Thinning Algorithm Research Articles

Thinning of Balti Script : Way Forward to Balti OCR

Natural language is one of the applications of Artificial Intelligence, which trains machines to do the jobs in human language. OCR is one of the fields where the writing efforts are omitted and text images are converted into editable text. An OCR may have post and preprocessing to enhance the text image more suitable for the rest of the OCR process. Thinning is the preprocessing approach in which the characters, words and text is thinned to its one-pixel skeleton. Much of the work has been done in the various languages of the world as well as Pakistani languages. The work on Balti OCR is nonexistent. In this study, a thinning algorithm is proposed for the Balti language, a language spoken in the northern areas of Pakistan and India. Many of the Balti images were tested with the proposed algorithm and the proposed system produced accurate results by giving a one pixel skeleton of input image. The proposed algorithm tested with hundreds of Balti language images and selected results are presented in this paper. The current research has many directions including the way forward to building Balti OCR, Balti ICR (both segmentation based and segmentation free).

Rebar binding point location method based on improved YOLOv5 and thinning algorithm

In order to solve the problem that the location of rebar binding points is unstable and easy to be affected by environmental noise, this paper proposes a rebar binding point location method based on object depth classification and thinning algorithm. Firstly, YOLOv5 is used to locate the target frame of the rebar binding point, and the Biformer module suitable for small target detection is added to YOLOv5. Secondly, the continuous depth data in the target frame is classified by the depth-first algorithm, and the top level rebar is classified and stored as a binary image, and the Zhang-Suen thinning algorithm is used to obtain the reinforced skeleton. Finally, the skeleton intersection point recognition algorithm is used to obtain the position of the rebar binding point. From the training results, 95 % of the target frames for rebar binding points were recognized. Experiments show that the method can accurately obtain the location of the binding points of the rebar, and can accurately classify the noise, non-target binding points and target binding points, and has high stability for the location of binding points, which is better than YOLOv8-POSE.

A new global daily sea‐surface temperature analysis system at Environment and Climate Change Canada

AbstractA new global daily sea‐surface temperature (SST) analysis system has been developed at Environment and Climate Change Canada (ECCC). All components of the new SST analysis system are implemented within the Modular and Integrated Data Assimilation System (MIDAS) software. MIDAS is already used for the data assimilation component of the main operational numerical weather prediction (NWP) systems at ECCC. The new SST analysis system, integrated together with the global sea‐ice analysis, will be part of the combined ocean surface analysis used for all operational prediction systems at ECCC. The data assimilation method used to compute the new SST analyses is two‐dimensional variational method with a diffusion operator for representing the horizontal background‐error correlations. A new algorithm for satellite data bias estimation has also been developed employing gridded bias estimates computed from a spatial averaging of the differences between collocated satellite and in‐situ data. New algorithms for quality control and thinning of satellite data have also been implemented, making each type of observational dataset more evenly distributed over the globe. The performance of the new SST system is examined relative to the current operational SST system by using independent data. The impact of using the new SST analysis within NWP and ocean prediction systems is also evaluated. When compared with the operational system currently in use, the experiments employing the new SST analysis system produce a nearly neutral impact on the NWP and ocean prediction systems. This validation of the new system is an important first step towards the ability to use MIDAS to perform ensemble‐based three‐dimensional ocean and coupled ocean‐ice–atmosphere data assimilation.

Automatic detection and tracking polar lows from synthetic aperture radar and radiometer observations

ABSTRACT Polar lows are small, high-latitude, intense maritime cyclones and frequently have severe impacts on the ocean such as strong winds, high waves and heavy rainfall. They are difficult to observe and forecast due to their short lifetime (<48 hours), small horizontal scales (200 ~ 1000 km), and the sparse synoptic observing network that exists in the subarctic and Arctic oceans. Previous studies have identified and monitored polar lows by visual analysis of visible and thermal infrared imagery from satellites. However, this manual inspection method is subjective, time-consuming and inevitably involves errors in polar low detections. In this study, we present an automatic objective procedure which we demonstrate by detecting polar lows using spaceborne active synthetic aperture radar (SAR) and passive microwave radiometer observations. Based on the marker-controlled watershed segmentation method and the morphological image thinning algorithm, the centre locations of polar lows are determined using RADARSAT-2 and Sentinel-1A high-resolution SAR images and total atmospheric water vapour content fields from radiometers (AMSR2, SSM/I, GMI, and WindSat). Furthermore, the trajectories of polar lows are constructed, using detected centres from multi-temporal SAR and radiometer observations. Polar low detections are confirmed by high surface wind speeds from SAR, scatterometer, and radiometer data, the presence of cloud vortex signatures visible in MODIS, AVHRR, and VIIRS thermal infrared imagery, as well as the difference between the sea surface temperature and the air temperature at 500 hPa. These results show that the proposed methods have potential to automatically detect and track polar lows from multisensor data. We also estimate the characteristic parameters of detected polar lows. The diameters, translation speeds, and distances travelled are 189 km and 225 km, 8 m/s and 4.9 m/s, and 318 km and 263 km, respectively.

Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing

The identification of branches and bud points is the key to intelligent pruning of dormant grapevine branches and precise positioning of the pruning point on the branch is an important prerequisite for robotic arm pruning. This study takes Cabernet Sauvignon wine grapes as the experimental object and proposes a depth image-based pruning point localization algorithm based on pruning rules. In order to solve the problem of bud recognition in complex backgrounds, this study adopts a detection method that combines semantic segmentation and target detection. Firstly, the semantic segmentation algorithm PSP-net is used to separate the branches and the main stem from the background and the separated image undergoes two kinds of processing: one is to skeletonize it using the Zhang–Suen thinning algorithm and the other is to identify the buds and obtain the center coordinates of the buds using the target-detection method YOLOv5; finally, combining with the depth information of the depth image, we use the coordinates of the buds to determine the location of the pruning point located on the skeleton image. The results show that PSP-net has better results in segmentation performance with mIoU reaching 83.73%. YOLOv5 performs better in target detection with mAP reaching 81.06% and F1 reaching 0.80. The accuracy of this method in determining the location of pruning points reaches 82.35%. It can provide a method for fruit tree pruning robots to determine the location of pruning points.

Skeleton extraction of hard‐pen regular script based on stroke characterization and ambiguous zone detection

AbstractThe Intelligent Evaluation System for Calligraphy Characters (IESCC) is used for teaching calligraphy, and users can learn calligraphy through the modifications given by the system. Chinese character skeleton extraction is an important step in the intelligent evaluation algorithm of calligraphic characters. The skeletons of Chinese characters extracted by traditional refinement algorithms are prone to redundant branches and deformed skeletons, which can lead to skeleton extraction results that do not conform to the topology of the original character. In this study, the focus lies on hard‐pen regular script, and skeleton repair and extraction are performed for these characters. According to the writing characteristics of regular script, the redundant burs are removed and the deformation zone of the thinned skeleton is detected, and then the idea of first splitting is used, then restructuring, to propose a skeleton extraction algorithm based on stroke characterization and ambiguous zone detection for hard‐pen regular script, referred to as SCAD. First, a thinning algorithm is used to extract the skeleton of Chinese characters and remove redundant pixels. By analyzing the stroke characteristics of regular script, the burrs are classified and different conditions are set to detect and remove the burrs. Then the ambiguous zones are detected according to the different kinds of junction points. Then, curvature, stroke width and direction deviation are used to analyze the continuity of stroke segments, and the decision function is used to classify the stroke segments. Finally, the stroke segments with optimal pairings were compensated by interpolation according to the direction trend. This concludes the skeleton extraction. Skeleton extraction is performed on 1000 sample characters, and the SCAD algorithm can extract the skeleton of Chinese characters with an accuracy of up to 98.37%. It is proved that the SCAD method proposed here is a practical and effective method to extract the skeleton of hard‐pen regular script.

Research on Path Planning with the Integration of Adaptive A-Star Algorithm and Improved Dynamic Window Approach

In response to the shortcomings of the traditional A-star algorithm, such as excessive node traversal, long search time, unsmooth path, close proximity to obstacles, and applicability only to static maps, a path planning method that integrates an adaptive A-star algorithm and an improved Dynamic Window Approach (DWA) is proposed. Firstly, an adaptive weight value is added to the heuristic function of the A-star algorithm, and the Douglas–Pucker thinning algorithm is introduced to eliminate redundant points. Secondly, a trajectory point estimation function is added to the evaluation function of the DWA algorithm, and the path is optimized for smoothness based on the B-spline curve method. Finally, the adaptive A-star algorithm and the improved DWA algorithm are integrated into the fusion algorithm of this article. The feasibility and effectiveness of the fusion algorithm are verified through obstacle avoidance experiments in both simulation and real environments.

