Segmentation of Kidneys from Computed Tomography Using 3D Fast GrowCut Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1145-1150 ◽  
Author(s):  
Gao Yuan Dai ◽  
Zhi Cheng Li ◽  
Jia Gu ◽  
Lei Wang ◽  
Xing Min Li ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Experimental Results ◽  
Volume Data ◽  
Great Role ◽  
Color Features ◽  
Monotonically Decreasing Function ◽  
Kidney Segmentation ◽  
Ct Volume

This paper proposes a fast GrowCut (FGC) algorithm and applies the new algorithm in three-dimensional (3D)kidney segmentation from computed tomography (CT) volume data. Users could mark the object of interest with different labels in CT slices.FGC propagates the labels using monotonically decreasing function and color features to derive an optimal cut for a given data in space. The color features play a great role in comparing with neighborhood cells. The experimental results clearly demonstrate the superiority of FGC in accuracy and speed.

Cutting Display of Industrial CT Volume Data Based on GPU

2011 ◽  
Vol 271-273 ◽  
pp. 1096-1102
Author(s):  
Yong Ning Zou ◽  
Jue Wang ◽  
Jian Wei Li
Keyword(s):  
Computed Tomography ◽  
Programming Model ◽  
Rapid Development ◽  
Frame Rate ◽  
Volume Data ◽  
Graphic Processor ◽  
Industrial Ct ◽  
Performance Results ◽  
Ct Volume

The rapid development of Graphic Processor Units (GPU) in recent years in terms of performance and programmability has attracted the attention of those seeking to leverage alternative architectures for better performance than that which commodity CPU can provide. This paper presents a new algorithm for cutting display of computed tomography volume data on the GPU. We first introduce the programming model of the GPU and outline the implementation of techniques for oblique plane cutting display of volume data on both the CPU and GPU. We compare the approaches and present performance results for both the CPU and GPU. The results show that cutting display image generated by GPU algorithm is clear, frame rate on GPU is 2-9 times than that on CPU.

PROTOTYPE OF A 2.5D PERIAPICAL RADIOGRAPHY SYSTEM USING AN INTRAORAL COMPUTED TOMOSYNTHESIS APPROACH

2018 ◽  
Vol 30 (01) ◽  
pp. 1850004 ◽  
Author(s):  
Che-Wei Liao ◽  
Chia-Jui Hsieh ◽  
Heng-Li Huang ◽  
Lih-Jyh Fuh ◽  
Chih-Wei Kuo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Experimental Results ◽  
Radiographic Images ◽  
Radiography System ◽  
Computed Tomography Scanning ◽  
Noise Ratio ◽  
Dimensional Object ◽  
Periapical Radiography

Digital periapical radiography is widely used in clinical dentistry because the technique is relatively simple and inexpensive. However, the main drawback of periapical radiography is that it represents a three-dimensional object in a two-dimensional film due to its inherent projection technique. The objective of this study was to develop a prototype intraoral computed tomosynthesis system, which can provide quasi-three-dimensional (so-called 2.5D) images. We developed a prototype intraoral computed tomosynthesis machine. Regular digital periapical radiography, computed tomosynthesis scanning, and computed tomography scanning of a human central incisor were performed. Then, reconstruction images obtained using computed tomosynthesis and computed tomography approaches were quantitatively evaluated using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). From the experimental results, compared with periapical radiographic images, reconstruction images obtained using the computed tomosynthesis approach revealed detailed microstructures in different depth sections. In addition, the SNR and CNR of reconstruction images obtained using the computed tomography approach was better than those of the images obtained using the computed tomosynthesis approach. However, the differences could not be clearly identified by the naked eye. The preliminary experimental results indicate that an intraoral computed tomosynthesis system may be useful for clinical dental diagnosis.

scholarly journals Pulmonary lesion subtypes recognition of COVID-19 from radiomics data with three-dimensional texture characterization in computed tomography images

