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.

scholarly journals A new tool for 3D segmentation of computed tomography data: Drishti Paint and its applications

2020 ◽  
Author(s):  
Yuzhi Hu ◽  
Ajay Limaye ◽  
Jing Lu
Keyword(s):  
Computed Tomography ◽  
Research Field ◽  
3D Segmentation ◽  
Digital Reconstruction ◽  
Computational Tomography ◽  
Internal Structures ◽  
Scan Data ◽  
Volume Exploration ◽  
Non Destructive ◽  
Tomography Data

AbstractComputational tomography is more and more widely used in many fields for its non-destructive and high-resolution in detecting internal structures of the samples. 3D segmentation of computed tomography data, which sheds light into internal features of target objects, is increasingly gaining in importance. However, how to efficiently and precisely reconstruct computed tomography data and better represent the data remains a hassle. Here, using a set of scan data of a fossil fish as a case study, we present a new release of open-source volume exploration, rendering, and 3D segmentation software, Drishti v2.6.6, and its protocol for performing 3D segmentation and other advanced applications. We provide new toolsets and workflow to segment computed tomography data thus benefit the scientific community with more accurate and precise digital reconstruction, 3D modelling and 3D printing results. Our procedure is widely applicable not only in palaeontology, but also in biological, medical, and industrial researches, and can be used as a framework to segment computed tomography and other forms of volumetric data from any research field.

Morphometry of the sacrum and its implication on trans-sacral corridors using a computed tomography data-based three-dimensional statistical model

2017 ◽  
Vol 17 (8) ◽  
pp. 1141-1147 ◽  
Author(s):  
Daniel Wagner ◽  
Lukas Kamer ◽  
Takeshi Sawaguchi ◽  
Robert Geoff Richards ◽  
Hansrudi Noser ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Statistical Model ◽  
Three Dimensional ◽  
Tomography Data

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.

Three Dimensional Imaging of Bone From Analysis of Computed Tomography Data

Orthopedics ◽  
1985 ◽  
Vol 8 (10) ◽  
pp. 1269-1273 ◽  
Author(s):  
Steven T Woolson ◽  
Linda L Fellingham ◽  
Parvati Dev ◽  
Arthur Vassiliadis
Keyword(s):  
Computed Tomography ◽  
Three Dimensional ◽  
Three Dimensional Imaging ◽  
Tomography Data

Use of computed tomography data for forensic identification of an individual

2020 ◽  
Vol 6 (4) ◽  
pp. 41-45
Author(s):  
Sergey V. Leonov ◽  
Julia P. Shakiryanova
Keyword(s):  
Computed Tomography ◽  
Computer Tomography ◽  
Soft Tissues ◽  
Three Dimensional ◽  
Reference Points ◽  
Forensic Identification ◽  
Dimensional Model ◽  
Three Dimensional Model ◽  
The Face ◽  
Tomography Data

Background: The article presents our own experience of using computer tomography for identification of individuals with known results. Aims: The aim of the study was to verify the possibility of performing an identification study using a three-dimensional model obtained from computed tomography of the head. Identification was performed using a three-dimensional model of the head, based on computer tomography sections made in various projections, with a step of 1.231.25 mm. Two-dimensional images of the face (photos) were used for comparison. All comparative studies were conducted using approved methods of craniofacial and portrait identification: by reference points and contours. The experiment used a computer program that allows you to export DICOM-files of computed tomography results to other formats (InVesalius), as well as computer programs that directly work with the research objects (Autodesk 3ds Max, alternative programs Adobe Photoshop, Smith Micro Poser Pro). Results: In the course of research, it was found that, having computer tomography data of the head, it is possible to conduct identification studies on the following parameters: on the reconstructed three-dimensional model of the soft tissues of the face, on the three-dimensional model of the skull (craniofacial identification), on the features of the structure of the ear. Conclusion: Positive results were obtained when comparing objects, which makes it advisable to use them in practical and scientific activities.

Adaptive Fuzzy Segmentation of Tumors in Three-Dimensional Computed Tomography (CT) Image Data

2007 ◽  
Author(s):  
Jung Leng Foo ◽  
Go Miyano ◽  
Thom Lobe ◽  
Eliot Winer
Keyword(s):  
Computed Tomography ◽  
Fuzzy Inference ◽  
Three Dimensional ◽  
Fuzzy Rule ◽  
Region Of Interest ◽  
Image Data ◽  
Ct Image ◽  
Inference System ◽  
Ct Data ◽  
Fuzzy Segmentation

The continuing advancement of computed tomography (CT) technology has improved the analysis and visualization of tumor data. As imaging technology continues to accommodate the need for high quality medical image data, this encourages the research for more efficient ways of extracting crucial information from these vast amounts of data. A new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data has been developed. To initialize the segmentation process, the user selects the region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROI’s spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy inference system. From a set of predefined fuzzy rules, the system generates a defuzzified output for every pixel in terms of similarity to the object. Pixels with the highest similarity values are selected to be the tumor. This process is repeated for every subsequent slice in the CT set, and the segmented region from the previous slice is used as the ROI for the current slice. This creates a propagation of information from the previous slices, to be used to segment the current slice. The membership functions used during the fuzzification and defuzzification processes are adaptive to the changes in the size and pixel intensities of the current ROI. The proposed method is highly customizable to suit different needs of a user, requiring information from only a single two-dimensional image. Implementing the fuzzy segmentation on two distinct CT sets, the fuzzy segmentation algorithm was able to successfully extract the tumor from the CT image data. Based on the results statistics, the developed segmentation technique is approximately 96% accurate when compared to the results of manual segmentations performed.

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