Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface

Zhang, Ziyan; Liu, Yan; Tian, Jiawei; Liu, Shan; Yang, Bo; Xiang, Longhai; Yin, Lirong; Zheng, Wenfeng

doi:10.3390/s21227570

Cited by 16 publications

(11 citation statements)

References 32 publications

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“…Tracking research on the surface of soft tissue is conducive to the use of surgical robots' high-precision and high-flexibility characteristics. It can perform precise surgical operations in different organs and tissues of the human body [10][11][12]. It is conducive to the recovery and reconstruction of surgical organs and tissues, greatly reduces the danger caused by the shaking of the body during the operation of the surgeon, greatly enhances the doctor's confidence and reduces the surgeon's fatigue, and enhances the safety and effectiveness of the operation.…”

Section: Introductionmentioning

confidence: 99%

Soft Tissue Feature Tracking Based on Deep Matching Network

Lu¹,

Liu²,

Hou³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Research in the field of medical image is an important part of the medical robot to operate human organs. A medical robot is the intersection of multi-disciplinary research fields, in which medical image is an important direction and has achieved fruitful results. In this paper, a method of soft tissue surface feature tracking based on a depth matching network is proposed. This method is described based on the triangular matching algorithm. First, we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm. The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set. After the training, the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame. From this matrix, the inter-frame feature matching results can be obtained. In this way, the inter-frame speckle tracking is completed. On this basis, the results of this method are compared with the matching results based on the convolutional neural network. The experimental results show that the proposed method has higher matching accuracy. In particular, the accuracy of the MNIST handwritten data set has reached more than 90%.

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Section: Introductionmentioning

confidence: 99%

Soft Tissue Feature Tracking Based on Deep Matching Network

Lu¹,

Liu²,

Hou³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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show abstract

“…The RMSE is the square root of the mean square error (MSE). MSE is the ratio of the sum of squares of the voxel value errors between the reconstructed image and the true value image to the number of voxels ( 54 , 55 ), defined as the formula (2):…”

Section: Experiments and Resultsmentioning

confidence: 99%

Sparse Angle CBCT Reconstruction Based on Guided Image Filtering

Yang

Cheng

et al. 2022

Front. Oncol.

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Cone-beam Computerized Tomography (CBCT) has the advantages of high ray utilization and detection efficiency, short scan time, high spatial and isotropic resolution. However, the X-rays emitted by CBCT examination are harmful to the human body, so reducing the radiation dose without damaging the reconstruction quality is the key to the reconstruction of CBCT. In this paper, we propose a sparse angle CBCT reconstruction algorithm based on Guided Image FilteringGIF, which combines the classic Simultaneous Algebra Reconstruction Technique(SART) and the Total p-Variation (TpV) minimization. Due to the good edge-preserving ability of SART and noise suppression ability of TpV minimization, the proposed method can suppress noise and artifacts while preserving edge and texture information in reconstructed images. Experimental results based on simulated and real-measured CBCT datasets show the advantages of the proposed method.

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“…Generally, the transformation model with eight parameters is mainly used in splicing, the matrix transformation with 8 degrees of freedom. Because a pair of registration points can support two equations to establish the relationship, four non-collinear matching pairs are needed to calculate the parameters (Zhang et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Improved Feature Point Pair Purification Algorithm Based on SIFT During Endoscope Image Stitching

Liu

Tian

et al. 2022

Front. Neurorobot.

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Endoscopic imaging plays a very important role in the diagnosis and treatment of lesions. However, the imaging range of endoscopes is small, which may affect the doctors' judgment on the scope and details of lesions. Image mosaic technology can solve the problem well. In this paper, an improved feature-point pair purification algorithm based on SIFT (Scale invariant feature transform) is proposed. Firstly, the K-nearest neighbor-based feature point matching algorithm is used for rough matching. Then RANSAC (Random Sample Consensus) method is used for robustness tests to eliminate mismatched point pairs. The mismatching rate is greatly reduced by combining the two methods. Then, the image transformation matrix is estimated, and the image is determined. The seamless mosaic of endoscopic images is completed by matching the relationship. Finally, the proposed algorithm is verified by real endoscopic image and has a good effect.

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Study on Reconstruction and Feature Tracking of Silicone Heart 3D Surface

Cited by 16 publications

References 32 publications

Soft Tissue Feature Tracking Based on Deep Matching Network

Soft Tissue Feature Tracking Based on Deep Matching Network

Sparse Angle CBCT Reconstruction Based on Guided Image Filtering

Improved Feature Point Pair Purification Algorithm Based on SIFT During Endoscope Image Stitching

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