Abstract
There are various medical imaging instruments used for diagnosing prostatic diseases. Ultrasound imaging is the most widely used tool in clinical diagnosis. Urologist outlines the prostate and diagnoses lesions based on his/her experiences. This diagnostic process is subjective and heuristic. Active contour model (ACM) has been successfully applied to outline the prostate contour. However, application of ACM in outlining the contour needs to give the initial contour points manually. In this paper, an automatic prostate tumor identification system is proposed. The sequential floating forward selection (SFFS) is applied to select significant features. A support vector machine (SVM) with radial basis kernel function is used for prostate tumor identification. Experimental results showed that the proposed method achieved higher accuracy than those of other methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chang, C., Lin, C.: LIBSVM: a library for support vector machines, 2.91. ed: Citeseer (2001)
Maroulis, D.E., Savelonas, M.A., Iakovidis, D.K., Karkanis, S.A., Dimitropoulos, N.: Variable Background Active Contour Model for Computer-Aided Delineation of Nodules in Thyroid Ultrasound Images. IEEE Trans. Infor. Tech. in Biomed. 11, 537–543 (2007)
Chen, D.R., Chang, R.F., Wu, W.J., Moon, W.K., Wu, W.L.: 3-D breast ultrasound segmentation using active contour model. Ultrasound in Medicine and Biology 29, 1017–1026 (2003)
Sahba, F., Tizhoosh, H.R., Salama, M.M.: Application of Reinforcement Learning for Segmentation of Transrectal Ultrasound Images. BMC Medical Imaging, 1471–2342 (2008)
Laws, K.: Textured image segmentation. University of southern california, Los Angeles Image Processing Institute (1980)
Betrouni, N., Vermandel, M., Pasquier, D., Maouche, S., Rousseau, J.: Segmentation of Abdominal Ultrasound Images of the Prostate Using a Priori Information and an Adapted Noise Filter. Computerized Medical Imaging and Graphics, 43–51 (2005)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. (1992)
Mohamed, S.S., Salama, M.M.: Spectral Clustering for TRUS Images. BioMedical Engineering OnLine (2007)
Zhang, Y., Sankar, R., Qian, W.: Boundary delineation in transrectal ultrasound image for prostate cancer. Comp. Biol. Med., 1591–1599 (2007)
Chum, Y.D., Seo, S.Y.: Image Retrieval using BDIP and BVLC Moments. IEEE Transactions on Circuits and Systems for Video Tech. 13, 951–957 (2003)
Chen, E.L., Chung, P.C., Chen, C.L., Tsai, H.M., Chang, C.I.: An automatic diagnostic system for CT liver image classification. IEEE Trans. on Biomedical Engineering 45, 783–794 (1998)
Chang, C.Y., Tsai, Y.S., Wu, I.L.: Integrating Validation Incremental Neural Network And Radial-Basis Function Neural Network For Segmenting Prostate In Ultrasound Images. International Journal of Innovative Computing, Information and Control 7, 3035–3046 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Chang, CY., Tu, MY., Tsai, YS. (2014). Prostate Tumor Identification in Ultrasound Images. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume II. Advances in Intelligent Systems and Computing, vol 298. Springer, Cham. https://doi.org/10.1007/978-3-319-07773-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-07773-4_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07772-7
Online ISBN: 978-3-319-07773-4
eBook Packages: EngineeringEngineering (R0)