Skip to main content
SpringerLink
Log in

Segmentation and Feature Extraction of Endoscopic Images for Making Diagnosis of Acute Appendicitis

  • APPLIED PROBLEMS
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

In recent years, digital endoscopy has established as key technology for medical screenings and minimally invasive surgery. Endoscopy image processing techniques have been applied to the diagnosis of diseases. In this paper, an effective approach is proposed to process endoscopic images to detect acute appendicitis. For this purpose, we first introduced image enhancement techniques that allow us to improve quality of endoscopic image for further processing. A simple and effective image segmentation technique was developed to detect vessels and vermiform appendix. The hierarchical set of features have been extracted for detecting acute appendicitis. It includes geometrical, colorimetric, densitometric, and topological features. For each appendicitis feature discriminant indexes have been introduced for diagnosis. This method has achieved good results in clinical application.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Similar content being viewed by others

REFERENCES

  1. K. V. Asari, S. Kumar, and D. Radhakrishnan, “A new approach for nonlinear distortion correction in endoscopic images based on least squares estimation,” IEEE Trans. Med. Imaging 18 (4), 345–354 (1999).

    Article  Google Scholar 

  2. J. Barreto, J. Roquette, P. Sturm, et al., “Automatic camera calibration applied to medical endoscopy,” in Proc. BMVC 2009 – 20th British Machine Vision Conference (London, 2009), BMVA Press, pp. 1–10.

  3. T. Stehle, M. Hennes, S. Gross, et al., “Dynamic distortion correction for endoscopy systems with exchangeable optics,” in Bildverarbeitung für die Medizin, Ed. by H. P. Meinzer, T.M. Deserno, et al., Informatik aktuell (Springer, Berlin, Heidelberg, 2009), pp. 142–146.

    Chapter  Google Scholar 

  4. M. Gschwandtner, M. Liedlgruber, A. Uhl, and A. Vécsei, “Experimental study on the impact of endoscope distortion correction on computer-assisted celiac disease diagnosis,” in Proc. 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB 2010) (Corfu, Greece, 2010), IEEE, pp. 1–6.

  5. N. A. Kallemeyn, N. M. Grosl, V. A. Magnotta, et al., “Arthroscopic lens distortion correction applied to dynamic cartilage loading,” Iowa Orthop. J. 27, 52–57 (2007).

    Google Scholar 

  6. M. Liedlgruber, A. Uhl, A. Vécsei, “Statistical analysis of the impact of distortion (correction) on an automated classification of celiac disease,” in Proc. 17th International Conference on Digital Signal Processing (DSP 2011) (Corfu, Greece, 2011), IEEE, pp. 1–6.

  7. T. Weibel, C. Daul, D. Wolf, et al. “Graph based construction of textured large field of view mosaics for bladder cancer diagnosis,” Pattern Recogn. 45 (12), 4138–4150 (2012).

    Article  Google Scholar 

  8. T. Bergen, T. Wittenberg, and C. Münzenmayer, “Shading correction for endoscopic images using principal color components,” Int. J. CARS 11 (3), 397–405 (2016).

    Article  Google Scholar 

  9. K. Gono, “Narrow band imaging: technology basis and research and development history,” Clin. Endosc. 48 (6), 476–480 (2015).

    Article  Google Scholar 

  10. K. Togashi, H. Osawa, K. Koinuma, et al., “A comparison of conventional endoscopy, chromoendoscopy, and the optimal-band imaging system for the differentiation of neoplastic and non-neoplastic colonic polyps,” Gastrointest. Endosc. 69 (3), 734–741 (2009).

    Article  Google Scholar 

  11. G. M. Kamphuis, D. M. de Bruin, T. Fallert, et al. “Storz professional image enhancement system: a new technique to improve endoscopic bladder imaging,” J. Cancer Sci. Ther. 8 (3), 71–77 (2016).

    Google Scholar 

  12. B. Lin, Y. Sun, J. Sanchez, and X. Qian, “Vesselness based feature extraction for endoscopic image analysis,” in Proc. 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI’14) (Beijing, China, 2014), IEEE, pp. 1295–1298.

