Ultrasound Image Processing, The New Method And Its Application In Prenatal Diagnosis

The ultrasonic diagnosis is one of the irreplaceable imaging diagnostic techniques in the modern clinical medicine, due to its merits of noninvasiveness, low cost and real-time imaging. Because of the characteristic of the coherent imaging process, ultrasound images suffer from a low signal-to-noise ratio that brings unfavorable effects into the quantitative analysis and diagnosis. In the pratical use, the ultrasound diagnosis confronts several shortcomings, such as the diagnosis accuracy strongly depending on the sonographer's experience and methods of the image feature extraction and analysis falling behind the developement of imaging techniques. It is meaningful to introduce automatic feature exaction and analysis techniqes into the ultrasound image processing. However, two crucial problems, the image denoising and image segmentation should be firstly solved.As a major method to evaluate the fetal intrauterine growth, the obstetric ultrasound diagnosis plays an important role in decreasing the mortality of expectant mothers and fetuses, and thus in ensuring healthy pregnancy and scientific nurture. In the clinical obstetric examination, the estimated fetal weight (EFW) derived from ultrasonic measurements of fetal parts is one of important indices to estimate the fetal outcome. However, the accuracy of the clinical fetal weight estimation is unsatisfactory. Large random errors in manual measurements of fetal parts and system errors of the regression formula are two main causes of the inaccurate EFW.This dissertation focuses on these crucial problems in the ultrasound image analysis—image denoising and image segmentation, aims at the automated image analysis and computer aided diagnosis in the obstetric ultrasound. The studies have been carried out in following aspects.For the image denoising, we focus on the anisotropic diffusion technique. A kernel anisotropic diffusion model is proposed for the robust noise reduction and edge detection under the strong noisy background. To improve the flexibility and stability of the speckle reducing anistropic diffusion (SRAD) filter, a mixture distribution decomposion method is designed to estimate the diffusion parameters adaptively by using the EM algorithm. By making use of the good contour capturing ability of the nonsubsampled Contourlet transform, a multiscale anisotropic diffusion method called as the contourlet transform based complex diffusion is proposed for the ultrasonic speckle reducition.To alleviate the effects of speckle noise and intensity inhomogeneity in the ultrasound image segmentation, a novel method is proposed, which integrates the speckle noise reduction and the intensity inhomogeneity compensation into a two-dimensional homogenized fuzzy C-means (2DHFCM) framework. The 2DHFCM is applicable to the segmentation of cavities and fluid-filled tissues with a complex geometrical shape. By extending the principle of 2DHFCM, a fast two dimensional fuzzy C-means algorithm (Fast₂DFCM) is utilized into the segmentation of natural images corrupted by the implusive noise, and a multi-information based fuzzy C-means algorithm is utilized into the segmentation of molecular images corrupted by Gaussian noise and intensity inhomogeneity.According to characteristics that a large amount of objectives in ultrasound images appear similar geometrical shapes and limited deformation, a method is developed by integrating the edge detection, fuzzy clustering, Hough transform and active contour model. The proposed method is able to make the best use of the strength of different segmentation algorithms, while avoiding their deficiencies. This method can be used in the segmentation and measurement of fetal biparietal diameter, head circumference, abdominal circumference and femur length.The fuzzy logic is introduced into the support vector regression to limit the contribution of suspect inaccurate measurements to the training of the fetal weight estimation model. To guarantee the generalization performance of the fuzzy support vector regression (FSVR), the genetic algorithm is employed to obtain optimal paramters for the FSVR. The proposed FSVR modle is used in the weight estimation respectively for normal weight fetuses and low birth weight fetuses. Its performance has been demonstrated by the comparison with the existed regression formulas and artificial neural network models.To avoid shortcomings associated with parameters acquisition and the model design in the clinical fetal weight estimation, a complete fetal weight estimation system is developed by integrating the automated fetal ultrasound image segmentation with the newly developed models. The fetal weight estimation system can provide more accurate fetal weight estimation than existed methods and decrease the estimation errors around 40 to 70 g. Finally, a computer aided diagnosis system is developed for obstetric ultrasound images. The system contains functions of the database manipulation and integrates the image denoising, image segmentation and fetal weight estimation methods which have been previously developed.