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Received: 2009-03-19

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Crosschecked: 2009-08-13

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Journal of Zhejiang University SCIENCE B 2009 Vol.10 No.9 P.648~658


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

Author(s):  Sheng TANG, Si-ping CHEN

Affiliation(s):  Post-Doctoral Research Station, Shenzhen University, Shenzhen 518060, China; more

Corresponding email(s):   chensiping@szu.edu.cn

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

Sheng TANG, Si-ping CHEN. A fast automatic recognition and location algorithm for fetal genital organs in ultrasound images[J]. Journal of Zhejiang University Science B, 2009, 10(9): 648~658.

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author="Sheng TANG, Si-ping CHEN",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

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%T A fast automatic recognition and location algorithm for fetal genital organs in ultrasound images
%A Sheng TANG
%A Si-ping CHEN
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%DOI 10.1631/jzus.B0930162

T1 - A fast automatic recognition and location algorithm for fetal genital organs in ultrasound images
A1 - Sheng TANG
A1 - Si-ping CHEN
J0 - Journal of Zhejiang University Science B
VL - 10
IS - 9
SP - 648
EP - 658
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Y1 - 2009
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.B0930162

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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