Obstetrics and Gynecology early diagnosis of the ultrasound image analysis and processing
|Keywords||the early diagnosis of gynecology and obstetrics ultrasound image speckle polycystic ovary syndrome adaptive morphological filtering object growing algorithm early fetal cardiac structure Rayleigh-trimmed anisotropic diffusion active cardiac model nuchal translucency hierarchical model|
Ultrasound diagnosis is one of the most important diagnostic imaging techniques, which has been widely applied in the diagnosis of gynecology and obstetrics due to its merits of harmlessness, real-time imaging and noninvasiveness. Ultrasound diagnosis of gynecology and obstetrics can be effectively used to evaluate the physiologic health of expectant mothers and fetuses, which is the important way to improve the accuracy and objectivity of the diagnosis. Nowadays one hot research topic of ultrasound diagnosis of gynecology and obstetrics is the detection for the early disease. This early diagnosis can benefit the further medical process but also increases the difficulty of diagnosis. The current problems of such clinical ultrasound diagnosis mainly come from the high dependence of the experience of doctors, the manual operations for detections and the difficulty of processing the information. Therefore it is required for the researches to set up an automatic analysis and processing system for ultrasound images.Because of the interferences of ultrasound echo signals during the imaging, the formed speckle may affect the performance of further image analysis and processing algorithms. Therefore the two key problems of the analysis and processing methods for ultrasound images are the processing of speckle and the establishment of object models.This dissertation focuses on three hot topics of the early diagnosis of gynecology and obstetrics. To automatically aid the diagnosis, the studies are carried out on the analysis and processing methods for ultrasound images.For the detection of follicles of polycystic ovary syndrome, the adaptive morphological filtering is first proposed for despeckling, which can effectively depresse the abrupt variations of pixel values due to the speckle. Then the enhanced labeled watershed algorithm is proposed to segment the candidates of follicles and the iterative algorithm of automatically selecting the region of interest is proposed based on the spectral residual approach. Finally the object growing algoritm is proposed to realize the automated detection of follicles inside the ovaries. Compared with other methods, experimental results demonstrate the better performance of this proposed method which achieves the 89.4% recognition rate and 7.45% misidentification rate. For the detection of the structure of early fetal hearts, the automatic selection algorithm for the region of interest is first proposed based on the accumulated motion image. This algorithm can overcome the low signal-to-noise ratio of the ultrasound image sequences and accurately extract the region of fetal hearts. Then the Rayleigh-trimmed anisotropic diffusion is proposed to deal with the low signal-to-noise ratio of the ultrasound images of fetal hearts and emphasize the motion information for the next processing. Finally the active cardiac model is proposed for the automated detection of early fetal cardiac structure. Both the structure and the motion information of fetal hearts are considered simultaneously with this model. The convergence of the inference of the model is guaranteed through optimizing the structure of the model. According to the experimental results of detecting the early fetal cardiac structures, the error of 90 percent of detections of this proposed method is less than 13 pixels.For the automated detection of the nuchal translucency region, a three-layer hierarchical model is proposed. In this model, the support vector machine is applied to represent the related objects based on the features of the histogram of oriented gradient. Then the corresponding spatial model is established to define the spatial constrains between different objects. The optimal solution for the inference of the model can be obtained by applying the dynamic programming and generalized distance transform. Experimental results demonstrate that the model can achieve the accuracy of about 60% for the automated detection of the nuchal translucency.