An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision

  • Mohammad AL-Oudat
  • Saleh Alomari
  • Hazem Qattous
  • Mohammad Azzeh
  • Tariq AL-Munaizel

Abstract

The biliary tree is a network of tubes that connects the liver to the gallbladder, an organ right beneath it. The bile duct is the major tube in the biliary tree. The dilatation of a bile duct is a key indicator for more major problems in the human body, such as stones and tumors, which are frequently caused by the pancreas or the papilla of vater. The detection of bile duct dilatation can be challenging for beginner or untrained medical personnel in many circumstances. Even professionals are unable to detect bile duct dilatation with the naked eye. This research presents a unique vision-based model for biliary tree initial diagnosis. To segment the biliary tree from the Magnetic Resonance Image, the framework used different image processing approaches (MRI). After the image’s region of interest was segmented, numerous calculations were performed on it to extract 10 features, including major and minor axes, bile duct area, biliary tree area, compactness, and some textural features (contrast, mean, variance and correlation). This study used a database of images from King Hussein Medical Center in Amman, Jordan, which included 200 MRI images, 100 normal cases, and 100 patients with dilated bile ducts. After the characteristics are extracted, various classifiers are used to determine the patients’ condition in terms of their health (normal or dilated). The findings demonstrate that the extracted features perform well with all classifiers in terms of accuracy and area under the curve. This study is unique in that it uses an automated approach to segment the biliary tree from MRI images, as well as scientifically correlating retrieved features with biliary tree status that has never been done before in the literature.

Author Biography

Mohammad Azzeh
Applied Science Private university

References

[1] Benou A, Veksler R, Friedman A, and Riklin Raviv T. Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences. Medical image analysis, 42:145- 159, dec 2017.
https://doi.org/10.1016/j.media.2017.07.006

[2] Sundos Abdulameer Alazawi, Narjis Mezaal Shati, and Amel H. Abbas. Texture features extraction based on GLCM for face retrieval system. Periodicals of Engineering and Natural Sciences (PEN), 7(3):1459-1467, oct 2019.
https://doi.org/10.21533/pen.v7i3.787

[3] Theek B, Nolte T, Pantke D, Schrank F, Gremse F, Schulz V, and Kiessling F. Emerging methods in radiology. Der Radiologe, 60(Suppl 1):41-53, nov 2020.
https://doi.org/10.1007/s00117-020-00696-0

[4] P. Bamback, K. C. Baumgardner, M. Bartanuszova, H. L. Nation, and A. P. Occhialini. Anomalous gallbladder septum-A case report. International Journal of Surgery Case Reports, 84:106082, jul 2021.
https://doi.org/10.1016/j.ijscr.2021.106082

[5] Leonardo Centonze, Stefano Di Sandro, Iacopo Mangoni, and Luciano De Carlis. Biliary Reconstruction Techniques: From Biliary Tumors to Transplantation. Liver Transplantation and Hepatobiliary Surgery, pages 61-73, 2020.
https://doi.org/10.1007/978-3-030-19762-9_7

[6] S. Nirmala. Devi. Segmentation and 3D visualization of liver, lesions and major blood vessels in abdomen CTA images. Biomedical Research, 28(16), 2017.

[7] Inês Domingues, Gisèle Pereira, Pedro Martins, Hugo Duarte, João Santos, and Pedro Henriques Abreu. Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET. Artificial Intelligence Review 2019 53:6, 53(6):4093-4160, nov 2019.
https://doi.org/10.1007/s10462-019-09788-3

[8] Reboul E. Vitamin E Bioavailability: Mechanisms of Intestinal Absorption in the Spotlight. Antioxidants (Basel, Switzerland), 6(4), dec 2017.
https://doi.org/10.3390/antiox6040095

[9] Lemaigre FP. Development of the Intrahepatic and Extrahepatic Biliary Tract: A Framework for Understanding Congenital Diseases. Annual review of pathology, 15:1-22, jan 2020.
https://doi.org/10.1146/annurev-pathmechdis-012418-013013

