Automated Inhomogeneity Correction and Fat Extraction in T1-weighted MRI of Long Bones: An Adaptive Disk Structure Element Morphological (ADSEM) Approach for Improved Osteosarcoma Diagnosis and Analysis

• Published in: International Journal of Intelligent Engineering and Systems
• Main Author: Othman M.H.; Meng B.C.C.; Damanhuri N.S.; Aziz M.E.; Othman N.A.
• Format: Article
• Language: English
• Published: Intelligent Network and Systems Society 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184168283&doi=10.22266%2fijies2024.0229.39&partnerID=40&md5=cd117353599d61036e49d6a17729deac

Fat extraction is a crucial aspect of diagnostic analysis in T1-weighted magnetic resonance imaging (MRI) images. However, the accuracy is affected by image inhomogeneity. Inhomogeneity refers to variations in signal intensity across an image, which can be caused by uneven magnetic fields or abnormal fluids in MRI image. This study uses fuzzy C-means (FCM) algorithm for fat region extraction. However, FCM is struggle with regions of similar intensity. The objective of this study is to propose a method for inhomogeneity correction using adaptive disk structure element morphological (ADSEM) approach. This rectifies the impact of inhomogeneity-induced intensity variations. The method is then integrated with FCM for fat extraction. This approach overcome FCM's intensity similarity limitation, enhancing fat extraction accuracy. Comparative assessments highlight the integrated ADSEM-FCM method's superiority over FCM. The quantitative assessment for proposed method in term of accuracy, recall, precision and F1 score is 0.9246, 0.9777, 0.7740, and 0.8526 respectively. © (2024), (Intelligent Network and Systems Society). All Rights Reserved.