Thinning Chinese, Korean, Japanese and Thai script for segmentation-free OCRs

While searching on the internet, the OCR keyword will return a thousand research papers on optical character recognition. These papers are ranging from Latin language scripts, Cyrillic, Devanagari, Korean, Japanese, Chinese and Arabic scripts. Sindhi and many other languages extend the Arabic script in which base characters are same while the other characters are adopted in a same situation. Many of the languages possess OCRs for their languages but still there are some other languages which still require the OCRs for their language. The paper is organized in various sections such as introduction followed by Sindhi language characteristics. The OCR approaches and methods are explained. The last section describes the conclusion and future work. An OCR is a set of complex steps to convert image text to editable text. Skeletonization or shrining a word or character body is a method which helps to recognize text more easily. Multiple languages impose various challenges and are hard to recognize and skeletonization or thinning produces a new image which can be easy to recognize. The connected elements are found with this approach. A custom-built software has been developed to interface the generalized thinning algorithm so that the scripts of Chinese, Japanese, Korean and Thai be tested. The output of this algorithm is the final image to be used for the further processing of the OCR. Although the intention was to create algorithms for segmentation free OCRs, the study results and the software can also be used for segmentation-based algorithms. The generalized algorithm shows the accuracy of more than 95% for the experimented four scripts.

Three-dimensional modeling of ancient yiyuan architecture using static laser scanning technology

Abstract The awareness of protecting traditional culture and humanistic landscapes is increasing year by year, and the protection of ancient gardens is becoming more and more a consensus among people. This paper takes the architectural profile of Guyi Garden as the research basis, introduces the specific application of 3D laser scanning technology, plans the scope of architectural data acquisition in Guyi Garden, and designs the corresponding architectural data acquisition program. With respect to the acquired point cloud data of GuYiYuan, the bilateral filtering algorithm is used to reduce the noise of the data, the curvature thinning algorithm is combined to reduce the point cloud data of the buildings in GuYiYuan, the point cloud data are spliced based on the connection point, and the ICP algorithm with the introduction of the optimization of the rotational angle constraints is introduced to realize the alignment of the point cloud data. In addition, the shadows generated from the point cloud data are removed by combining the relighting method, and the linear transformation method is also used to globally correct the texture image generated from the point cloud data. The point cloud density was reduced from 0.95m² to 0.01m² when the thinning rate was increased from 8% to 96%, and the alignment error of the improved ICP algorithm under 25dB Gaussian white noise was only 0.083*10-4mm. The maximum error accuracy of the two-dimensional model of Yiyi Nodang generated based on the point cloud data was only 0.007m, and the error of the three-dimensional model was controlled to be within the range of ±9mm. Accurate architectural data can be obtained through the use of 3D laser scanning technology, leading to a new method for digitally preserving historical buildings.

Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing.

Crack disease is one of the most serious and common diseases in road detection. Traditional manual methods for measuring crack detection can no longer meet the needs of road crack detection. In previous work, the authors proposed a crack detection method for asphalt pavements based on an improved YOLOv5s model, which is a better model for detecting various types of cracks in asphalt pavements. However, most of the current research on automatic pavement crack detection is still focused on crack identification and location stages, which contributes little to practical engineering applications. Based on the shortcomings of the above work, and in order to improve its contribution to practical engineering applications, this paper proposes a method for segmenting and analyzing asphalt pavement cracks and identifying parameters based on image processing. The first step is to extract the crack profile through image grayscale, histogram equalization, segmented linear transformation, median filtering, Sauvola binarization, and the connected domain threshold method. Then, the magnification between the pixel area and the actual area of the calibration object is calculated. The second step is to extract the skeleton from the crack profile images of asphalt pavement using the Zhang-Suen thinning algorithm, followed by removing the burrs of the crack skeleton image using the connected domain threshold method. The final step is to calculate physical parameters, such as the actual area, width, segments, and length of the crack with images obtained from the crack profile and skeleton. The results show that (1) the method of local thresholding and connected domain thresholding can completely filter noise regions under the premise of retaining detailed crack region information. (2) The Zhang-Suen iterative refinement algorithm is faster in extracting the crack skeleton of asphalt pavement, retaining the foreground features of the image better, while the connected-domain thresholding method is able to eliminate the missed isolated noise. (3) In comparison to the manual calibration method, the crack parameter calculation method proposed in this paper can better complete the calculation of crack length, width, and area within an allowable margin of error. On the basis of this research, a windowing system for asphalt pavement crack detection, WSPCD1.0, was developed. It integrates the research results from this paper, facilitating automated detection and parameter output for asphalt pavement cracks.