[10] Rao HB, Koshy AK, Sudhindran S, Prabhu NK, and Venu RP. Paradigm shift in the management of bile duct strictures complicating living donor liver transplantation. Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology, 38(6):488-497, dec 2019.
https://doi.org/10.1007/s12664-019-01000-2

[11] Kaiming He, Jian Sun, and Xiaoou Tang. Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12):2341-2353, 2011.
https://doi.org/10.1109/TPAMI.2010.168

[12] Sandy Hassan Hossary, Ashraf Anas Zytoon, Mohamed Eid, Ahmed Hamed, Mohamed Sharaan, and Ahmed Abd El Maguid Ebrahim. MR Cholangiopancreatography of the Pancreas and Biliary System: A Review of the Current Applications. Current Problems in Diagnostic Radiology, 43(1):1-13, jan 2014.
https://doi.org/10.1067/j.cpradiol.2013.10.001

[13] Nathan C. Hull, Gary R. Schooler, and Edward Y. Lee. Bile Duct and Gallbladder. Pediatric Body MRI, pages 235-253, 2020.
https://doi.org/10.1007/978-3-030-31989-2_8

[14] Hieu Trung Huynh, Ibrahim Karademir, Aytekin Oto, and Kenji Suzuki. Computerized Liver Volumetry on MRI by Using 3D Geodesic Active Contour Segmentation. AJR. American journal of roentgenology, 202(1):152, jan 2014.
https://doi.org/10.2214/AJR.13.10812

[15] Joo I, Lee JM, and Yoon JH. Imaging Diagnosis of Intrahepatic and Perihilar Cholangiocarcinoma: Recent Advances and Challenges. Radiology, 288(1):7-23, jul 2018.
https://doi.org/10.1148/radiol.2018171187

[16] Dongsheng Jiang, Weiqiang Dou, Luc Vosters, Xiayu Xu, Yue Sun, and Tao Tan. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network. Japanese Journal of Radiology, 36(9):566-574, dec 2017.
https://doi.org/10.1007/s11604-018-0758-8

[17] Dongsheng Jiang, Weiqiang Dou, Luc Vosters, Xiayu Xu, Yue Sun, and Tao Tan. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network. Japanese Journal of Radiology, 36(9):566-574, dec 2017.
https://doi.org/10.1007/s11604-018-0758-8

[18] Kozaka K, Sheedy SP, Eaton JE, Venkatesh SK, and Heiken JP. Magnetic resonance imaging features of small-duct primary sclerosing cholangitis. Abdominal radiology (New York), 45(8):2388- 2399, aug 2020.
https://doi.org/10.1007/s00261-020-02572-w

[19] Skoczylas K and Pawełas A. Ultrasound imaging of the liver and bile ducts - expectations of a clinician. Journal of ultrasonography, 15(62):292-306, sep 2015.
https://doi.org/10.15557/JoU.2015.0026

[20] Sugimachi K, Mano Y, Matsumoto Y, Iguchi T, Taguchi K, Hisano T, Sugimoto R, Morita M, and Toh Y. Adenomyomatous hyperplasia of the extrahepatic bile duct: a systematic review of a rare lesion mimicking bile duct carcinoma. Clinical journal of gastroenterology, 14(2):393-401, apr 2021.
https://doi.org/10.1007/s12328-020-01327-w

[21] Nachiket Kapre and André DeHon. Programming FPGA Applications in VHDL. Reconfigurable Computing, pages 129-153, jan 2008.
https://doi.org/10.1016/B978-012370522-8.50011-X

[22] JA Lübbe. Aspects on Advanced Procedures During Endoscopic Retrograde Cholangiopancreatography (ERCP) for Complex Hepatobiliary Disorders. PhD thesis, Karolinska Institutet, Sweden, 2021.

[23] Mazurowski MA, Buda M, Saha A, and Bashir MR. Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. Journal of magnetic resonance imaging : JMRI, 49(4):939-954, apr 2019.
https://doi.org/10.1002/jmri.26534

[24] Jose V. Manjon and Pierrick Coupe. MRI denoising using Deep Learning and Non-local averaging. nov 2019.

