Abstract
In this work a new informative feature set is proposed to identify suspicious Regions Of Interest (ROIs) in the prostate TransRectal UltraSound (TRUS) images. The proposed features are region based to overcome the limitations present in the pixel based feature extraction methods. First a thresholding algorithm integrated with the medical information is used to identify different candidate ROIs. Next, image registration is performed to transform the prostate image to a model based from which some of the proposed region based features are extracted. Subsequently, the proposed raw based and model based region features are extracted at the region level.
Finally Mutual Information is used to evaluate the extracted features and compare their information content with both the typical pixel based features and the well known texture and grey level features. It was found that the proposed features provide more information than both the texture features and the pixel based features.
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References
Prostate Cancer Statistics, Canadian Cancer Society (August 2008)
Catalona, W.J., Smith, D.S., Ratliff, T.L.: Measurement of prostate specific antigen in serum as a screening test for prostate cancer. English J. Medicine 324, 1156–1161 (1991)
Standaert, B., Alwan, A., Nelen, V., Denis, L.: Prostate volume and cancer in screening programs. Prostate 33, 188–194 (1997)
Presti, J.R.J.C., O’Dowd, G.J., Miller, M.C., Mattu, R., Veltri, R.W.: Extended peripheral zone biopsy schemes increase cancer detection rates and minimize variance in prostate specific antigen and age related cancer rates: Results of a community multi-practice study. J. Urol. 169(1), 125–129 (2003)
Presti, J.R.J.C., Chang, J.J., Bhargava, V., Shinohara, K.: The optimal systematic prostate biopsy scheme should include 8 rather than 6 biopsies: Results of a prospective clinical trial. J. Urol. 163(1), 163–167 (2000)
Mohamed, S.S., Salama, M.M.A., Kamel, M., Rizkalla, K., Chin, J.: Prostate Cancer Multi-feature Analysis using TRUS Images. Physics in Medicine and Biology 50(15), N1175–N1185 (2005)
Scheipers, U., Ermert, H., Sommerfeld, H.-J., Garcia-Schurmann, M., Senge, T., Philippou, S.: Ultrasounic multifeature tissue characterization for prostate diagnostics. Ultrasound. Med. Biol. 29(8), 1137–1149 (2003)
Mohamed, S.S., Salama, M.M.A.: Prostate Cancer Spectral Multi-Feature Analysis using TRUS Images. IEEE Transactions on Medical Imaging 27(4), 548–556 (2008)
Mohamed, S.S., Li, J., Salama, M.M.A., Freeman, G.: Prostate Tissue Texture Feature Extraction for Cancer Recognition in TRUS Images. Journal of Digital Imaging (2008) DOI 10.1007/s10278-008-9124-1
Lizzi, F.L., Feleppa, E.J., Astor, M., Kalisz, A.: Statistics of ultrasonic spectral parameters for prostate and liver examinations. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 44(4), 935–942 (1997)
Rifkin, M.D.: Ultrasound of the Prostate: Imaging in the Diagnosis and Therapy of Prostatic Disease, 2nd edn. Lippincott Williams and Wilkins (1996)
Papanikolaou, N., Gearheart, D., Bolek, T., Meigooni, A., Meigooni, D., Mohiuddin, M.: A Volumetric and Dosimetric Study of LDR Brachytherapy Prostate Implants based on Image Fusion of Ultrasound and Computed Tomography. In: Proceedings of the 22nd Annual EMBS International Conference, Chicago IL, July 23-28 (2000)
Studholme, C., Hill, D.L.G., Hawkes, D.J.: Automated three-dimensional registration of a magnetic resonance and positron emission tomography brain images by ultiresolution optimization of voxel similarity measures. Medical Physics 24(1), 25–35 (1997)
Kostelec, P.J., Weaver, J.B., Healy, D.M.: Multiresolution elastic image registration. Medical Physics 25(9), 1593–1604 (1998)
Jacobs, M.A., Windham, J.P., Soltanian-Zadeh, H., Peck, D.J., Knight, R.A.: Registration and warping of magnetic resonance images to histological sections. Medical Physics 26(8), 1568–1578 (1999)
Bharatha, A., Hirose, M., Hata, N., Warfield, S.K., Ferrant, M., Zou, K.H., Suarez-Santana, E., Ruiz- Alzola, J., D’Amico, A., Cormack, r.A., Kikinis, R., Jolesz, F.A., Tempany, C.M.C.: Evaluation of threedimensional finite element-based deformable registration of pre- and intraoperative prostate imaging. Medical Physics 28(12), 2551–2560 (2001)
Christensen, G.E., Yin, P., Vannier, M.W., Chao, K.S.C., Dempsey, J.F., Williamson, J.F.: Large- Deformation Image Registration using Fluid Landmarks. Department of Electrical and Computer Engineering, The University of Iowa
Joshi, S.C., Miller, M.I.: Landmark Matching via Large Deformation Diffeomorphisms. IEEE Transactions on Image Processing 9(8), 1357–1370 (2000)
Mir, A.H., Hanmandlu, M., Tandon, S.N.: Texture Analysis of CT Images. Engineering in Medicine and Biology Magazine 14(6), 781–786 (1995)
Prakash, K.N.B., Ramakrishnan, A.G., Suresh, S., Chow, T.W.P.: Fetal Lung Maturity Analysis Using Ultrasound Image Features. IEEE Transactions on Information Technology in Biomedicine 6(1), 38–45 (2002)
Parzen, E.: On Estimation of a Probability Density Function and Mode. The Annals of Mathematical Statistics 33(3), 1065–1076 (1962)
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Hui, E.K.T., Mohamed, S.S., Salama, M.M.A., Fenster, A. (2009). Prostate TRUS Image Region-Based Feature Extraction and Evaluation. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_75
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DOI: https://doi.org/10.1007/978-3-642-02611-9_75
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