MRI T1-Weighted Fat Segmentation Based on Active Contour for Osteosarcoma Patients

Osteosarcoma (OS) is the most common and frequent primary bone cancer in children and adolescents. This type of bone cancer often developed at the long bones’ extremities close to the metaphyseal growth plates. To address these issues, OS quantitative analysis is performed using Magnetic Resonance Imaging (MRI) to plan surgical procedures and track the effectiveness of treatment. Fat suppression is commonly employed to eliminate the fat signal from T1-Weighted MRI sequence. The suppressed fat signal in T1-Weighted is important and often used to identify abnormalities in other MRI sequences. In clinical, MRI images must be interpreted by a radiologist. Manually outlining the tumour location is laborious and subjective. Image processing involves segmentation to separate information from the desired target region of the image. Active Contour (AC) is an algorithm often used in medical images for segmentation. Nevertheless, the process of mask initialization can be challenging and requires careful attention. This study proposes a method to define the mask initialization so that the AC has a starting point to extract fat in T1-Weighted for the purpose of fat suppression. From the results obtained, AC with the proposed mask initialization method successfully segmented the fat in T1-Weighted MRI images with accuracy, precision, recall and F1-score of 0.92, 0.86, 0.88 and 0.89 respectively.

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Publication Details

Journal
Journal of Advanced Research in Applied Sciences and Engineering Technology
Published
2024-08-05
DOI
https://doi.org/10.37934/araset.49.2.114
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

MRI T1-Weighted Fat Segmentation Based on Active Contour for Osteosarcoma Patients

Mohd Ezane Aziz, Nor Azlan Othman, Mohamad Haizan Othman, Nor Salwa Damanhuri et al.
Journal of Advanced Research in Applied Sciences and Engineering Technology
Radiomics and Machine Learning in Medical Imaging
article

MRI T1-Weighted Fat Segmentation Based on Active Contour for Osteosarcoma Patients

Mohd Ezane Aziz, Nor Azlan Othman, Mohamad Haizan Othman, Nor Salwa Damanhuri, Muhammad Shauqi Muhammad Shukor, Belinda Chong Chiew Meng
article en

Abstract

Osteosarcoma (OS) is the most common and frequent primary bone cancer in children and adolescents. This type of bone cancer often developed at the long bones’ extremities close to the metaphyseal growth plates. To address these issues, OS quantitative analysis is performed using Magnetic Resonance Imaging (MRI) to plan surgical procedures and track the effectiveness of treatment. Fat suppression is commonly employed to eliminate the fat signal from T1-Weighted MRI sequence. The suppressed fat signal in T1-Weighted is important and often used to identify abnormalities in other MRI sequences. In clinical, MRI images must be interpreted by a radiologist. Manually outlining the tumour location is laborious and subjective. Image processing involves segmentation to separate information from the desired target region of the image. Active Contour (AC) is an algorithm often used in medical images for segmentation. Nevertheless, the process of mask initialization can be challenging and requires careful attention. This study proposes a method to define the mask initialization so that the AC has a starting point to extract fat in T1-Weighted for the purpose of fat suppression. From the results obtained, AC with the proposed mask initialization method successfully segmented the fat in T1-Weighted MRI images with accuracy, precision, recall and F1-score of 0.92, 0.86, 0.88 and 0.89 respectively.

Journal of Advanced Research in Applied Sciences and Engineering TechnologyVol. 49(2)
Hospital Universiti Sains Malaysia (MY), Universiti Teknologi MARA (MY)
Good health and well-being
Openalex Percentile: Top 82%
Radiomics and Machine Learning in Medical Imaging
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