scholarly journals Research on Panoramic Stitching Algorithm of Lateral Cranial Sequence Images in Dental Multifunctional Cone Beam Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2200
Author(s):  
Junyuan Liu ◽  
Xi Li ◽  
Siwan Shen ◽  
Xiaoming Jiang ◽  
Wang Chen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Cone Beam Computed Tomography ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Block Matching ◽  
Cone Beam ◽  
Fusion Algorithm ◽  
Panoramic Images

In the design of dental multifunctional Cone Beam Computed Tomography, the linear scanning strategy not only saves equipment cost, but also avoids the demand for patients to be repositioned when acquiring lateral cranial sequence images. In order to obtain panoramic images, we propose a local normalized cross-correlation stitching algorithm based on Gaussian Mixture Model. Firstly, the Block-Matching and 3D filtering algorithm is used to remove quantum and impulse noises according to the characteristics of X-ray images; Then, the segmentation of the irrelevant region and the extraction of the region of interest are performed by Gaussian Mixture Model; The locally normalized cross-relation is used to complete the registration with the multi-resolution strategy based on wavelet transform and Particle Swarm Optimization algorithm; Finally, image fusion is achieved by the weighted smoothing fusion algorithm. The experimental results show that the panoramic image obtained by this method has significant performance in both subjective vision and objective quality evaluation and can be applied to preoperative diagnosis of clinical dental deformity and postoperative effect evaluation.

Feature Selection Based on Gaussian Mixture Model Clustering for the Classification of Pulmonary Nodules Based on Computed Tomography

2020 ◽  
Vol 10 (5) ◽  
pp. 1033-1039
Author(s):  
Huihong Duan ◽  
Xu Wang ◽  
Xingyi He ◽  
Yonggang He ◽  
Litao Song ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Feature Selection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Pulmonary Nodules ◽  
Gaussian Mixture ◽  
Local Optimum ◽  
Computer Aided ◽  
Aided Diagnosis

Background: In the pulmonary nodules computer aided diagnosis systems (CAD), feature selection plays an important role in reducing the false positive rate and improving the system accuracy. To solve the problem of feature selection techniques by which the diversity of features was damaged in the process of distinguishing malignant pulmonary nodules from benign pulmonary nodules, this study developed a novel feature selection algorithm for improving the accuracy of traditional computer-aided differential diagnosis for benign and malignant classification of pulmonary nodules. Method: Firstly, we divided the extracted features of nodules into several groups by using Gaussian mixture model (GMM). Secondly, we applied Relief and sequential forward selection (SFS) algorithm to find local optimum features dataset for each group. Afterwards, we used the optimumpath forest (OPF) classifier with the found features dataset to obtain the classification results. Finally, the local optimum features dataset with the highest area under curve AUC in all groups were added into the final selected set. Results: According to collected pulmonary nodules on computed tomography (CT) scans, tested with two set of samples, we achieved an average accuracy of 89.5%, sensitivity of 87.1% and specificity of 90.9% on the first set of samples, and 90.1%, 88.7% and 92.1% on the second set of samples. The areas under the receiver operating characteristic (ROC) curves based on these two sample sets were 95.2%, and 96.3% respectively. Conclusions: This study shows that the proposed method was promising for improving the pulmonary nodules computer aided diagnosis systems performance of benign and malignant pulmonary nodules.

Algorithm of Moving Vehicle Detection by Improved Gaussian Mixture Model Based on ROI

2015 ◽  
Vol 734 ◽  
pp. 463-467 ◽  
Author(s):  
Pan Pan Zhang ◽  
Chun Yang Mu ◽  
Xing Ma ◽  
Fu Lu Xu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Background Model ◽  
Morphological Filter ◽  
Improved Method ◽  
Moving Vehicle ◽  
Model Based ◽  
Moving Vehicle Detection

Detection of moving object is a hot topic in computer vision. Traditionally, it is detected for every pixel in whole image by Gaussian mixture background model, which may waste more time and space. In order to improving the computational efficiency, an advanced Gaussian mixture model based on Region of Interest was proposed. Firstly, the solution finds out the most probably region where the target may turn up. And then Gaussian mixture background model is built in this area. Finally, morphological filter algorithm is used for improving integrity of the detected targets. Results show that the improved method could have a more perfect detection but no more time increasing than typical method.

