Similar articles

Abstract: 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.

[1] Barandela, R., Sanchez, J.S., Garcia, V., Rangel, E., 2003. Strategies for learning in class imbalance problems. Pattern Recogn., 36(3):849-851.

[2] Batista, G.E., Prati, R.C., Monard, M.C., 2004. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor. Newsl., 6(1):20-29.

[3] Bhanu Prakash, K.N., Ramakrishnan, A.G., Suresh, S., Chow, W.P., 2002. Fetal lung maturity analysis using ultrasound image features. IEEE Trans. Inf. Technol. Biomed., 6(1):38-45.

[4] Bishop, C.M., 1995. Neural Networks for Pattern Recognition. Oxford University Press, Oxford.

[5] Blum, A., Langley, P., 1997. Selection of relevant features and examples in machine learning. Artif. Intell., 97(1-2): 245-271.

[6] Buller, D., Buller, A., Innocent, P.R., Pawlak, W., 1996. Determining and classifying the region of interest in ultrasonic images of the breast using neural networks. Artif. Intell. Med., 8(1):53-66.

[7] Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P., 2002. SMOTE: synthetic minority oversampling technique. J. Artif. Intell. Res., 16:321-357.

[8] Chawla, N.V., Japkowicz, N., Kolcz, A., 2004. Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explor. Newsl., 6(1):1-6.

[9] Chen, D.R., Chang, R.F., Chen, C.J., Ho, M.F., Kuo, S.J., Chen, S.T., Hung, S.J., Moon, W.K., 2005. Classification of breast ultrasound images using fractal feature. Clin. Imaging, 29(4):235-245.

[10] Chen, Y., Yin, R., Flynn, P., Broschat, S., 2003. Aggressive region growing for speckle reduction in ultrasound images. Pattern Recogn. Lett., 24(4-5):677-691.

[11] Chrzanowski, L., Drozdz, J., Strzelecki, M., Krzeminska-Pakula, M., Jedrzejewski, K.S., Kasprzak, J.D., 2008. Application of neural networks for the analysis of intravascular ultrasound and histological aortic wall appearance—an in vitro tissue characterization study. Ultrasound Med. Biol., 34(1):103-113.

[12] Gonzalez, R.C., Woods, R.E., 2002. Digital Image Processing, 2nd Ed. Prentice Hall, New Jersey.

[13] Guyon, I., Elisseeff, A., 2003. An introduction to variable and feature selection. J. Mach. Learn. Res., 3(7-8):1157-1182.

[14] Jardim, S., Figueiredo, M., 2005. Segmentation of fetal ultrasound images. Ultrasound Med. Biol., 31(2):243-250.

[15] Johnson, R.A., Wichern, D., 1992. Applied Multivariate Statistical Analysis. Prentice Hall, New Jersey.

[16] Kadonaga, T., Abe, K., 1995. Comparison of Methods for Detecting Corner Points from Digital Curves. Proceedings of the First International Workshop on Graphic Recognition. Aug. 10~11, University Park, PA, USA, p.23-34.

[17] Karaman, M., Kutay, M., Bozdagi, G., 1995. An adaptive speckle suppression filter for medical ultrasonic imaging. IEEE Trans. Med. Imaging, 14(2):283-292.

[18] Kittler, J., Hatef, M., Duin, R.P.W., Matas, J., 1998. On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell., 20(3):226-239.

[19] Kubat, M., Matwin, S., 1997. Addressing the Curse of Imbalanced Training Test: One-Sided Selection. Proceedings of the Fourteenth International Conference on Machine Learning. Jul. 8~12, Nashville, Tennessee, USA, p.179-186.

[20] Li, S., 2007. Imbalanced Sex Ratio at Birth and Comprehensive Intervention in China. The 4th Asia Pacific Conference on Reproductive and Sexual Health and Rights, Oct. 29~31, Hyderabad, India. Available from: http://www.unfpa.org/gender/docs/studies/china.pdf

[21] Loupas, T., McDicken, W.N., Allan, P.L., 1989. Adaptive weighted median filter for speckle suppression in medical ultrasonic images. IEEE Trans. Cir. Syst., 36(1):129-135.

[22] Lu, C., van Gestel, T., Suykens, J.A.K., van Huffel, S., Vergote, I., Timmerman, D., 2003. Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines. Artif. Intell. Med., 28(3):281-306.

[23] Lu, W., Tan, J., Floyd, R., 2005. Automated fetal head detection and measurement in ultrasound images by iterative randomized Hough transform. Ultrasound Med. Biol., 31(7):929-936.

[24] Piliouras, N., Kalatzis, I., Dimitropoulos, N., Cavouras, D., 2004. Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound. Comput. Med. Imaging Graph., 28(5):247-255.

[25] Population Census Office under Chinese State Council, 2002. Tabulation on the 2000 Population Census of the People’s Republic of China. China Statistics Press, Beijing (in Chinese).

[26] Press, W.H., Flannery, B.P., 1992. Numerical Recipes in FORTRAN: The Art of Scientific Computing, 2nd Ed. Cambridge University Press, Cambridge, p.617-620.

[27] Qi, Y., Mason, W.M., 2005. Prenatal sex-selective abortion and high sex ratio at birth in rural China: a case study in Henan Province. California Center for Population Research, On-Line Working Paper Series. Available from: http://repositories.cdlib.org/ccpr/olwp/CCPR-057-05

[28] Scholly, T.A., Sutphen, J.H., Mackey, S.C., Langstaff, L.M., 1980. Sonographic determination of fetal gender. AJR, 135(6):1161-1165.

[29] Sivaramakrishna, R., Powell, K.A., Leiber, M.L., Chilcote, W.A., Shekhar, R., 2002. Texture analysis of lesions in breast ultrasound images. Comput. Med. Imaging Graph., 26(5):303-307.

[30] Smutek, D., Tjahjadi, T., Sara, R., Svec, M., Sucharda, P., Svacina, S., 2001. Image Texture Analysis of Sonograms in Chronic Inflammations of Thyroid Gland. Research Report CTU-CMP-2001-15. April 2001, Czech Technical University, Prague, Czech Republic, ISSN 1213-2365. Available from: ftp://cmp.felk.cvut.cz/pub/cmp/articles/sara/Smutek-TR-2001-15.pdf

[31] Strzelecki, M., Materka, A., Drozdz, J., Krzeminska-Pakula, M., Kasprzak, J.D., 2006. Classification and segmentation of intracardiac masses in cardiac tumor echocardiograms. Comput. Med. Imaging Graph., 30(2):95-107.

[32] Vince, D.G., Dixon, K.J., Cothren, R.M., Cornhill, J.F., 2000. Comparison of texture analysis methods for the characterization of coronary plaques in intravascular ultrasound images. Comput. Med. Imaging Graph., 24(4):221-229.

[33] Zhu, Y., Williams, S., Zwiggelaar, R., 2006. Computer technology in detection and staging of prostate carcinoma: a review. Med. Image Anal., 10(2):178-199.