## 3 The Proposed Method
### Image Preprocessing Module  
The raw bone stick image data typically carry a significant amount of noise, as shown in Figure 4a. If unprocessed images are directly fed into the model, they not only considerably increase computational overhead but also introduce redundant features. The redundancy of these features could interfere with the model's ability to effectively extract relevant features, thereby increasing the risk of overfitting and negatively affecting the model's generalization performance. Therefore, to effectively extract the key matching features from the images, this paper performs preprocessing on the image dataset. The specific procedure involves segmenting the main informative regions of the image and then cropping the image at the fracture points. The detailed processing steps are as follows:  
1. The main body of the bone stick is segmented using DIS-Net [19], a deep network designed for binary image segmentation. DIS-Net consists of an encoder, an image segmentation module based on U\({}^{2}\) - Net [20], and an intermediate supervision strategy. DIS-Net excels in precisely identifying and separating target objects in high-resolution images. Due to its superior binary classification capability and high-precision segmentation features, DIS-Net demonstrates high applicability in the segmentation of bone stick images. In the experiments presented in this study, we follow DIS-Net's training strategy and fine-tune the network using its pre-trained weights to achieve fine-grained segmentation of bone stick images, ultimately generating high-precision image labels. The segmentation results are shown in Figure 4b. 2. The original bone stick image (Figure 4a) is subjected to a pixel-wise AND operation with the segmented result image (Figure 4b), retaining the pixel values corresponding to the bone stick while setting the background pixel values to 255. This process generates a standardized bone stick image (Figure 4c). The computation is defined in (1), where dst represents the output image, src\({}_{1}\) and src\({}_{2}\) denote the original image and the mask image, respectively, and (i,j) indicates the pixel location. 3. Due to the prevalence of fractures in the bone sticks within the images, not all bone sticks awaiting matching exhibit an intact form. To address this, the present study employs a data augmentation strategy by cropping the fractured regions of the bone sticks. Furthermore, additional cropping is applied to the main body of the bone sticks to exclude areas unrelated to the fractured portions, thereby preserving critical feature information essential for bone stick matching. As illustrated in Figure 4d, the resulting cropped images are more tightly focused on the target regions, which enhances the model's ability to recognize the key features of the bone sticks more effectively. 4. Following the cropping process, white padding is added around the bone stick images with reference to their longer edge, ensuring that the final image dimensions are standardized to 224 \(\times\) 224 pixels. This normalization of image size aligns with the input requirements of the model, as shown in Figure 4e, thereby ensuring data consistency and enhancing the model's processing efficiency. Figure 4: Processing pipeline for bone stick images: (**a**) Original bone stick image. (**b**) Image segmented using DIS-Net. (**c**) Image with background removed. (**d**) Image cropped to retain fractured regions. (**e**) Image resized to 224 \(\times\) 224 pixels. To ensure the acquisition of a high-quality dataset for the bone stick image matching system and to enhance both the efficiency and accuracy of the matching process, a semi-automated mechanism is employed in the image preprocessing pipeline. Data preprocessing steps include image segmentation, noise removal, and cropping to retain the relevant fracture regions. Background removal is performed using DIS-Net to accurately isolate the bone stick fragments.