Evaluation of Electron Tomography Capabilities for Shale Imaging.

Despite the advantageous resolution of electron tomography (ET), reconstruction of three-dimensional (3D) images from multiple two-dimensional (2D) projections presents several challenges, including small signal-to-noise ratios, and a limited projection range. This study evaluates the capabilities of ET for thin sections of shale, a complex nanoporous medium. A numerical phantom with 1.24 nm pixel size is constructed based on the tomographic reconstruction of a Barnett shale. A dataset of 2D projection images is numerically generated from the 3D phantom and studied over a range of conditions. First, common reconstruction techniques are used to reconstruct the shale structure. The reconstruction uncertainty is quantified by comparing overall values of storage and transport metrics, as well as the misclassification of pore voxels compared to the phantom. We then select the most robust reconstruction technique and we vary the acquisition conditions to quantify the effect of artifacts. We find a strong agreement for large pores over the different acquisition workflows, while a wider variability exists for nanometer-scale features. The limited projection range and reconstruction are identified as the main experimental bottlenecks, thereby suggesting that sample thinning, advanced holders, and advanced reconstruction algorithms offer opportunities for improvement.

Ultrasonic measurement of roller revolution speed for high-speed rolling bearing

The sliding state of the bearing rollers can be identified by measuring the roller revolution speed. At present, the roller revolution speed is mainly approximated by the average speed of the cage, which can be obtained by optical and electric methods. However, there are some bottlenecks in the practical application of these methods. In this study, a non-destructive ultrasonic technique is proposed to measure the high-speed rolling bearings' roller revolution speed. In order to reduce the impact of time-domain noise on the ultrasonic echo pulse signal, autocorrelation analysis and the FFT transform are introduced. The passing frequency of a roller can be more accurately calculated by using the CZT (linear frequency modulation Z transform) spectrum thinning algorithm. This ultrasonic approach's validity is confirmed when compared to the conventional optical method at low speed. Then, in accordance with the theoretical models, the roller slippage at high speed is observed by directly measuring the speed of the roller revolution.

Simulation of Thinning by Integrating Tree Competition and Species Biodiversity for Target Tree-Based Management of Secondary Forests

This study presents auxiliary support techniques for tree selection strategies based on the spatial structure indices and three competition indices in secondary forests, and discusses the importance of tree competition in forest management. The spatial structure parameter in the structured management is used as a quantitative index—the uniform angle index and three competition indices are used in the design of the algorithm for selective thinning for secondary forest. Based on the target tree-based management principles, simulation of selective thinning was carried out using GIS and C# programming languages. Data for this study were collected from experimental sample plots at Jilin Wangqing Forestry Bureau in China. The simulation results strongly support the use of auxiliary technology for scientifically selecting trees for thinning, avoiding the subjectivity of the traditional manual selection. Selection is largely based on the uniform angle index and competition index. Hegyi’s competition index and its improved version used in the algorithm provided almost identical simulation results, i.e., thinning intensities suggested by these indices for the first sample plot are 21.8% and 21.5%, respectively, and for the second plot are 21.3% and 21.1%, respectively. Thus, one of these competition indices can be used to select trees for thinning. The comprehensive competition index (CCI, a combination of an improved version of Hegyi’s competition index with tree species mingling) can avoid the selection of individual trees with high mingling and help maintain the tree species diversity. CCI suggests thinning intensities of 18.3% and 18.4% for the first and second sample plots, respectively. Presented methods and results may provide auxiliary supports for scientific thinning and help promote the application of information technology in forest management.