scholarly journals Region-of-interest cone beam computed tomography (ROI CBCT) with a high resolution CMOS detector

2015 ◽  
Author(s):  
A. Jain ◽  
H. Takemoto ◽  
M. D. Silver ◽  
S. V. S. Nagesh ◽  
C. N. Ionita ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Cone Beam Computed Tomography ◽  
Region Of Interest ◽  
Cone Beam

scholarly journals Gaussian Mixture Model for MRI Image Segmentation to Build a Three-Dimensional Image on Brain Tumor Area

MATEMATIKA ◽  
2020 ◽  
Vol 36 (3) ◽  
pp. 217-234
Author(s):  
Anindya Apriliyanti Pravitasari ◽  
Nur Iriawan ◽  
Siti Azizah Nurul Solichah ◽  
Irhamah Irhamah ◽  
Kartika Fithriasari ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Magnetic Resonance Imaging Mri ◽  
Healthy Part ◽  
Mri Image ◽  
Tumor Area

A brain tumor is one of the deadly diseases that attack the central and nervoussystem. The treatment of brain tumor, need high accuracy and precision. Brain tumordetection through Magnetic Resonance Imaging (MRI) has two-dimensional output withthree perspectives, namely sagittal, coronal, and axial. These different perspectives needto be seen one by one to determine the location and size of the tumor. Tosolve the problem, this study constructs the three-dimensional visualization perspective ofMRI images. The tumor area in MRI image is segmented as a region of interest (ROI) byemploying the Gaussian Mixture Model (GMM) with Expectation-Maximization as theoptimization technique. These couple segmentation methods have revealed significant gainas a clear boundary of the tumor area to separate from the healthy part of the brain andan estimated tumor volume from sagittal, coronal, and axial perspectives. Furthermore,these findings have been successfully visualized in 3D construction of the tumor positionon the left side of the patient’s head with an estimated volume of 749mm3.

scholarly journals Unilocular Radiolucent Lesions of the Jaws: a Retrospective Panoramic and Cone Beam Computed Tomography Evaluation

2020 ◽  
Vol 24 (3) ◽  
pp. 28-34
Author(s):  
Bilgun Cetin ◽  
Derya Icoz ◽  
Faruk Akgunlu
Keyword(s):  
Computed Tomography ◽  
Cortical Bone ◽  
Cone Beam Computed Tomography ◽  
Panoramic Radiography ◽  
Mandibular Canal ◽  
Cone Beam ◽  
Imaging Characteristics ◽  
Dentigerous Cysts ◽  
Significant Difference ◽  
Panoramic Images

SummaryBackground/Aim: The aim of this study was to evaluate and compare the imaging characteristics of common, radiolucent, unilocular, intraosseous lesions of the jaws using both panoramic radiography and cone beam computed tomography (CBCT); also, to evaluate sufficiency of panoramic radiography in determining characteristic features of jaw lesions.Material and Methods: Retrospectively selected images of 57 patients with histopathology results were evaluated by two oral radiologists. The lesions were assessed based on shape, location, borders, relationship with the mandibular canal, presence of destruction of cortical bone, and expansion of cortical bone, and presence of an unerupted tooth related to the lesion. In addition, the widest areas of the lesions were measured. A total of 9 (15.8%) odontogenic keratocysts, 9 (15.8%) apical granulomas, 24 (42.1%) radicular cysts, 12 (21.0%) dentigerous cysts and 3 (5.2%) central giant cell granulomas in 57 patients (20 women, 37 men) with a mean age of 36.93 ± 17.96 years were included. Fifty-seven CBCT and 56 panoramic images of these patients were evaluated.Results: Twenty-nine (50.8%) lesions were in the mandible and 28 (49.2%) in the maxilla. A statistically significant difference was determined for the areas in CBCT images (p=0.007).Conclusions: Panoramic radiography is not as successful as CBCT in demonstrating some characteristics of the lesions, such as expansion and destruction. The area measurements may be beneficial in establishing the differential diagnosis of the lesion.

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