in Sonographic Artery Images Using Dual Dynamic Programming , 2008, IEEE Transactions on Information Technology in Biomedicine.

[36]  Martin Fodslette Meiller.  A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1993 .

[37]  Aurélio J. C. Campilho,et al.  Segmentation of the carotid intima-media region in B-mode ultrasound images , 2010, Image Vis. Comput..

[38]  T Gustavsson,et al.  A new automated computerized analyzing system simplifies readings and reduces the variability in ultrasound measurement of intima-media thickness. , 1997, Stroke.

[39]  E. Vicaut,et al.  Mannheim Carotid Intima-Media Thickness and Plaque Consensus (2004–2006–2011) , 2012, Cerebrovascular Diseases.