For Chinaware fragments, it is difficult to assemble them directly without considering the wonderful patterns painted on them. Given the simplicity of the Chinaware designs, each object contains similar textures and patterns. Compared to the oddly diverse appliance modeling, the difference between fragments of different Chinaware is small. The fragments are small and the fracture is flat, and there are many potential matching objects. For the Chinaware fragments’ reassembly, most of the work is still done manually. There is little available fully automatic reassembly work, and these approaches are for the reassembly of a single Chinaware. But what reality demands is the reassembly of the multi-Chinaware. Therefore, this article proposes an automatic method, whose strategy is analogous to the manual assembly, to accomplish this complex task. First, segment the contours of fracture surfaces of the fragments by the geometric feature of fracture edge; then, the contours are matched using our proposed multi-scale linear feature descriptor; given the massive fragments of multiple objects, we use the inaccurate matching strategy to build a matching set for each fracture surface contour and perform contour matching in this set. In this article, the fracture surface is segmented using the 2D slope information on the fragment edge. The descriptor proposed in the article uses distance triangle towers and chained angles to describe the “undulations” of the fracture surface. Moreover, the article uses a strict absolute-advantage-principle to reject false matching. In addition, after the initial reconstruction, we will iteratively adjust fragments according to the gap between the fragments to achieve better multi-fragment matching results. In this article, 18 porcelains, a total of 103 pieces, have been tested. Experiments are also carried out for special cases, including fragment reassembly with missing fragments and fragment reassembly with redundant fragments. The experimental results showed the effectiveness of method. However, the types of experimental data we currently have are relatively simple, and there is no way to reassemble the fragments in minutes. We hope to enable faster reassembly of more fragments in the future.

Objective: In this paper, a framework to visualize and model internal fixation plates is presented for computer-aided personalized and minimally invasive curved bone fracture surgery. Methods: We focus on personalized reverse reconstruction of the bone fracture plate based on three-dimensional (3-D) mesh models obtained from a 3-D optical scanner. The steps of the method are as follows. First, principal component analysis and the K-means method are used to reconstruct a Bezier curve (ridge line) of broken bones. Second, based on the geometric shape of the curved broken bones, a capsule projection model of the broken bones is proposed to obtain the feature information of the broken bone sections. Third, the ordering points to identify the clustering structure (OPTICS) method is utilized for preregistration (rough registration). Fourth, a regional self-growth strategy is designed to extract the cross-section points. Fifth, the iterative closest point method is applied for the accurate registration of the fracture surface models. Finally, a personalized internal fixation plate model is reconstructed based on several user points. Results: The internal fixation plate model can be reconstructed according to the patient's bone parameters. Conclusion: Clinicians can use this framework to obtain personalized and accurate internal fixation plate models that effectively represent the broken bones of patients. Via X-ray navigation, the personalized forged plate can be fixed on the target area through a small incision. Significance: This framework provides a reasonable and practicable technical approach for computer-aided minimally invasive curved bone fracture surgery.

As a recent research hot issue, obtaining the accurate 3 D organ models of Visible Human Project (VHP) has many significances. Therefore, how to extract the organ regions of interest (ROI) in the large-scale color slice image data set has become an urgent issue to be solved. In this paper, we propose a method framework based on OneCut algorithm and adjacent image geometric features to continuously extract the main organ regions is proposed. This framework mainly contains two parts: firstly, the OneCut algorithm is used to segment the ROI of target organ in the current image; secondly, the foreground image (obtained ROI) is corroded into several seed points and the background image (other region except for ROI) is refined into a skeleton. Then the obtained seed points and skeleton can be transmitted and mapped onto the next image as the input of OneCut algorithm. Thereby, the serialized slice images can be processed continuously without manual delineating. The experimental results show that the extracted VHP organs are satisfactory. This method framework may provide well technic foundation for other related application.

With the rapid development of computer technology, surgical training, and the digitalized teaching of human body morphology are gaining prominence in medical education. Accurate, true organ models are essential digital material for these computer-assisted systems. However, no direct three-dimensional (3D) true organ model acquisition method currently exists. Thus, the direct extraction of the interested organ models based on the existing Virtual Human Project (VHP) image set is urgently needed. In this paper, a closed-form solution-based volume matting method is proposed. Using a small quantity of graffiti in the foreground and background, target 3D regions can be extracted by closed-form solution computing. The upper triangular storage strategy and the preconditioned conjugate-gradient (PCG) method also promote robustness. Four image data sets (2 virtual human male and 2 virtual human female) from the United States National Library of Medicine (including brain slices, eye slices, lung slices, heart slices, liver slices, kidney slices, spine slices, arm slices, vastus slices, and foot slices) were selected to extract the 3D volume organ models. The experimental results show that the extracted 3D organs were acceptable and satisfactory. This method may provide technical support for medical and other scientific research fields.

BACKGROUND: There is a great demand for the extraction of organ models from three-dimensional (3D) medical images in clinical medicine diagnosis and treatment. OBJECTIVE: We aimed to aid doctors in seeing the real shape of human organs more clearly and vividly. METHODS: The method uses the minimum eigenvectors of Laplacian matrix to automatically calculate a group of basic matting components that can properly define the volume image. These matting components can then be used to build foreground images with the help of a few user marks. RESULTS: We propose a direct 3D model segmentation method for volume images. This is a process of extracting foreground objects from volume images and estimating the opacity of the voxels covered by the objects. CONCLUSIONS: The results of segmentation experiments on different parts of human body prove the applicability of this method.