Contour segmentation in 2D ultrasound medical images with particle filtering

Object segmentation in medical images is an actively investigated research area. Segmentation techniques are a valuable tool in medical diagnostics for cancer tumours and cysts, for planning surgery operations and other medical treatment. In this paper, a Monte Carlo algorithm for extracting lesion contours in ultrasound medical images is proposed. An efficient multiple model particle filter for progressive contour growing (tracking) from a starting point is developed, accounting for convex, non-circular forms of delineated contour areas. The driving idea of the proposed particle filter consists in the incorporation of different image intensity inside and outside the contour into the filter likelihood function. The filter employs image intensity gradients as measurements and requires information about four manually selected points: a seed point, a starting point, arbitrarily selected on the contour, and two additional points, bounding the measurement formation area around the contour. The filter performance is studied by segmenting contours from a number of real and simulated ultrasound medical images. Accurate contour segmentation is achieved with the proposed approach in ultrasound images with a high level of speckle noise.

[1]  Zvi Friedman,et al.  The combined effect of spatial compounding and nonlinear filtering on the speckle reduction in ultrasound images. , 2006, Ultrasonics.

[2]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[3]  Marcos Martín-Fernández,et al.  An approach for contour detection of human kidneys from ultrasound images using Markov random fields and active contours , 2005, Medical Image Anal..

[4]  Jérôme Tonetti,et al.  An Information Fusion Method for the Automatic Delineation of the Bone-Soft Tissues Interface in Ultrasound Images , 2004, ECCV Workshops CVAMIA and MMBIA.

[5]  Hans Driessen,et al.  Point estimation for jump Markov systems: Various MAP estimators , 2009, 2009 12th International Conference on Information Fusion.

[6]  Anil Kokaram,et al.  ORIENTED PARTICLE SPRAY: PROBABILISTIC CONTOUR TRACING WITH DIRECTIONAL INFORMATION , 2004 .

[7]  Nikos Paragios,et al.  Particle Filters, a Quasi-Monte Carlo Solution for Segmentation of Coronaries , 2005, MICCAI.

[8]  Mark S. Nixon,et al.  Medical Image Segmentation by Water Flow , 2007 .

[9]  Lyudmila Mihaylova,et al.  Contour extraction from ultrasound images viewed as a tracking problem , 2009, 2009 12th International Conference on Information Fusion.

[10]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[11]  Patrick Pérez,et al.  JetStream: probabilistic contour extraction with particles , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[12]  Donka S. Angelova,et al.  Contour Determination in Ultrasound Medical Images Using Interacting Multiple Model Probabilistic Data Association Filter , 2006, Numerical Methods and Applications.

[13]  Farhang Sahba,et al.  A coarse-to-fine approach to prostate boundary segmentation in ultrasound images , 2005, Biomedical engineering online.

[14]  C.L. de Korte,et al.  Fully automated endoluminal contour detection in intracoronary ultrasound images: a pre-processing for intravascular elastography , 2002, 2002 IEEE Ultrasonics Symposium, 2002. Proceedings..

[15]  Hans Driessen,et al.  Particle based MAP state estimation: A comparison , 2009, 2009 12th International Conference on Information Fusion.

[16]  Max Mignotte,et al.  AN UNSUPERVISED MULTISCALE APPROACH FOR THE DYNAMIC CONTOUR-BASED BOUNDARY DETECTION ISSUE IN ULTRASOUND IMAGERY , 2000 .

[17]  J. Alison Noble,et al.  Ultrasound image segmentation: a survey , 2006, IEEE Transactions on Medical Imaging.

[18]  Ruey-Feng Chang,et al.  Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors , 2004, Breast Cancer Research and Treatment.

[19]  Jörg Weule,et al.  Non-Linear Gaussian Filters Performing Edge Preserving Diffusion , 1995, DAGM-Symposium.

[20]  John W. Fisher,et al.  MCMC Curve Sampling for Image Segmentation , 2007, MICCAI.

[21]  Edgar Arce Santana,et al.  Hidden Markov Measure Field Models for Image Segmentation , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Roman Goldenberg,et al.  Fast Geodesic Active Contours , 1999, Scale-Space.

[23]  G. Hamarneh,et al.  Combining snakes and active shape models for segmenting the human left ventricle in echocardiographic images , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[24]  Purang Abolmaesumi,et al.  An interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images , 2004, IEEE Transactions on Medical Imaging.

[25]  M Mignotte,et al.  A multiscale optimization approach for the dynamic contour-based boundary detection issue. , 2001, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[26]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[27]  Schmutzer Gj.  Effect of the overall electrical resistance of the tissues on the work of an implanted cardiac pacemaker , 1969 .

[28]  Pierrick Coupé,et al.  3D Wavelet Subbands Mixing for Image Denoising , 2008, Int. J. Biomed. Imaging.

[29]  C. Rother,et al.  Bayesian Methods in Graphics , 2003 .

[30]  Wilfrid Lefer,et al.  Semi-Automatic Integrated Segmentation Approaches and Contour Extraction Applied to Computed Tomography Scan Images , 2008, Int. J. Biomed. Imaging.

[31]  Meritxell Bach Cuadra,et al.  A multidimensional segmentation evaluation for medical image data , 2009, Comput. Methods Programs Biomed..

[32]  Purang Abolmaesumi,et al.  Segmentation of prostate contours from ultrasound images , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[33]  Geir Storvik,et al.  A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[35]  Habib Zaidi.  Medical image segmentation: Quo Vadis , 2006, Comput. Methods Programs Biomed..

[36]  Yuanyuan Jia,et al.  Segmentation in noisy medical images using PCA model based particle filtering , 2008, SPIE Medical Imaging.

[37]  William M. Wells,et al.  An EM algorithm for shape classification based on level sets , 2005, Medical Image Anal..

[38]  Baoxin Li,et al.  Adaptive Rao–Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance , 2007, IEEE Transactions on Image Processing.

[39]  Purang Abolmaesumi,et al.  An estimation/correction algorithm for detecting bone edges in CT images , 2005, IEEE Transactions on Medical Imaging.

[40]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.