Next Article in Journal
Ethical AI in Financial Inclusion: The Role of Algorithmic Fairness on User Satisfaction and Recommendation
Previous Article in Journal
AHEAD: A Novel Technique Combining Anti-Adversarial Hierarchical Ensemble Learning with Multi-Layer Multi-Anomaly Detection for Blockchain Systems
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Hybrid Segmentation Algorithm for Rheumatoid Arthritis Diagnosis Using X-ray Images

by
Govindan Rajesh
1,*,
Nandagopal Malarvizhi
1 and
Man-Fai Leung
2
1
Department of Computer Science & Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai 600062, India
2
School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB1 1PT, UK
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2024, 8(9), 104; https://doi.org/10.3390/bdcc8090104
Submission received: 23 July 2024 / Revised: 20 August 2024 / Accepted: 24 August 2024 / Published: 2 September 2024

Abstract

Rheumatoid Arthritis (RA) is a chronic autoimmune illness that occurs in the joints, resulting in inflammation, pain, and stiffness. X-ray examination is one of the most common diagnostic procedures for RA, but manual X-ray image analysis has limitations because it is a time-consuming procedure and is prone to errors. A specific algorithm aims to a lay stable and accurate segmenting of carpal bones from hand bone images, which is vitally important for identifying rheumatoid arthritis. The algorithm demonstrates several stages, starting with Carpal bone Region of Interest (CROI) specification, dynamic thresholding, and Gray Level Co-occurrence Matrix (GLCM) application for texture analysis. To get the clear edges of the image, the component is first converted to the greyscale function and thresholding is carried out to separate the hand from the background. The pad region is identified to obtain the contours of it, and the CROI is defined by the bounding box of the largest contour. The threshold value used in the CROI method is given a dynamic feature that can separate the carpal bones from the surrounding tissue. Then the GLCM texture analysis is carried out, calculating the number of pixel neighbors, with the specific intensity and neighbor relations of the pixels. The resulting feature matrix is then employed to extract features such as contrast and energy, which are later used to categorize the images of the affected carpal bone into inflamed and normal. The proposed technique is tested on a rheumatoid arthritis image dataset, and the results show its contribution to diagnosis of the disease. The algorithm efficiently divides carpal bones and extracts the signature parameters that are critical for correct classification of the inflammation in the cartilage images.
Keywords: rheumatoid arthritis; X-ray imaging; carpal bone segmentation; dynamic thresholding; texture analysis rheumatoid arthritis; X-ray imaging; carpal bone segmentation; dynamic thresholding; texture analysis

Share and Cite

MDPI and ACS Style

Rajesh, G.; Malarvizhi, N.; Leung, M.-F. A Hybrid Segmentation Algorithm for Rheumatoid Arthritis Diagnosis Using X-ray Images. Big Data Cogn. Comput. 2024, 8, 104. https://doi.org/10.3390/bdcc8090104

AMA Style

Rajesh G, Malarvizhi N, Leung M-F. A Hybrid Segmentation Algorithm for Rheumatoid Arthritis Diagnosis Using X-ray Images. Big Data and Cognitive Computing. 2024; 8(9):104. https://doi.org/10.3390/bdcc8090104

Chicago/Turabian Style

Rajesh, Govindan, Nandagopal Malarvizhi, and Man-Fai Leung. 2024. "A Hybrid Segmentation Algorithm for Rheumatoid Arthritis Diagnosis Using X-ray Images" Big Data and Cognitive Computing 8, no. 9: 104. https://doi.org/10.3390/bdcc8090104

APA Style

Rajesh, G., Malarvizhi, N., & Leung, M.-F. (2024). A Hybrid Segmentation Algorithm for Rheumatoid Arthritis Diagnosis Using X-ray Images. Big Data and Cognitive Computing, 8(9), 104. https://doi.org/10.3390/bdcc8090104

Article Metrics

Back to TopTop