Localisation of Zones of Cancer Detection in Prostate Gland Using Ratio Matrix and Radial Scanning of 2D Trans-rectal Ultrasound Images;

Document Type : Research/Original Article

Authors

1 Electronic Engineering Dept., University of Nigeria, Nsukka, Enugu State, Nigeria

2 Mechatronic Engineering Dept., Federal University of Technology Owerri, Imo State, Nigeria

Abstract

Researchers have continued to proffer various solutions to the challenge of delineating from Trans-rectal
ultrasound (TRUS) 2D-images of the prostate the regions of desired property. This paper presents an algorithm
that categorises the detected regions suspected to be cancerous, hyper-echoic pixels, in the prostate gland from
a 2D Trans-rectal Ultrasound images into three zones. The developed algorithm uses radial scanning of the pixels
of the prostate gland image from common seed point both to detect and delineate the suspected cancerous
pixels into zones, namely peripheral, transition and central, by applying ratios of the anatomical zones of the
prostate gland. Expert knowledge, intensity and gradient features were implemented to delineate regions of
interest. MATLAB programming tool was used for creating the codes that implemented the algorithms. Samples
of TRUS 2D-images of the prostate for patients with raised PSA values (>10 ng/ml) used in a previous work by
Award (2007) were used for testing the algorithm. The test results showed that the algorithm could detect zones
of the prostate boundary exhibit image properties for cancer cells and also the percentage of malignancy
detected in zones agreed with existing research findings. Comparison of detection results with that of an expert
radiologist yielded the following performance parameters; accuracy of 88.55% and sensitivity of 71.65%.

Keywords

References

Dabic-Stankovic K, Rajkovic K, Acimovic M, Milosevic N, Stankovic J. A Quantitative Analysis of Two-dimensional Manually Segmented Transrectal Ultrasound Axial Images in Planning High Dose Rate Brachytherapy for Prostate Cancer. Vojnosanit Pregl. 2017; 74(5): 420-427. doi: 10.2298/VSP150901231D
2.     Siegel RI, Miller KD, Jemal A. Cancer Statistics, 2016, CA Cancer J. Clin. 2016 Jan-Feb; 66(1): 7-30. doi: 10.3322/caac.21332.
3.     Yu Y, Cheng J, Li J, Chen W, Chiu B. Automatic Prostate Segmentation from Trans-rectal Ultrasound Images. 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings. doi:10.1109/BioCAS.2014.6981659
 4.    Pathak SD, Chalana V, Haynor DR, Kim Y. Edge-Guided Boundary Delineation in Prostate Ultrasound Images. IEEE Trans Med Imaging. 2000; 19(12): 543-551.
 5.    Pham DL, Xu C, Prince JL. Current Methods in Medical Image Segmentation. Annu. Rev.  Biomed. Eng. 2000. 02:315-337.
 6.    Liu X, Langer DL, Haider MA, Yang Y, Wemick MN, Yetick IS. Prostate Cancer Segmentation With Simultaneous Estimation of Markov Random Field Parameters and Class. IEEE Trans Med Imaging. 2009;  28(6): 906-915.
 7.    Chiu B. A New Segmentation Algorithm for Prostate Boundary Detection in 2D Ultrasound Images. Master’s thesis, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada, 2003.
 8.    Chijindu VC, Udeze CC, Ahaneku MA, Anoliefo EC. Detection of Prostate Cancer Using Radial/Axial Scanning of 2D Trans-rectal Ultrasound Images. Bulletin of Electrical Engineering and Informatic. 2018; 7(2): 222-229. doi: 10.11591/eei.v7i2.727
9.     Ladak HM, Mao F, Wang Y, Downey DB, Steinman DA, Fenster A. Prostate Boundary Segmentation from 2D Ultrasound Images. Med Phys. 2000 Aug; 27(8): 1777-1788. doi: 10.1118/1.1286722
10.  Lobregt S, Viergever MA. A Discrete Dynamic Contour Model. IEEE Trans Med Imaging, 1995; 14(1): 12-22.
11.  Yang X, Schuster D, Master V, Nieh P, Fenster A, Fei B. Automatic 3D Segmentation of Ultrasound Images Using Atlas Registration and Statistical Texture Prior. Proc. of SPIE 2011; 7964:796432-1-8. doi: 10.1117/12.877888
12.  Zhan Y, Shen D. Deformable Segmentation of 3D Ultrasound Prostate Images Using Statistical Texture Matching Method. IEEE Trans Med Imaging, 2006; 25(3): 256-272.
13.  Abari H, Fei B. 3D Ultrasound Image Segmentation Using Wavelet Support Vector Machines. Med. Phys. 2012 June;39(6): 2972-2984. doi: 10.1118/1.4709607
14.  Awad JGE. Prostate Segmentation and Regions of Interest Detection in Transrectal Ultrasound Images. A Doctor of Philosophy Thesis, University of Waterloo, Waterloo, Ontarion, Canada, 2007.
15.  Wu R, Ling KV, Shao W, Ng WS. Registration of Organ Surface with Intra-operative 3D Ultrasound Image Using Genetic Algorithm. MCCAI 2000; 2878: 383-390. doi: 10.1007/978-3-540-39899-8_48.
16.  Eskandari H, Talebpour A, Tabrizi SH, Nowroozi MR. Development of a Fast Algorithm for Automatic Delineation of Prostate Gland on 2D Ultrasound Images. Proceedings of the 19th Iranian Conference on Biomedical Engineering (ICBME 2012), pp. 313-317, 2012.
 
Iranian Journal of Colorectal Research
Volume 7, Issue 4
December 2019
Pages 1-7

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