Analysis of displacement compensation methods for wavelet lifting of medical 3-D thorax CT volume data

2012 ◽  
Author(s):  
Wolfgang Schnurrer ◽  
Jurgen Seiler ◽  
Eugen Wige ◽  
Andre Kaup
Keyword(s):  
Volume Data ◽  
Compensation Methods ◽  
Thorax Ct ◽  
Wavelet Lifting ◽  
Ct Volume

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.

scholarly journals Development of Automatic Visceral Fat Volume Calculation Software for CT Volume Data

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Mitsutaka Nemoto ◽  
Tusufuhan Yeernuer ◽  
Yoshitaka Masutani ◽  
Yukihiro Nomura ◽  
Shouhei Hanaoka ◽  
...  
Keyword(s):  
Visceral Fat ◽  
Whole Body ◽  
Volume Data ◽  
Volume Calculation ◽  
Whole Body Ct ◽  
Experienced Radiologist ◽  
Fat Volume ◽  
Calculation Software ◽  
Body Ct ◽  
Ct Volume

Objective. To develop automatic visceral fat volume calculation software for computed tomography (CT) volume data and to evaluate its feasibility.Methods. A total of 24 sets of whole-body CT volume data and anthropometric measurements were obtained, with three sets for each of four BMI categories (under 20, 20 to 25, 25 to 30, and over 30) in both sexes. True visceral fat volumes were defined on the basis of manual segmentation of the whole-body CT volume data by an experienced radiologist. Software to automatically calculate visceral fat volumes was developed using a region segmentation technique based on morphological analysis with CT value threshold. Automatically calculated visceral fat volumes were evaluated in terms of the correlation coefficient with the true volumes and the error relative to the true volume.Results. Automatic visceral fat volume calculation results of all 24 data sets were obtained successfully and the average calculation time was 252.7 seconds/case. The correlation coefficients between the true visceral fat volume and the automatically calculated visceral fat volume were over 0.999.Conclusions. The newly developed software is feasible for calculating visceral fat volumes in a reasonable time and was proved to have high accuracy.

Label Propagation Algorithm for the Slices Detection of a Ground-Glass Opacity Nodule

2019 ◽  
Vol 7 (1) ◽  
pp. 104-118 ◽  
Author(s):  
Weiwei Du ◽  
Dandan Yuan ◽  
Jianming Wang ◽  
Xiaojie Duan ◽  
Yanhe Ma ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Lung Tumor ◽  
Ground Glass Opacity ◽  
Label Propagation ◽  
Ground Glass ◽  
Volume Data ◽  
Propagation Algorithm ◽  
Benign Lung Tumor ◽  
The Impact ◽  
Ct Volume

A radiologist must read hundreds of slices to recognize a malignant or benign lung tumor in computed tomography (CT) volume data. To reduce the burden of the radiologist, some proposals have been applied with the ground-glass opacity (GGO) nodules. However, the GGO nodules need be detected and labeled by a radiologist manually. Some slices with the GGO nodule can be missed because there are many slices in several volume data. Although some papers have proposed a semi-supervised learning method to find the slices with GGO nodules, the was no discussion on the impact of parameters in the proposed semi-supervised learning. This article also explains and analyzes the label propagation algorithm which is one of the semi-supervised learning methods to detect the slices including the GGO nodules based on the parameters. Experimental results show that the proposal can detect the slices including the GGO nodules effectively.

A novel method for 3D crack edge extraction in CT volume data

2011 ◽  
Vol 19 (4) ◽  
pp. 429-442 ◽  
Author(s):  
Bi Bi ◽  
Li Zeng ◽  
Haina Jiang
Keyword(s):  
Crack Edge ◽  
Edge Extraction ◽  
Volume Data ◽  
Novel Method ◽  
Ct Volume

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.

Bitplane Based Volume Morphology for 3-D Image Speckle Noise Suppressing

2013 ◽  
Vol 740 ◽  
pp. 188-192
Author(s):  
Chen Jian ◽  
Tong Li ◽  
Jiang Hua ◽  
Zeng Ying ◽  
Yan Bin
Keyword(s):  
Image Processing ◽  
Data Analysis ◽  
Computing Time ◽  
Speckle Noise ◽  
Scale Space ◽  
3D Image ◽  
Volume Data ◽  
3D Image Processing ◽  
Industrial Ct ◽  
Ct Volume

In 3D image processing, such as medical volume data and industrial CT volume data analysis, speckle noise suppressing significantly affects their accuracy. This paper utilizes a group of volume morphology arithmetic operators, mainly including volume open and volume close, by extending area morphology into 3-D space. Using these operators, the light and dark objects of small size could be removed directly from the 3-D space of the target objects, while the connectivity of the main 3-D target objects in the volume data is still preserved. To improve the volume morphology operations efficiency, we decompose volume data in bitplanes instead of in gray scale space to reduce the binary volumes. To verify the effect of the improved volume morphology operators, they are applied to suppress speckle noises in 3-D images of coral and rat skull and compared with original volume morphology operations. Experimental results show that the algorithm proposed in this paper can significantly reduce computing time, while maintaining comparable results to original operations results.

Sign in / Sign up

Export Citation Format

Share Document