The Study Of Automatic Ultrasound Uterus Image Recognition

The research development of medical ultrasound image recognition is still cumbered by the poor quality of ultrasound image, including speckle, low signal-to-noise ratio, lost of edge information, etc. In this dissertation, focusing on the instinctive characteristics of ultrasound image recognition problem, the class imbalance problem and the data noise problem are studied at first, and then an ultrasound uterus image recognition framework is proposed, which is based on the proposed feature data preprocessing mechanism and the multiple classifiers fusion. After that, a fast and automatic ultrasound uterus image recognition algorithm for the intra-uterine device is proposed and realized. At last, a fast and automatic ultrasound image recognition algorithm for fetal genital organ is proposed and realized.The main researches of this dissertation are listed as following:1, The related work of dealing with the class imbalance problem is firstly reviewed, and then a proper generation mechanism of synthetic minority class examples is discussed. According to the analysis, a novel oversampling algorithm with synthetic examples, ADOMS, is proposed. The experiments are arranged on the UCI datasets and the experimental results show that comparing with other relative methods, algorithm ADOMS is able to alleviate the deterioration of the classification performance of the classifiers effectively.2, The related work of dealing with the data noise problem is firstly reviewed, and then based on the concept of mathematic morphology, a series of morphological data cleansing algorithms are proposed. The experiments are arranged on the UCI datasets and the experimental results show that these morphological data cleansing algorithms can effectively improve the classification performance of the classifiers, comparing with other relative methods.3, A concatenation mechanism is proposed to handle the class imbalance problem and the data noise problem together, and the effect of the proposed concatenation mechanism to the ultrasound uterus image recognition problem is confirmed by the experiments. And then, combing a proposed feature data preprocessing mechanism and the multiple classifiers fusion, an ultrasound uterus image recognition framework is proposed.4, A fast and automatic ultrasound uterus image recognition algorithm for the intra-uterine device (IUD) is proposed and realized. The algorithm is composed of automatic ultrasound uterus image segmentation and the specific recognition framework, which are connected by object of interest (OOI). Based on 719 ultrasound uterus images, the experiments are carried out. The experimental results show that the proposed algorithm is fully automatic, and the average time-consuming is 527 milliseconds per frame, as well as the accuracy for the uterus image with IUD is 81.1% and the accuracy for the uterus image without IUD is 94.7%.5, A fast and automatic ultrasound image recognition algorithm for fetal genital organ is proposed and realized. The algorithm is composed of rough classification stage and fine classification stage, which are connected by pixel of interest (POI). Based on 658 positive images (the ultrasound images which containing fetal genital organ) and 500 negative images (the ultrasound images without fetal genital organ), the experiments are carried out. The experimental results show that the proposed algorithm is fully automatic, and the average time-consuming is 453 milliseconds per frame, as well as the accuracy for the positive images is 80.9%, and the accuracy for the negative images is 83.8%.