[25] HAZEM MIGDADY. A FEATURES EXTRACTION WRAPPER METHOD FOR NEURAL NETWORKS WITH APPLICATION TO DATA MINING AND MACHINE LEARNING. PhD thesis, Southern Illinois University Carbondale, may 2013.

[26] Hazem Migdady. BOUNDNESS OF A NEURAL NETWORK WEIGHTS USING THE NOTION OF A LIMIT OF A SEQUENCE. International Journal of Data Mining & Knowledge Management Process (IJDKP), 4(3), 2014.
https://doi.org/10.5121/ijdkp.2014.4301

[27] Hazem Migdady, Yousef Talafha, and Hussam Alrabaiah. Controlling the Behavior of a Neural Network Weights Using Variables Correlation and Posterior Probabilities Estimation. IOSR Journal of Computer Engineering, 16(3):36-41, 2014.
https://doi.org/10.9790/0661-16323641

[28] Bakheet N, Ichkhanian Y, Runge TM, Vosoughi K, and Khashab MA. Endoscopic ultrasoundguided cholecystoduodenostomy for acute cholecystitis with removal of large (missed) cystic duct stones. Endoscopy, 51(12):E354-E355, 2019.
https://doi.org/10.1055/a-0934-4898

[29] Mehta N, Strong AT, Stevens T, El-Hayek K, Nelson A, Owoyele A, Eltelbany A, Chahal P, Rizk M, Burke CA, McMichael J, Lopez R, Veniero J, Vargo J, Kroh M, and Bhatt A. Common bile duct dilation after bariatric surgery. Surgical endoscopy, 33(8):2531-2538, aug 2019.
https://doi.org/10.1007/s00464-018-6546-9

[30] Ivashchenko OV, Rijkhorst EJ, Ter Beek LC, Hoetjes NJ, Pouw B, Nijkamp J, Kuhlmann KFD, and Ruers TJM. A workflow for automated segmentation of the liver surface, hepatic vasculature and biliary tree anatomy from multiphase MR images. Magnetic resonance imaging, 68:53-65, may 2020.
https://doi.org/10.1016/j.mri.2019.12.008

[31] Rawla P, Sunkara T, Thandra KC, and Barsouk A. Epidemiology of gallbladder cancer. Clinical and experimental hepatology, 5(2):93-102, 2019.
https://doi.org/10.5114/ceh.2019.85166

[32] Dubok Park, Hyungjo Park, David K Han, and Hanseok Ko. SINGLE IMAGE DEHAZING WITH IMAGE ENTROPY AND INFORMATION FIDELITY. In EEE International Conference on Image Processing (ICIP), pages 4037-4041, 2014.
https://doi.org/10.1109/ICIP.2014.7025820

[33] Rong Peng, Ling Zhang, Xiao-Ming Zhang, Tian-Wu Chen, Lin Yang, Xiao-Hua Huang, and Ze- Ming Zhang. Common bile duct diameter in an asymptomatic population: A magnetic resonance imaging study. World Journal of Radiology, 7(12):501, 2015.
https://doi.org/10.4329/wjr.v7.i12.501

[34] Stephen M. Pizer, E. Philip Amburn, John D. Austin, Robert Cromartie, Ari Geselowitz, Trey Greer, Bart ter Haar Romeny, John B. Zimmerman, and Karel Zuiderveld. Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing, 39(3):355-368, sep 1987.
https://doi.org/10.1016/S0734-189X(87)80186-X

[35] Tao Qin. Machine Learning Basics. Dual Learning, pages 11-23, 2020.
https://doi.org/10.1007/978-981-15-8884-6_2

[36] Saurabh S and Green B. Is hyperkinetic gallbladder an indication for cholecystectomy? Surgical endoscopy, 33(5):1613-1617, may 2019.
https://doi.org/10.1007/s00464-018-6435-2

[37] C. Saravanan. Color image to grayscale image conversion. 2010 2nd International Conference on Computer Engineering and Applications, ICCEA 2010, 2:196-199, 2010.
https://doi.org/10.1109/ICCEA.2010.192