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Wei Li ◽  
Yangyong Cao ◽  
Kun Yu ◽  
Yibo Cai ◽  
Feng Huang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Feature Selection ◽  
Random Forest ◽  
Data Augmentation ◽  
Critical Role ◽  
Three Dimensional ◽  
Ground Glass Opacity ◽  
Classification Model ◽  
Volume Data ◽  
Computed Tomography Images

Abstract Background The COVID-19 disease is putting unprecedented pressure on the global healthcare system. The CT (computed tomography) examination as a auxiliary confirmed diagnostic method can help clinicians quickly detect lesions locations of COVID-19 once screening by PCR test. Furthermore, the lesion subtypes classification plays a critical role in the consequent treatment decision. Identifying the subtypes of lesions accurately can help doctors discover changes in lesions in time and better assess the severity of COVID-19. Method The most four typical lesion subtypes of COVID-19 are discussed in this paper, which are GGO (ground-glass opacity), cord, solid and subsolid. A computer-aided diagnosis approach of lesion subtype is proposed in this paper. The radiomics data of lesions are segmented from COVID-19 patients CT images with diagnosis and lesions annotations by radiologists. Then the three-dimensional texture descriptors are applied on the volume data of lesions as well as shape and first-order features. The massive feature data are selected by HAFS (hybrid adaptive feature selection) algorithm and a classification model is trained at the same time. The classifier is used to predict lesion subtypes as side decision information for radiologists. Results There are 3734 lesions extracted from the dataset with 319 patients collection and then 189 radiomics features are obtained finally. The random forest classifier is trained with data augmentation that the number of different subtypes of lesions is imbalanced in initial dataset. The experimental results show that the accuracy of the four subtypes of lesions is (93.06%, 96.84%, 99.58%, and 94.30%), the recall is (95.52%, 91.58%, 95.80% and 80.75%) and the f-score is (93.84%, 92.37%, 95.47%, and 84.42%). Conclusion The three-dimensional radiomics features used in this paper can better express the high-level information of COVID-19 lesions in CT slices. HAFS method aggregates the results of multiple feature selection algorithms intersects with traditional methods to filter out redundant features more accurately. After selection, the subtype of COVID-19 lesion can be judged by inputting the features into the RF (random forest) model, which can help clinicians more accurately identify the subtypes of COVID-19 lesions and provide help for further research.

Evaluation of Scanning Parameters Based on Image Entropy for Dimensional Computed Tomography Metrology

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Lin Xue ◽  
Hiromasa Suzuki
Keyword(s):  
Computed Tomography ◽  
Measurement Accuracy ◽  
Ct Scanning ◽  
Volume Data ◽  
Dimensional Metrology ◽  
X Ray ◽  
Image Entropy ◽  
Improve Measurement ◽  
Ct Volume

Many types of artifacts appear in X-ray computed tomography (CT) volume data, which influence measurement quality of industrial cone beam X-ray CT. Most of those artifacts are associated to CT scanning parameters; therefore, a good scanning parameter setting can weaken the influence to improve measurement accuracy. This paper presents a simulation method for evaluating CT scanning parameters for dimensional metrology. The method can aid CT metrology to achieve high measurement accuracy. In the method, image entropy is used as a criterion to evaluate the quality of CT volume data. For entropy calculation of CT volume data, a detailed description about bin width and entropy zone is given. The relationship between entropy values of CT volume data and error parameters of CT metrology is shown and discussed. By use of this method, mainly we focus on specimen orientation evaluation, and some other typical scanning parameters are used to evaluate the proposed method. Two typical specimens are used to evaluate the performance of the proposed method.

scholarly journals A Comparative Study on the Voxel Values in Alveolar Bones Acquired by MDCT and Newly Developed Dental Dual-Energy CBCT

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7552
Author(s):  
Sungho Chang ◽  
Sangchul Lee
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Dual Energy ◽  
Volume Data ◽  
Mean Values ◽  
Identity Line ◽  
Dental Hospital ◽  
National University ◽  
Voxel Value ◽  
Seoul National University

