(46-3) 15 * << * >> * Русский * English * Содержание * Все выпуски

Novel approach of simplification detected contours on X-ray medical images
A.M.S. Al-Temimi 1, V.S. Pilidi 2, M.K.I. Ibraheem 3

General Secretariat for the Council of Ministers, Baghdad, Iraq;
Sothern Fedreal University, Rostov-on-Don, Russia;
University of Mustansiriyah, Baghdad, Iraq

 PDF, 615 kB

DOI: 10.18287/2412-6179-CO-1014

Страницы: 479-482.

Язык статьи: English.

This paper gives description of a method for simplifying the number of points representing detected contours of the bones on digital X-ray images. Such simplification permits simplify way for correction the location of these points in the cases, if the analyzed image has poor quality, and to reduces the time of analysis it to get the reference lines and angles for diagnosis purposes of the area under investigation.

Ключевые слова:
object recognition, digital X-ray image, reference lines and angles, contour simplification, medicine diagnosis system.

Al-Temimi AMS, Pilidi VS, Ibraheem MKI. Novel approach of simplification detected contours on X-ray medical images. Computer Optics 2022; 46(3): 479-482. DOI: 10.18287/2412-6179-CO-1014.


  1. Shivanand SG, Pooja UP, Ramesh RM. Detection of osteoarthritis using knee X-ray image analyses: A machine vision based approach. Int J Comput Appl 2016; 145(1): 20-26.
  2. Gornale SS, Patravali PU, Manza RR. A Survey on Exploration and Classification of Osteoarthritis Using Image Processing Technique. International Journal of Scientific and Engineering 2016; 7(6): 334-355.
  3. Shivpuje VB, Sable GS. A review on digital dental radiographic images for disease identification and classification. Int J Eng Res Appl 2016; 6(7): 38-42.
  4. Candemir S, Jaeger S, Palaniappan K, Musco JP, Singh RK, Xue Z, Karargyris A, Antani S, Thoma G, McDonald CJ. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imaging 2014; 33(2): 577-590.
  5. Ilyasova NYu, Kupriyanov AV, Ustinov AV. Intraocular foreign body characteristics study on the basis of skull radiographical images analysis. Computer Optics 2011; 35(2): 268-274.
  6. Al-Temimi AMS, Pilidi VS. Automating the process of determining the reference lines on the X-ray medical images. Engineering Journal of Don 2017; 1. Source: <http://ivdon.ru/en/magazine/archive/n1y2017/4007>.
  7. Al-Temimi AMS, Pilidi VS. On an algorithm for analyzing the structure of radiographic medical images [In Russian]. University News. North-Caucasian Region. Technical Sciences Series 2018; 1(197): 23-28.
  8. Al-Temimi AMS, Pilidi VS. Improvements of programming methods for finding reference lines on X-Ray images. Computer Optics 2019; 43(3): 397-401.
  9. Solomin LN, Shchepkina EA. Determination of reference lines and angles for the long bones [In Russian]. Saint-Petersburg: “RNIITO imeni R.R. Vredena” Publisher; 2010: 21.
  10. Paley D. Principles of deformity correction. New York: Springer-Verlag; 2005.
  11. Morrey B. The elbow and its disorders. Philadelphia: Saunders; 2000.
  12. Cooke TD, Li J, Scudamore RA. Radiographic assessment of bony contributions to knee deformity. Orthop Clin North Am 1994; 25(3): 387-393.
  13. Chao EY, Neluheni EV, Hsu RW, Paley В. Biomechanics of malalignment. Orthop Clin Noth Am 1994; 25(3): 379-386.
  14. Glimet T, Masse JP, Kuntz D. Obesity and arthritis of the knee. Rev Rhum Mal Osteoartic 1990; 57(3): 207-209.
  15. Al-Temimi AMS. System for the analysis of radiographic images of the knee joint. Certificate of state registration of the computer program No. 2018610378. Date of state registration in the Register of Computer Programs January 10, 2018.

© 2009, IPSI RAS
Россия, 443001, Самара, ул. Молодогвардейская, 151; электронная почта: journal@computeroptics.ru; тел: +7 (846) 242-41-24 (ответственный секретарь), +7 (846) 332-56-22 (технический редактор), факс: +7 (846) 332-56-20