[38] Emmanuel A Selvaraj, Emma L Culver, Helen Bungay, Adam Bailey, Roger W Chapman, and Michael Pavlides. Evolving role of magnetic resonance techniques in primary sclerosing cholangitis. World Journal of Gastroenterology, 25(6):644, 2019.
https://doi.org/10.3748/wjg.v25.i6.644

[39] Ilkay Sensoy. A review on the food digestion in the digestive tract and the used in vitro models. Current Research in Food Science, 4:308-319, jan 2021.
https://doi.org/10.1016/j.crfs.2021.04.004

[40] Venkatesh SK, Welle CL, Miller FH, Jhaveri K, Ringe KI, Eaton JE, Bungay H, Arrivé L, Ba-Ssalamah A, Grigoriadis A, Schramm C, and Fulcher AS. Reporting standards for primary sclerosing cholangitis using MRI and MR cholangiopancreatography: guidelines from MRWorking Group of the International Primary Sclerosing Cholangitis Study Group. European radiology, pages 1-15, 2021.
https://doi.org/10.1007/s00330-021-08147-7

[41] Saluja SS, Varshney VK, Bhat VS, Nekarakanti PK, Arora A, Sachdeva S, and Mishra PK. Management of Obscurely Dilated Common Bile Duct with Normal Liver Function Tests: A Pragmatic Approach. World journal of surgery, 45(9):2712-2718, sep 2021.
https://doi.org/10.1007/s00268-021-06175-4

[42] Inomata T, Nakaya K, Michimoto K, Kano R, Masuda Y, Suzuki H, Sawaguchi N, Sugawara K, and Sugiyama S. Evaluation of the usefulness of cystic duct three-dimensional computed tomography with non-contrast for before laparoscopic cholecystectomy and endoscopic transpapillary gallbladder drainage in comparison to magnetic resonance cholangiopancreatography. Journal of medical imaging and radiation sciences, 52(2):248-256, jun 2021.
https://doi.org/10.1016/j.jmir.2021.03.037

[43] LA Vese TF Chan. Active contours without edges. IEEE Trans Image Process, 10(2):266-277, 2017.
https://doi.org/10.1109/83.902291

[44] Prasun Chandra Tripathi and Soumen Bag. CNN-DMRI: A Convolutional Neural Network for Denoising of Magnetic Resonance Images. Pattern Recognition Letters, 135:57-63, jul 2020.
https://doi.org/10.1016/j.patrec.2020.03.036

[45] Yang W, Chen Y, Liu Y, Zhong L, Qin G, Lu Z, Feng Q, and Chen W. Cascade of multi-scale convolutional neural networks for bone suppression of chest radiographs in gradient domain. Medical image analysis, 35:421-433, jan 2017.
https://doi.org/10.1016/j.media.2016.08.004

[46] Fu-Tian Wang, Li Yang, Jin Tang, Si-Bao Chen, and Xin Wang. DLA+: A Light Aggregation Network for Object Classification and Detection. International Journal of Automation and Computing 2021, pages 1-10, mar 2021.
https://doi.org/10.1007/s11633-021-1287-y

[47] Nakai Y, Sato T, Hakuta R, Ishigaki K, Saito K, Saito T, Takahara N, Hamada T, Mizuno S, Kogure H, Tada M, Isayama H, and Koike K. Management of Difficult Bile Duct Stones by Large Balloon, Cholangioscopy, Enteroscopy and Endosonography. Gut and liver, 14(3):297-305, 2020.
https://doi.org/10.5009/gnl19157

[48] Zhen-Jie Yao, Jie Bi, and Yi-Xin Chen. Applying Deep Learning to Individual and Community Health Monitoring Data: A Survey. International Journal of Automation and Computing 2018 15:6, 15(6):643-655, sep 2018.
https://doi.org/10.1007/s11633-018-1136-9

[49] Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 26(7):3142-3155, jul 2017.
https://doi.org/10.1109/TIP.2017.2662206
Published
2021-09-03
How to Cite
AL-OUDAT, Mohammad et al. An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 16, n. 5, sep. 2021. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/4275>. Date accessed: 18 oct. 2021. doi: https://doi.org/10.15837/ijccc.2021.5.4275.