The purpose of this study was to analyze the effectiveness of newly developed dental dual-energy (DE) cone-beam computed tomography (CBCT) to compare both the voxel values in hard bone tissue of DE-CBCT and multidetector computed tomography (MDCT) images, collected in a clinical trial conducted at Seoul National University Dental Hospital. A software implemented as a scripted module of a three-dimensional (3D) slicer was developed to register the volume data from the MDCT space to DE-CBCT, locate the same 3D regions of interest (ROIs) in each image space, and extract the statistics of the ROIs. The mean values were paired and used as representative values of the ROIs. A scatter plot with the line of equality and Bland–Altman (BA) plot of difference for a pair of measured means were used for statistical analysis. Of the ROI pairs, 96% were within ±15% from the identity line, and more than 95% of the measured ROI pairs were within the limits of agreement of the 95% confidence intervals (CIs), with the CI of the limits in BA plots. The newly developed dental DE-CBCT showed a level of voxel value accuracy similar to that of MDCT.

scholarly journals Reproducibility of landmark identification in the jaw and teeth on 3-dimensional cone-beam computed tomography images

2011 ◽  
Vol 81 (5) ◽  
pp. 843-849 ◽  
Author(s):  
Mariko Fuyamada ◽  
Hiroyuki Nawa ◽  
Momoko Shibata ◽  
Kazuhito Yoshida ◽  
Yoshitaka Kise ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cone Beam Computed Tomography ◽  
Three Dimensional ◽  
Cone Beam ◽  
Volume Data ◽  
3D Images ◽  
3 Dimensional ◽  
Computed Tomography Images ◽  
Landmark Identification ◽  
Significant Difference

Abstract Objective: To compare the reproducibility of landmark identification on three-dimensional (3D) cone-beam computed tomography (CBCT) images between procedures based on traditional cephalometric definitions (procedure 1) and those tentatively proposed for 3D images (procedure 2). Materials and Methods: A phantom with embedded dried human skull was scanned using CBCT. The acquired volume data were transferred to a personal computer, and 3D images were reconstructed. Eighteen dentists plotted nine landmarks related to the jaws and teeth four times: menton (Me), pogonion (Po), upper-1 (U1), lower-1 (L1), left upper-6 (U6), left lower-6 (L6), gonion (Go), condyle (Cd), and coronoid process (Cp). The plotting reliabilities of the two procedures were compared by calculating standard deviations (SDs) in three components (x, y, and z) of coordinates and volumes of 95% confidence ellipsoid. Results: All 27 SDs for procedure 2 were less than 1 mm, and only five of them exceeded 0.5 mm. The variations were significantly different between the two procedures, and the SDs of procedure 2 were smaller than those of procedure 1 in 21 components of coordinates. The ellipsoid volumes were also smaller for procedure 2 than procedure 1, although a significant difference was not found. Conclusions: Definitions determined strictly on each three sectional images, such as for procedure 2, were required for sufficient reliability in identifying the landmark related to the jaws and teeth.

scholarly journals Kidney Segmentation in CT Data Using Hybrid Level-Set Method with Ellipsoidal Shape Constraints

2017 ◽  
Vol 24 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Andrzej Skalski ◽  
Katarzyna Heryan ◽  
Jacek Jakubowski ◽  
Tomasz Drewniak
Keyword(s):  
Computed Tomography ◽  
Level Set ◽  
Renal Cancer ◽  
Imaging Techniques ◽  
Three Dimensional ◽  
Spatial Arrangement ◽  
Energy Functional ◽  
Contour Method ◽  
Ellipsoidal Shape ◽  
Kidney Segmentation

