Abstract
Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly describes the challenges faced by segmentation techniques from magnetic resonance images followed by an overview of diverse categories of segmentation approaches. The review paper also focuses on automatic approaches and semiautomatic approaches which are extensively used with performance metrics and sufficient achievement for clinical trial assistance. Furthermore, the results of different approaches related to MR sequences used to image the knee tissues and future aspects of the segmentation are discussed.
Highlights
Arthritis is one of the serious, prevalent joint diseases that cause disability and health issues in a large population.is arthritis is categorized with progressive degradation of joint tissues with a variety of abnormalities [1] and is a serious issue in recent years
Osteoarthritis is the damage of joint cartilage and leads to damage of functionality in the knee and hips, and the early signs can be observed with tears in cartilage
Rheumatoid arthritis is an autoimmune disease that largely affects the soft tissues around joints, bones, and cartilage. erefore, the thickness and volume of the knee joint are the important parameters to evaluate rheumatoid arthritis
Summary
Arthritis is one of the serious, prevalent joint diseases that cause disability and health issues in a large population. Is process of segmentation is important in providing the information about knee structure and the progress of the disease to the radiologist for diagnosis. This is a perilous and complex task for numerous reasons such as irregular shape, size, and connecting tissues. Erefore, many studies have focused on the progress of different methods to segment the knee magnetic resonance images [4, 5]. Us, this scientific review provides a comprehensive knowledge of different computational methods utilized for the segmentation of magnetic resonance images. Articles that are written in English and focused on rheumatoid arthritis disease are selected for research.
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