Olayinka Ayodele Jongbo

Work place: Department of Computer Science, Ekiti State University, Ado-Ekiti, Nigeria.

E-mail: yinkutech1@gmail.com


Research Interests: Computational Science and Engineering, Computational Learning Theory, Image Compression, Image Manipulation, Image Processing, Data Structures and Algorithms


Olayinka A. Jongbo obtained both his BSc (Ed) and MSc in Computer Science from the prestigious Ekiti State University, Ado-Ekiti, Ekiti State, Nigeria. His research interest lies within the areas of Machine Learning, Data Mining, Image Processing and Big Data Analytics. He has publications in reputable peer-reviewed journals.

Author Articles
Segmentation of Medical X-ray Bone Image Using Different Image Processing Techniques

By Folasade Olubusola Isinkaye Abiodun Gabriel Aluko Olayinka Ayodele Jongbo

DOI: https://doi.org/10.5815/ijigsp.2021.05.03, Pub. Date: 8 Oct. 2021

Accurate medical image processing plays a crucial role in several clinical diagnoses by assisting physicians in timely treatment of wounds and mishaps. Medical doctors in the hospitals generally rely on examining bone x-ray images based on their expertise, knowledge and  past experiences in determining whether a fracture exist in bone or not. Nevertheless, majority of fractures identification methods using X-rays in the hospitals is beyond human understanding due to variation in different attributes of fracture and complication of bone organization thereby making it difficult for doctors to correctly diagnose and proffer adequate treatment to patient ailments. The need for robust diagnostic image processing techniques for image segmentation for different bone structures cannot be overemphasized. This research implemented different image segmentation techniques on a bone x-ray image in order to identify the most efficient for timely medical diagnosis. Also, the strength and weaknesses of the diverse segmentation techniques were also identified. This will empowered researchers with appropriate knowledge needed to improve and build better image segmentation models which doctors can use in handling complex medical image processing problems. Also, miss rate in bone X-rays that contains multiple abnormalities can be lowered by using appropriate image segmentation techniques thereby improving some of the labor intensive work of medical personnel during bone diagnosis.  MATLAB 9.7.0 programing tool was used for the implementation of the work. The results of X-ray bone segmentation revealed that active contour model using snake model showed the best performance in detecting boundaries and contours of regions of interest when used in segmenting Femur bone image than the other medical image segmentation approaches implemented in the work.

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