Abstract With development of medical diagnostic and imaging techniques the sparing surgeries are facilitated. Renal cancer is one of examples. In order to minimize the amount of healthy kidney removed during the treatment procedure, it is essential to design a system that provides three-dimensional visualization prior to the surgery. The information about location of crucial structures (e.g. kidney, renal ureter and arteries) and their mutual spatial arrangement should be delivered to the operator. The introduction of such a system meets both the requirements and expectations of oncological surgeons. In this paper, we present one of the most important steps towards building such a system: a new approach to kidney segmentation from Computed Tomography data. The segmentation is based on the Active Contour Method using the Level Set (LS) framework. During the segmentation process the energy functional describing an image is the subject to minimize. The functional proposed in this paper consists of four terms. In contrast to the original approach containing solely the region and boundary terms, the ellipsoidal shape constraint was also introduced. This additional limitation imposed on evolution of the function prevents from leakage to undesired regions. The proposed methodology was tested on 10 Computed Tomography scans from patients diagnosed with renal cancer. The database contained the results of studies performed in several medical centers and on different devices. The average effectiveness of the proposed solution regarding the Dice Coefficient and average Hausdorff distance was equal to 0.862 and 2.37 mm, respectively. Both the qualitative and quantitative evaluations confirm effectiveness of the proposed solution.

scholarly journals Three-dimensional segmentation of computed tomography data using Drishti Paint : new tools and developments

2020 ◽  
Vol 7 (12) ◽  
pp. 201033
Author(s):  
Yuzhi Hu ◽  
Ajay Limaye ◽  
Jing Lu
Keyword(s):  
Computed Tomography ◽  
Open Source ◽  
Three Dimensional ◽  
Volume Data ◽  
Digital Reconstruction ◽  
Ct Data ◽  
Scan Data ◽  
Volume Exploration ◽  
Three Dimensional Segmentation ◽  
Tomography Data

Computed tomography (CT) has become very widely used in scientific and medical research and industry for its non-destructive and high-resolution means of detecting internal structure. Three-dimensional segmentation of computed tomography data sheds light on internal features of target objects. Three-dimensional segmentation of CT data is supported by various well-established software programs, but the powerful functionalities and capabilities of open-source software have not been fully revealed. Here, we present a new release of the open-source volume exploration, rendering and three-dimensional segmentation software, Drishti v. 2.7. We introduce a new tool for thresholding volume data (i.e. gradient thresholding) and a protocol for performing three-dimensional segmentation using the 3D Freeform Painter tool. These new tools and workflow enable more accurate and precise digital reconstruction, three-dimensional modelling and three-dimensional printing results. We use scan data of a fossil fish as a case study, but our procedure is widely applicable in biological, medical and industrial research.

X-ray microtomography

1988 ◽  
Vol 46 ◽  
pp. 998-999
Author(s):  
H.W. Deckman ◽  
B.F. Flannery ◽  
J.H. Dunsmuir ◽  
K.D' Amico
Keyword(s):  
Computed Tomography ◽  
Internal Structure ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Tissue Structure ◽  
Individual Element ◽  
X Ray ◽  
Non Invasive ◽  
Panoramic Imaging ◽  
Three Dimensional Images

We have developed a new X-ray microscope which produces complete three dimensional images of samples. The microscope operates by performing X-ray tomography with unprecedented resolution. Tomography is a non-invasive imaging technique that creates maps of the internal structure of samples from measurement of the attenuation of penetrating radiation. As conventionally practiced in medical Computed Tomography (CT), radiologists produce maps of bone and tissue structure in several planar sections that reveal features with 1mm resolution and 1% contrast. Microtomography extends the capability of CT in several ways. First, the resolution which approaches one micron, is one thousand times higher than that of the medical CT. Second, our approach acquires and analyses the data in a panoramic imaging format that directly produces three-dimensional maps in a series of contiguous stacked planes. Typical maps available today consist of three hundred planar sections each containing 512x512 pixels. Finally, and perhaps of most import scientifically, microtomography using a synchrotron X-ray source, allows us to generate maps of individual element.

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