Top

Pattern Recognition and Image Analysis

Published in:

01-10-2019 | APPLIED PROBLEMS

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

Authors: Shiping Ye, A. Nedzvedz, Fangfang Ye, S. Ablameyko

Published in: Pattern Recognition and Image Analysis | Issue 4/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article Using the K-Nearest Neighbors Algorithm for Automated Detection of Myocardial Infarction by Electrocardiogram Data Entries

next article Estimation of Blood Flow Velocity in Coronary Arteries Based on the Movement of Radiopaque Agent

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

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).CrossRef

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.

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.CrossRef

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.

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).

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.

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).CrossRef

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

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

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).CrossRef

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).

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).MathSciNetCrossRef

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).CrossRef

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).

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).

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).CrossRef

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).

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).CrossRef

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).

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].

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].

Title: Segmentation and Feature Extraction of Endoscopic Images for Making Diagnosis of Acute Appendicitis
Authors: Shiping Ye
A. Nedzvedz
Fangfang Ye
S. Ablameyko
Publication date: 01-10-2019
Publisher: Pleiades Publishing
Published in: Pattern Recognition and Image Analysis / Issue 4/2019
Print ISSN: 1054-6618
Electronic ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661819040205

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 4/2019

Surface Classification of Damaged Concrete Using Deep Convolutional Neural Network

Application of Graphic and Image Technology in Strong Convective Weather Monitoring and Early-Warning System

Using the K-Nearest Neighbors Algorithm for Automated Detection of Myocardial Infarction by Electrocardiogram Data Entries

Strong-Structural Convolution Neural Network for Semantic Segmentation

Erratum to: Predictive Diagnosis of Glaucoma Based on Analysis of Focal Notching along the Neuro-Retinal Rim Using Machine Learning

Research on Improvement of Stagewise Weak Orthogonal Matching Pursuit Algorithm

Premium Partner