Intelligent recognition and identification of fracture types and parameters for borehole images based on developed convolutional neural networks and post-processing

Borehole imaging is one of the main methods for fracture observation in the rock formations, but there is still a lack of identification systems for deriving fracture parameters from borehole images. Additionally, previous research were limited to sinusoidal fractures. In this study, an intelligent system is developed for multiple types of qualitative fracture identification in a quick and robust manner using convolution neural networks and post-processing. Initially, borehole imaging was carried out in six coal mines to develop a borehole image database that includes a wide variety of rock types and fractures. Afterward, the acquired borehole images of fractures were categorized into three types, such as sinusoidal fractures, vertical fractures and broken areas. In this connection, dual convolutional neural networks were developed to recognize fracture type pixels. Meanwhile, the calculation models associated with corresponding fracture parameters were established to quantitatively describe the fracture characteristics. Eventually, the post-processing programs were developed to identify the geometric parameters for each fracture type. The results indicate that the developed dual convolutional neural networks has the capability to detect fracture pixels with a recognition accuracy of 77.1% for each sinusoidal and vertical fracture pixels, and recognication accuracy of 72.9% for broken area pixels. The post-processing program was developed based on Hough transform and a thinning algorithm, that enables it to the the average recognition rate of 88.71% with an average false alarm rate of 16.67% for all three types of fractures.

Extraction of farmland shelterbelts from remote sensing imagery based on a belt-oriented method

IntroductionFarmland shelterbelts play a positive role in ensuring food security and ecological safety. The absence or degradation of shelterbelt structures can lead to fragmentation of the remotely extracted results. Conversely, shelterbelt maintenance and management system considers these shelterbelts as entire units, even if they are divided into several parts by the gaps in them. It is essential to propose a remote extraction method to fill in fragmented results and accurately represent the distribution of farmland shelterbelts.MethodsIn this study, random forest algorithm was employed to classify land cover from ZY-3 (ZiYuan-3 satellite from China) imagery. Then, a thinning algorithm of mathematical morphology was applied to extract farmland shelterbelts, and the straight-line connection algorithm was used to connect central lines belonging to the same belt. Finally, the result was validated using nine uniformly distributed training sample areas across the entire region.Results and discussionThis method achieved a correct identification rate of 94.9% within the training areas. Among the different regions, the highest identification accuracy recorded was 98.4% and the lowest was 87.7%. In conjunction with cropland information and the shape index of forest patches, it was possible to remove information for non-farmland shelterbelts without introducing external information. This approach achieved a more refined extraction of forestland information. The combination of the thinning algorithm and straight-line connection algorithm addressed the issue of fragmented results in farmland shelterbelt extraction, compensating for the limitations of relying solely on mathematical morphology for belt connectivity. The research method can provide technical support for the monitoring and management of farmland shelterbelts.

Reconstruct the AMSR-E/2 thin ice thickness algorithm to create a long-term time series of sea-ice production in Antarctic coastal polynyas

This study presented an extended time series of ice production in Antarctic coastal polynyas for 20 years of 2002–21 using high spatial resolution satellite data from passive microwave sensors: the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). For this, we reconstructed the previously presented thin ice thickness algorithms and re-estimated ice production by replacing atmospheric input data for the heat flux calculations with the newly released ECMWF Reanalysis v5 (ERA5). The consistency of ice production between AMSR-E and AMSR2, whose operation periods do not overlap, was confirmed from comparisons with ice production by the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) with relatively coarse spatial resolution. The open ocean area, which cannot be detected from the thin ice thickness algorithm, was defined using sea-ice concentration (SIC) from AMSR-E and AMSR2. The satellite derived SIC has been suggested to be underestimated in the new thin ice (polynya) area. The effect of the underestimation on the ice production estimation was evaluated. The underestimation of SIC from three major algorithms was also investigated from comparisons with the thin ice thickness presented in this study. The coastal polynya dataset covering 20 years with the higher spatial resolution presented in this study is vital for climate-change-related studies.

Defects Detection in Printed Circuit Boards by Separation and Comparison of Chains

The known K-means clustering, flood-filling, and thinning algorithms are used to find coordinates of contacts in PCB images. Images of different types and colors are considered. The clustering algorithm is used to reduce the number of colors, to get uniform colors in the PCB image. The thinning algorithm is used to build skeletons and find pixels of contacts. The flood-fill algorithm is used to mark and separate chains, defect connectivity, and metal defects. Different subtraction operations are applied to original, transformed, and distributed cumulative histogram images.

Testing a Method Based on an Improved UNet and Skeleton Thinning Algorithm to Obtain Branch Phenotypes of Tall and Valuable Trees Using Abies beshanzuensis as the Research Sample.

