A fast automatic recognition and location algorithm for fetal genital organs in ultrasound images

作     者：Sheng TANG Si-ping CHEN 

作者机构：Post-Doctoral Research Station Shenzhen University Shenzhen 518060 China post-Doctoral WorkStation Microprofit Electronie Co. Ltd Shenzhen 518057 China Department of Biomedical Engineering Shenzhen University Shenzhen 518060 China 

出 版 物：《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》 (浙江大学学报（英文版）B辑（生物医学与生物技术）)

年 卷 期：2009年第10卷第9期

页      面：648-658页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 081601[工学-大地测量学与测量工程] 0816[工学-测绘科学与技术] 0802[工学-机械工程] 

主　　题：Ultrasound image Fetal genital organ Point of interest (POI) Feature selection Feature extraction Class imbalance Multiple classifier architecture 

摘      要：Severe sex ratio imbalance at birth is now becoming an important issue in several Asian countries. Its leading immediate cause is prenatal sex-selective abortion following illegal sex identification by ultrasound scanning. In this paper, a fast automatic recognition and location algorithm for fetal genital organs is proposed as an effective method to help prevent ultrasound technicians from unethically and illegally identifying the sex of the fetus. This automatic recognition algorithm can be divided into two stages. In the rough stage, a few pixels in the image, which are likely to represent the genital organs, are automatically chosen as points of interest (POIs) according to certain salient characteristics of fetal genital organs. In the fine stage, a specifically supervised learning framework, which fuses an effective feature data preprocessing mechanism into the multiple classifier architecture, is applied to every POI. The basic classifiers in the framework are selected from three widely used classifiers: radial basis function network, backpropagation network, and support vector machine. The classification results of all the POIs are then synthesized to determine whether the fetal genital organ is present in the image, and to locate the genital organ within the positive image. Experiments were designed and carried out based on an image dataset comprising 658 positive images (images with fetal genital organs) and 500 negative images (images without fetal genital organs). The experimental results showed true positive (TP) and true negative (TN) results from 80.5% (265 from 329) and 83.0% (415 from 500) of samples, respectively. The average computation time was 453 ms per image.