Segmentation of ultrasound B-mode images with intensity inhomogeneity correction.
Displayed ultrasound (US) B-mode images often exhibit tissue intensity inhomogeneities dominated by nonuniform beam attenuation within the body. This is a major problem for intensity-based, automatic segmentation of video-intensity images because conventional threshold-based or intensity-statistic-based approaches do not work well in the presence of such image distortions. Time gain compensation (TGC) is typically used in standard US machines in an attempt to overcome this. However this compensation method is position-dependent which means that different tissues in the same TGC time-range (or corresponding depth range) will be, incorrectly, compensated by the same amount. Compensation should really be tissue-type dependent but automating this step is difficult. The main contribution of this paper is to develop a method for simultaneous estimation of video-intensity inhomogeities and segmentation of US image tissue regions. The method uses a combination of the maximum a posteriori (MAP) and Markov random field (MRF) methods to estimate the US image distortion field assuming it follows a multiplicative model while at the same time labeling image regions based on the corrected intensity statistics. The MAP step is used to estimate the intensity model parameters while the MRF step provides a novel way of incorporating the distributions of image tissue classes as a spatial smoothness constraint. We explain how this multiplicative model can be related to the ultrasonic physics of image formation to justify our approach. Experiments are presented on synthetic images and a gelatin phantom to evaluate quantitatively the accuracy of the method. We also discuss qualitatively the application of the method to clinical breast and cardiac US images. Limitations of the method and potential clinical applications are outlined in the conclusion.
Xiao
Guofang
G
Medical Vision Laboratory, Department of Engineering Science, University of Oxford, UK.
Brady
Michael
M
Noble
J Alison
JA
Zhang
Yongyue
Y
D000465:Algorithms; D016477:Artifacts; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D019560:Echocardiography, Three-Dimensional / Q000379:methods*; D005240:Feasibility Studies; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods* / Q000706:statistics & numerical data; D008390:Markov Chains; D015233:Models, Statistical; D019047:Phantoms, Imaging; D012680:Sensitivity and Specificity; D013269:Stochastic Processes; D018616:Ultrasonography, Doppler, Duplex / Q000379:methods*