  13. F. Deeba, F. M. Bui, and K. A. Wahid, “Automated GrowCut for segmentation of endoscopic images,” in Proc. 2016 International Joint Conference on Neural Networks (IJCNN) (Vancouver, Canada, 2016), IEEE, pp. 4650–4657.

  14. D. You, S. Antani, D. Demner-Fushman, and G. R. Thoma, “Biomedical image segmentation for semantic visual feature extraction,” in Proc. 2014 IEEE International Conference on Bioinformatics & Biomedicine (BIBM) (Belfast, UK, 2014), IEEE, pp. 289–292.

  15. Z. Xue, D. You, S. Chachra, et al., “Extraction of endoscopic images for biomedical figure classification,” in Medical Imaging 2015: PACS and Imaging Informatics: Next Generation and Innovations, Ed. by T. S. Cook and J. Zhang, Proc. SPIE 9418, 94180P-1– 94180P-13 (2015).

  16. M. Häfner, R. Kwitt, A. Uhl, et al., “Feature extraction from multi-directional multi-resolution image transformations for the classification of zoom-endoscopy images,” Pattern Anal. Appl. 12 (4), 407–413 (2009).

    Article  MathSciNet  Google Scholar 

  17. S. A. Karkanis, D. K. Iakovidis, D. E. Maroulis, et al., “Computer-aided tumor detection in endoscopic video using color wavelet features,” IEEE Trans. Inf. Technol. Biomed. 7 (3), 141–152 (2003).

    Article  Google Scholar 

  18. M. Dhanalakshmi, N. Sriraam, G. Ramya, N. Bhargavi, and V. Tamizhthennagaarasi, “Computer Aided Diagnosis for enteric lesions in endoscopic images using Anfis,” Int. J. Wisdom Based Comput. 2 (1), 1–5 (2012).

    Google Scholar 

  19. H. Takiyama, T. Ozawa, S. Ishihara, et al., “Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks,” Sci. Rep. 8, Article 7497, 1–8 (2018).

    Google Scholar 

  20. G. Wimmer, A. Vécsei, M. Häfner, and A. Uhl, “Fisher encoding of convolutional neural network features for endoscopic image classification,” J. Med. Imaging 5 (3), 034504-1–034504-11 (2018).

    Article  Google Scholar 

  21. S. V. Aksenov, K. A. Kostin, A. V. Ivanova, J. Liang, and A. V. Zamyatin, “An ensemble of convolutional neural networks for the use in video endoscopy,” Modern Technologies in Medicine (Sovremennye Tehnologii v Medicine) 10 (2), 7–17 (2018).

    Google Scholar 

  22. M. H. Laves, J. Bicker, L. A. Kahrs, and T. Ortmaier, “A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation,” Int. J. CARS 14 (3), 483–492 (2019).

    Article  Google Scholar 

  23. Support Worldwide Technical Support and Product Information. IMAQ Vision Concepts Manual (National Instruments Corporation, Austin, TX, 2010).

  24. A. Nedzved, V. Bucha, and S. Ablameyko, “Augmented 3D endoscopy video,” in Proc. 2008 3DTV-Conference: The True Vision – Capture, Transmission and Display of 3D Video (3DTV-CON 2008) (Istanbul, Turkey, 2008), pp. 349–353.

  25. A. Nedzved and S. Ablameyko, “Thinning of gray scale in medical image processing,” Pattern Recogn. Image Anal. 8(3), 436–438 (1998).

    Google Scholar 

  26. V. B. Alexandrov and K. R. Alexandrov, “Laparoscopic technology in colon cancer surgery: thinkings,” Endoskopicheskaya Khirurgiya (Endoscopic Surgery) 4 (3), 4–6 (1998) [in Russian].

    Google Scholar 

  27. A. G. Kriger, B. K. Shurkalin, A. A. Shogenov, and K. E. Rzhebaev, “Laparoscopy in diagnostics of acute appendicitis,” Khirurgiya (Pirogov Russian Journal of Surgery) No. 8, 14–19 (2000) [in Russian].

Download references

ACKNOWLEDGMENTS

Authors express gratitude to doctors A. Gurevich and N. Gurevich from Mogilev Municipal Emergency Hospital for valuable help.