Sudden changes in the morphological characteristics of trees are closely related to plant health, and automated phenotypic measurements can help improve the efficiency of plant health monitoring, and thus aid in the conservation of old and valuable tress. The irregular distribution of branches and the influence of the natural environment make it very difficult to monitor the status of branches in the field. In order to solve the problem of branch phenotype monitoring of tall and valuable plants in the field environment, this paper proposes an improved UNet model to achieve accurate extraction of trunk and branches. This paper also proposes an algorithm that can measure the branch length and inclination angle by using the main trunk and branches separated in the previous stage, finding the skeleton line of a single branch via digital image morphological processing and the Zhang-Suen thinning algorithm, obtaining the number of pixel points as the branch length, and then using Euclidean distance to fit a straight line to calculate the inclination angle of each branch. These were carried out in order to monitor the change in branch length and inclination angle and to determine whether plant branch breakage or external stress events had occurred. We evaluated the method on video images of Abies beshanzuensis, and the experimental results showed that the present algorithm has more excellent performance at 94.30% MIoU as compared with other target segmentation algorithms. The coefficient of determination (R2) is higher than 0.89 for the calculation of the branch length and inclination angle. In summary, the algorithm proposed in this paper can effectively segment the branches of tall plants and measure their length and inclination angle in a field environment, thus providing an effective method to monitor the health of valuable plants.

Prior skeleton based online deep reinforcement learning for coronary artery centerline extraction.

Coronary centerline extraction is an essential technique for X-ray coronary angiography (XCA) image analysis, which provides qualitative and quantitative guidance for percutaneous coronary intervention (PCI). In this paper, an online deep reinforcement learning method for coronary centerline extraction is proposed based on the prior vascular skeleton. Firstly, with XCA image preprocessing (foreground extraction and vessel segmentation) results, the improved ZhangSuen image thinning algorithm is used to rapidly extract the preliminary vascular skeleton network. On this basis, according to the spatial-temporal and morphological continuity of the angiography image sequence, the connectivity of different branches is determined using k-means clustering, and the vessel segments are then grouped, screened, and reconnected to obtain the aorta and its major branches. Finally, using the previous results as prior information, an online Deep Q-Network (DQN) reinforcement learning method is proposed to optimize each branch simultaneously. It comprehensively considers grayscale intensity and eigenvector continuity to achieve the combination of data-driven and model-driven without pre-training. Experimental results on clinical images and the third-party dataset demonstrate that the proposed method can accurately extract, restructure, and optimize the centerline of XCA images with a higher overall accuracy than the existing state-of-the-art methods.

Determination of intramedullary nail based on centerline adaptive registration.

Objective: Internal fixation with intramedullary nails is a gold standard for the treatment of femoral shaft fractures. However, both the mismatch between intramedullary nails and the medullary cavity and inaccurate positioning of entry points will lead to deformation of intramedullary nails after implantation. The study aimed to determine a suitable intramedullary nail with an optimal entry point for a specific patient based on centerline adaptive registration. Method: A homotopic thinning algorithm is employed to extract centerlines of the femoral medullary cavity and the intramedullary nail. The two centerlines are registered to obtain a transformation. The medullary cavity and the intramedullary nail are registered based on the transformation. Next, a plane projection method is employed to calculate the surface points of the intramedullary nail laid outside the medullary cavity. According to the distribution of compenetration points, an iterative adaptive registration strategy is designed to decide an optimal position of the intramedullary nail in medullary cavity. The isthmus centerline is extended to the femur surface, where the entry point of the intramedullary nail is located. The suitability of an intramedullary nail for a specific patient was calculated by measuring the geometric quantities reflecting the interference between the femur and nail, and the suitability values of all nails are compared and the most suitable one is determined. Results: The growth experiment indicated that the bone to nail alignment is indeed affected by the extension of the isthmus centerline, including the extension direction and velocity. The geometrical experiment showed that this method could find the best registration position of intramedullary nails and select the optimal intramedullary nail for a specific patient. In the model experiments, the determined intramedullary nail could be successfully placed into the medullary cavity through the optimal entry point. A pre-screening tool to determine nails which can be successfully used has been given. In addition, the distal hole was accurately located within 14.28s. Conclusion: These results suggest that the proposed method can select a suitable intramedullary nail with an optimal entry point. The position of the intramedullary nail can be determined in the medullary cavity, while deformation is avoided. The proposed method can determine the largest diameter intramedullary nail with as little damage to the intramedullary tissue as possible. The proposed method provides preparation aid for internal fixation with intramedullary nails guided by navigation systems or extracorporeal aimers.