Algorithms for Automatic Analysis of Human Foot Radiographic Images

Yusupov Ozod  Rabbimovich; Abdieva Khabiba  Sobirovna; Davronova Oybarchin  Murodovna; Qo‘ziyeva Nazokat  Ilhomjon qizi

Authors

Yusupov Ozod Rabbimovich Docent of Samarkand State University named after Sharof Rashidov
Abdieva Khabiba Sobirovna Docent of Samarkand State University named after Sharof Rashidov
Davronova Oybarchin Murodovna A master’s student of Samarkand State University named after Sharof Rashidov
Qo‘ziyeva Nazokat Ilhomjon qizi A master’s student of Samarkand State University named after Sharof Rashidov

Keywords:

human foot, x-ray images, noise, enhancement, segmentation

Abstract

This article provides an overview of algorithms for processing human foot X-ray images, which are essential for diagnosing various foot conditions, including fractures, deformities, and joint diseases. The study explores several image preprocessing techniques, such as detecting structural changes, noise reduction, and contrast enhancement, all of which help improve the quality of radiographic images and increase diagnostic accuracy. In addition, the paper discusses challenges related to noise, distortions, and low contrast in X-ray images, and outlines methods to mitigate these issues. By implementing these algorithms, the study aims to enhance the effectiveness of foot-related diagnoses and support more efficient medical decision-making.

References

S. Myint, A. S. Khaing, and H. M. Tun, "Detecting Leg Bone Fracture in X-ray Images," Int. J. Sci. & Res., vol. 5, Jun. 2016, pp. 140-144.

V. D. Vegi, S. L. Patibandla, S. S. Kavikondala, and Z. Basha, "Computerized Fracture Detection System using X-ray Images," Int. J. Control Theory Appl., vol. 9, Nov. 2016, pp. 615-621.

S. K. Mahendran and S. Santhosh, "An Enhanced Tibia Fracture Detection Tool Using Image Processing and Classification Fusion Techniques in X-Ray Images," Global J. Comput. Sci. Technol., vol. 11, Aug. 2011, pp. 27-28.

S. K. Mahendran and S. Santhosh Baboo, "Ensemble Systems for Automatic Fracture Detection," Int. J. Eng. Technol. (JACSIT), vol. 4, Feb. 2012, pp. 7-10.

Z. Al-Ameen, G. Sulong, A. Rehman, A. Al-Dhelaan, T. Saba, and M. Al-Rodhaan, "An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization," EURASIP J. Adv. Signal Process., vol. 2015, no. 32, pp. 2-12, 2015. doi: 10.1186/s13634-015-0214-1.

C. Albon, Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning, 2018.

J. Arévalo Pérez, F. Gragera Torres, A. Marín Toribio, L. Koren Fernández, C. Hayoun, and I. Daimiel Naranjo, "Angio CT assessment of anatomical variants in renal vasculature: its importance in the living donor," Insights Imaging, vol. 4, no. 2, pp. 199-211, Apr. 2013.

B. Z., H. Z., X. P., S. Fidler, A. Barriuso, and A. Torralba, "Scene parsing through ADE20K dataset."

V. Badrinarayanan, A. Kendall, and R. Cipolla, "SegNet: A deep convolutional encoder-decoder architecture for image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 12, pp. 2481–2495, Dec. 2017.

B. P. and C. Botha, Image Analysis for Medical Visualization in Visual Computing for Medicine (2nd ed.), 2014.

N. Burningham, Z. Pizlo, and J. Allebach, "Image Quality Metrics," in Encyclopedia of Imaging Science and Technology, J. P. Hornak, Ed., New York: Wiley, 2002, pp. 38.

J. F. Canny, "Finding edges and lines in images," Master's thesis, MIT AI Lab., TR-720.

S. K. Mahendran and S. Santhosh, "Automatic Fracture Detection in X-Ray Images Using Image Processing and Classification Fusion Techniques," Global J. Comput. Sci. Technol., vol. 11, no. 2, Aug. 2011, pp. 15-20.

F. Gragera, J. Marín, and A. Koren, "Advanced segmentation techniques in medical imaging: a comprehensive review," Int. J. Imaging Systems Technol., vol. 30, no. 1, pp. 89-102, Jan. 2019.

P. L. D. Silva and L. S. Coutinho, "Fracture detection and classification in X-ray images using deep convolutional networks," IEEE Trans. Biomed. Eng., vol. 66, no. 11, pp. 3141-3153, Nov. 2019.