## 3 Results
### ROI Detection/Segmentation  
Image segmentation is an essential task in image processing, allowing similar pixels to be identified and organized into homogeneous regions. Therefore, the selection of appropriate segmentation techniques and parameters is a challenge to achieving high-quality results. [PERSON] et al. [4] used the Active Shape Model (ASM) for ROI identification. In this approach, 43 anatomical points used to describe the shape of the area of interest were manually placed in each training image to specify the femoral and tibial forms. Then, the shape model was generated using principal component analysis (PCA). In 2019, [PERSON] et al. [12] employed a semi-automated segmentation method to extract the ROIs of tibial trabecular bone from X-ray images. Initially, four points on the tibial edge were manually marked. Subsequently, three ROIs were extracted by connecting the anatomical markers using a uniform size of 128 \(\times\) 128 pixels. A vertical adjustment was conducted, with the upper limits of the medial and lateral compartments positioned below \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Description** & **Labeled Images** & **Availability** \\ \hline OAI & \(\bullet\) Osteoarthritis Initiative (OAI) dataset: Multicenter, ten-year observational study of 4796 participants & 4446 (KL and OARSI) & [[https://nda.nih.gov/oai/](https://nda.nih.gov/oai/)]([https://nda.nih.gov/oai/](https://nda.nih.gov/oai/)) (accessed on 5 January 2023) \\  & \(\bullet\) Multicenter Osteoarthritis Study (MOST): PA and lateral knees of 2026 participants (Men and Women average age = 50–79 years) & 2920(KL) & [[https://most.ucsf.edu](https://most.ucsf.edu)]([https://most.ucsf.edu](https://most.ucsf.edu)) (accessed on 5 January 2023) \\  & \(\bullet\) Fixed-flexion digital knee X-ray images collected from various Karnataka hospitals, in India (known as Medical-expert) & 1650 (KL) & contact authors \\  & \(\bullet\) 728 limbs with gait analysis data of & 215 (KL) & contact authors \\ [PERSON] et al. [7] & 364 participants (men and women age \(\geq\) 20 years) collected from Seoul National University Hospital (Korea) from 2013 to 2017 & 215 (KL) & contact authors \\ [PERSON] et al. [8] & \(\bullet\) 6103 radiographic exams collected from Dandery University Hospital, Stockholm, Sweden & 6403 (KL) & contact authors \\  & \(\bullet\) Fixed-flexion PA knee X-ray images collected from an outpatient clinic at a large academic joint arthroplasty practice, in Arizona, USA & 4755 (IKDC) & contact authors \\  & (from 2016 to 2019) & \(\bullet\) 81 subjects, collected from Oulu University Hospital, Finland & 370 (KL) & ClinicalTrials.gov, ID: NCT02937064 \\  & \(\bullet\) 2378 participants of the Chinese People’s & 2579 (KL) & contact author \\ [PERSON] et al. [11] & \(\bullet\) & \(\bullet\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Description of the most commonly used datasets. the tibial edge's smallest point automatically. Finally, the horizontal adjustment of the ROI position is defined as the center between the ends of the knee. [PERSON] et al. [13] have applied BoneFinder software to segment the edge of the femur and tibia in each bilateral knee radiograph. Then, both the contrast and histogram intensity were normalized, and the DICOM image was converted to eight bits. Then, the resulting image was centered on 110 \(\times\) 110 mm and resized to 300 \(\times\) 300 pixels with 0.37 mm as pixel spacing. BoneFinder was also used by [PERSON] and [PERSON] [14] for ROI extraction from the right and left knees. In this method, each knee image was turned to align the tibial plates horizontally. Then, histogram clipping and overall contrast normalization were applied to the resulting images. Finally, the image was resized to 310 \(\times\) 310 pixels (0.45 mm resolution) using bilinear interpolation. In 2020, BoneFinder was employed in another study by [PERSON] et al. [15] to implement an adaptive segmentation method for the detection of adaptive regions of interest (ROIs).