Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN: 0976-4127
Online ISSN:
0976-4135


  About JMPT
  DLINE Portal Home
Home
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Publisher
Paper Submission
Subscription
Contact us
 
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy
 
  For Authors
  Guidelines for Contributors
Online Submission
Call for Papers
Author Rights
 
 
RELATED JOURNALS
Journal of Digital Information Management (JDIM)
International Journal of Computational Linguistics Research (IJCL)
International Journal of Web Application (IJWA)

 

 
Journal of Multimedia Processing and Technologies
 

 

Colonoscopy Image Processing using the Structural Entropy
Szilvia Nagy, Brigita Sziová, Levente Solecki and László T. Kóczy
Széchenyi István University H-9026 GyQr Egyetem tér 1, Hungary
Abstract: The fine surface of the bowel and the colorectal polyps reflect the colonoscopy images. This is analogous to the case of combustion engine surface scans, where the grooving and wear can be detected from the fine pattern superposed to a cylinder curvature. While comparing we can found that the colonoscopy images and to have many reflections, whereas the roughness scanners detect small dust particles and well as the vibrations from the environment. We bring a model in this work to take care of both the problems using histogram stretching together with a special type of filtering. Besides we have used the masks to control the effect of the operators. In the testing we have measured the effects of the processing steps on the structural entropy of the image because the structural entropies are used in characterization of the images.
Keywords: Colonoscopy, Roughness, Background Subtraction, Reflection, Outliers, Entropy Colonoscopy Image Processing using the Structural Entropy
DOI:https://doi.org/10.6025/jmpt/2021/12/3/74-80
Full_Text   PDF 737 KB   Download:   109  times
References:

[1] Søreide, K., Nedrebø, B. S., Reite, A., et al. (2009). Endoscopy Morphology, Morphometry and Molecular Markers: Predicting Cancer Risk in Colorectal Adenoma, Expert Rev. Mol. Diagn, 9, 125-137.
[2] Rácz, I., Jánoki, M., Saleh, H. (2010). Colon Cancer Detection by ‘Rendezvous Colonoscopy’: Successful Removal of Stuck Colon Capsule by Conventional Colonoscopy, Case Rep. Gastroenterol., Volume 4, Karger, 2010, p 19–24.
[3] Jass, J. R. (2006). Classification of colorectal cancer based on correlation of clinical, morphological and molecular features, Histopathology, Volume 50, Wiley, 113–130.
[4] Kudo, S., Hirota, S., Nakajima, T., et al. (1994). Colorectal tumours and pit pattern. J Clin Pathol, 47, p 880-885.
[5] Kudo, S., Tamura, S., Nakajima, T., et al. (1996). Diagnosis of colorectal tumorous lesions by magnifying endoscopy. Gastrointest Endosc, 44, 8-14.
[6] Kudo, S., Rubio, C.A., Teixeira, C.R., et al. (2001). Pit pattern in colorectal neoplasia: endoscopic magnifying view. Endoscopy, 33, 367-373.
[7] Bernal, J., Sanchez, F. J., Vilariño. F. (2012). Towards Automatic Polyp Detection with a Polyp Appearance Model, Pattern Recognition, 45, 3166-3182.
[8] Silva, J. S., Histace, A., Romain, O., Dray, X., Granado, B. (2014). Towards embedded detection of polyps in WCE images for early diagnosis of colorectal cancer, Int J Comput Assisted Radiology and Surgery, 9, 283-293.
[9] Bernal, J., Sanchez, F. J., Fernández-Esparrach, G., Gil, D., Rodrígez, C., Vilariño, F. (2015). WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians, Computerized Medical Imaging and Graphics, 43, 99-111.
[10] Nagy, Sz., Lilik, F., Kóczy, L. (2017). Entropy based fuzzy classification and detection aid for colorectal polyps, IEEE Africon 2017, Cape Town, South Africa, 15-17, September.
[11] Nagy, Sz., Sziová, B., Kóczy, L. T. (2018). The effect of image feature qualifiers on fuzzy colorectal polyp detection schemes using KH interpolation - towards hierarchical fuzzy classification of coloscopic still images, accepted for publication at Fuzz IEEE 2018, Rio de Janeiro.
[12] Solecki, L., Nagy, Sz. (2016). Wavelet Analysis and Structural Entropy Based Intelligent Classification Method for Combustion Engine Cylinder Surfaces, In: Proceedings of the 8th European Symposium on Computational Intelligence and Mathematics, ESCIM, 5-8th October 2016, Sofia, 115-120.
[13] Pipek, J., Varga, I., Universal classification scheme for the spatial localization properties of one-particle states in finite dimensional systems, Phys. Rev. A, Volume 46, APS, Ridge NY-Washington DC, 1992, 3148—3164.
[14] Varga, I., Pipek, J. (2003). Rényi entropies characterizing the shape and the extension of the phase space representation of quantum wave functions in disordered systems, Phys. Rev. E, Volume 68, APS, Ridge NY-Washington DC, 026202.
[15] Molnár, L. M., Nagy, Sz., Mojzes, I. (2010). Structural entropy in detecting background patterns of AFM images, Vacuum, Volume 84, Elsevier, Amsterdam, 2010, 179-183.
[16] Bonyár, A., Molnár, L. M., Harsányi, G. Localization factor: a new parameter for the quantitative characterization of surface structure with atomic force microscopy (AFM), MICRON, Volume 43, Elsevier, Amsterdam, 2012, 305-310.


Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|

 

Copyright © 2011 dline.info