Funding

This work is supported by the National High-end Foreign Experts Program (GDW20183300463) and Joint Fund of Zhejiang Natural Science Foundation Committee and Zhejiang Society of Mathematical Medicine (nos. LSY19F010001, LGJ18F020001, LGJ19F020002), This research was supported by projects of BRFFI F18R-218 “Development and experimental research of descriptive methods for automatisation of biomedical images analysis”.

Author information

Authors and Affiliations

  1. Zhejiang Shuren University, 310015, Hangzhou, China

    Shiping Ye & Fangfang Ye

  2. Belarusian State University, 220030, Minsk, Republic of Belarus

    A. Nedzvedz & S. Ablameyko

  3. United Institute of Informatics Problems of National Academy of Sciences, 220020, Minsk, Republic of Belarus

    A. Nedzvedz & S. Ablameyko

Authors
  1. Shiping Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Nedzvedz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fangfang Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Ablameyko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Shiping Ye, A. Nedzvedz, Fangfang Ye or S. Ablameyko.

Ethics declarations

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest.

COMPLIANCE WITH ETHICAL STANDARDS

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Shiping Ye born in 1967. Professor and Vice President of Zhejiang Shuren University. Graduated from Zhejiang University in 1988. In 2003 he got his master’s degree in Computer Science and Technology from Zhejiang University. His scientific interests include application of computer graphics and image, GIS. He has published more than 60 academic articles. Four research projects he has taken part in have been awarded second prize of Zhejiang Provincial Scientific and Technological Achievement. Two teaching research programs he has presided over have been awarded first prize and second prize of Zhejiang Provincial Teaching Achievement respectively.

Fangfang Ye born in 1980 Anhui province, China. She received her PhD degrees in Control Theory and Control Engineering from Zhejiang University, Hangzhou, China in 2014. Her research interests focus on image segmentation, medical imaging processing and pattern recognition. She is also a lecturer at Zhejiang Shuren University, Hangzhou, China. She has published 5 academic articles.

Sergey Ablameyko born in 1956, DipMath in 1978, PhD in 1984, D.Sc. in 1990, Prof. in 1992. Rector (President) of Belarusian State University from 2008 to 2017 and Professor of BSU from 2017. His scientific interests are: image analysis, pattern recognition, digital geometry, knowledge-based systems, geographical information systems, medical imaging. He has more than 600 publications. He is in Editorial Board of Pattern Recognition and Image Analysis, Supercomputers and many other international and national journals. He is a Fellow of International Association for Patter Recognition, Academician of Belarusian Engineering Academy, Academician of National Academy of Sciences of Belarus, Academician of the European Academy, Russian Academy of Natural Sciences, Russian Space Academy and many others. He was a First Vice-President of International Association for Pattern Recognition IAPR (2006–2008), President of Belarusian Association for Image Analysis and Recognition. He is a Chairman of BSU Academic Council of awarding of PhD and D.Sc. degrees. For his activity he was awarded by State Prize of Belarus (highest national scientific award) in 2002, Belarusian Medal of F. Skoryna, Russian Award of Friendship and many other awards.

Alexander Nedzved. Graduated from the Belarus State University in 1992. He is deputy head of the laboratory of Image processing and recognition of United Institute of Informatics Problems of Belarussian Academy of Sciences. He also works in Belorussian State University and Belorussian state medical university. With workgroup he has created an image analysis system “BIOSCAN” (Minsk) (http://www.itlab.anitex.by/bioscan/), an image analysis system “IMAGEWARP”, cytology image processing system “Cytron”, system of magnetooptical films analysis “Zubr”. Scientific interests are image processing, feature extraction, algorithms of 2D–3D image thinning and segmentation and 3D reconstruction, segmentation of colour images, pattern recognition, mathematical morphology, knowledge-based systems, intelligent software. Member of Belarus Association for Image Analysis and Recognition. Author of more than 100 publications. (http://nedzveda.narod2.ru).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, S., Nedzvedz, A., Ye, F. et al. Segmentation and Feature Extraction of Endoscopic Images for Making Diagnosis of Acute Appendicitis. Pattern Recognit. Image Anal. 29, 738–749 (2019). https://doi.org/10.1134/S1054661819040205

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1054661819040205

Keywords:

Search

Navigation