scholarly journals Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ayman El-Baz ◽  
Ahmed Elnakib ◽  
Mohamed Abou El-Ghar ◽  
Georgy Gimel'farb ◽  
Robert Falk ◽  
...  
Keyword(s):  
Template Matching ◽  
Automatic Detection ◽  
Spiral Ct ◽  
Lung Nodules ◽  
Data Sets ◽  
Genetic Optimization ◽  
Anatomical Structures ◽  
Lung Abnormalities ◽  
Ct Data ◽  
2D And 3D

Automatic detection of lung nodules is an important problem in computer analysis of chest radiographs. In this paper, we propose a novel algorithm for isolating lung abnormalities (nodules) from spiral chest low-dose CT (LDCT) scans. The proposed algorithm consists of three main steps. The first step isolates the lung nodules, arteries, veins, bronchi, and bronchioles from the surrounding anatomical structures. The second step detects lung nodules using deformable 3D and 2D templates describing typical geometry and gray-level distribution within the nodules of the same type. The detection combines the normalized cross-correlation template matching and a genetic optimization algorithm. The final step eliminates the false positive nodules (FPNs) using three features that robustly define the true lung nodules. Experiments with 200 CT data sets show that the proposed approach provided comparable results with respect to the experts.

Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM

2014 ◽  
Vol 36 ◽  
pp. 27-39 ◽  
Author(s):  
Alex Martins Santos ◽  
Antonio Oseas de Carvalho Filho ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva ◽  
Rodolfo Acatauassú Nunes ◽  
...  
Keyword(s):  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Automatic Detection ◽  
Gaussian Mixture ◽  
Tsallis Entropy ◽  
Lung Nodules ◽  
3D Ct ◽  
Ct Data

2D and 3D visualisation of head and neck tumours from spiral-CT data

2000 ◽  
Vol 33 (3) ◽  
pp. 170-177 ◽  
Author(s):  
H Greess ◽  
A Nömayr ◽  
B Tomandl ◽  
M Blank ◽  
M Lell ◽  
...  
Keyword(s):  
Head And Neck ◽  
Spiral Ct ◽  
Head And Neck Tumours ◽  
3D Visualisation ◽  
Ct Data ◽  
2D And 3D

Pseudoaneurysm of the abdominal aorta: evaluation with virtual angioscopy of spiral-CT data sets

1999 ◽  
Vol 9 (6) ◽  
pp. 1227-1230 ◽  
Author(s):  
E. Neri ◽  
D. Caramella ◽  
R. Cioni ◽  
F. Trincavelli ◽  
C. Vignali ◽  
...  
Keyword(s):  
Abdominal Aorta ◽  
Spiral Ct ◽  
Data Sets ◽  
Virtual Angioscopy ◽  
Ct Data

Three-Dimensional Imaging of Thoracic and Abdominal Organs and Vessels from Spiral CT Data Sets

1992 ◽  
pp. 163-172
Author(s):  
M. Lenz ◽  
J. Gmeinwieser ◽  
A. Wunderlich ◽  
W. Bautz ◽  
B. Kersting-Sommerhoff ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Spiral Ct ◽  
Data Sets ◽  
Three Dimensional Imaging ◽  
Abdominal Organs ◽  
Ct Data ◽  
Thoracic And Abdominal

Effect of CT-based attenuation correction on uptake ratios in skeletal SPECT

2007 ◽  
Vol 46 (01) ◽  
pp. 38-42 ◽  
Author(s):  
V. Schulz ◽  
I. Nickel ◽  
A. Nömayr ◽  
A. H. Vija ◽  
C. Hocke ◽  
...  
Keyword(s):  
Vertebral Body ◽  
Facet Joint ◽  
One Dimension ◽  
Patient Specific ◽  
Data Sets ◽  
Facet Joints ◽  
Lumbar Vertebral Body ◽  
Ct System ◽  
Ct Data ◽  
Attenuation Corrected

SummaryThe aim of this study was to determine the clinical relevance of compensating SPECT data for patient specific attenuation by the use of CT data simultaneously acquired with SPECT/CT when analyzing the skeletal uptake of polyphosphonates (DPD). Furthermore, the influence of misregistration between SPECT and CT data on uptake ratios was investigated. Methods: Thirty-six data sets from bone SPECTs performed on a hybrid SPECT/CT system were retrospectively analyzed. Using regions of interest (ROIs), raw counts were determined in the fifth lumbar vertebral body, its facet joints, both anterior iliacal spinae, and of the whole transversal slice. ROI measurements were performed in uncorrected (NAC) and attenuation-corrected (AC) images. Furthermore, the ROI measurements were also performed in AC scans in which SPECT and CT images had been misaligned by 1 cm in one dimension beforehand (ACX, ACY, ACZ). Results: After AC, DPD uptake ratios differed significantly from the NAC values in all regions studied ranging from 32% for the left facet joint to 39% for the vertebral body. AC using misaligned pairs of patient data sets led to a significant change of whole-slice uptake ratios whose differences ranged from 3,5 to 25%. For ACX, the average left-to-right ratio of the facet joints was by 8% and for the superior iliacal spines by 31% lower than the values determined for the matched images (p <0.05). Conclusions: AC significantly affects DPD uptake ratios. Furthermore, misalignment between SPECT and CT may introduce significant errors in quantification, potentially also affecting leftto- right ratios. Therefore, at clinical evaluation of attenuation- corrected scans special attention should be given to possible misalignments between SPECT and CT.

Computerized Modeling Based on Spiral CT Data for Noninvasive Determination of Aortic Stent-Graft Length

2002 ◽  
Vol 9 (4) ◽  
pp. 520-528 ◽  
Author(s):  
Albert Rott ◽  
Thomas Boehm ◽  
Joachim Söldner ◽  
Jürgen R. Reichenbach ◽  
Jürgen Heyne ◽  
...  
Keyword(s):  
Stent Graft ◽  
Spiral Ct ◽  
Aortic Stent Graft ◽  
Ct Data ◽  
Graft Length

scholarly journals 3D convolutional neural network for automatic detection of lung nodules in chest CT

2017 ◽  
Author(s):  
Sardar Hamidian ◽  
Berkman Sahiner ◽  
Nicholas Petrick ◽  
Aria Pezeshk
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Chest Ct ◽  
Lung Nodules

Computer-assisted Diagnosis for Early Stage Pleural Mesothelioma

2007 ◽  
Vol 46 (03) ◽  
pp. 324-331 ◽  
Author(s):  
P. Jäger ◽  
S. Vogel ◽  
A. Knepper ◽  
T. Kraus ◽  
T. Aach ◽  
...  
Keyword(s):  
Early Stage ◽  
Computation Time ◽  
Hounsfield Unit ◽  
Computer Assisted ◽  
Clinical Test ◽  
Pleural Mesothelioma ◽  
Data Sets ◽  
Data Set ◽  
Average Computation Time ◽  
Ct Data

Summary Objectives: Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. Foritsearly stage, pleurectomy with perioperative treatment can reduce morbidity and mortality. The diagnosis is based on a visual investigation of CT images, which is a time-consuming and subjective procedure. Our aim is to develop an automatic image processing approach to detect and quantitatively assess pleural thickenings. Methods: We first segment the lung areas, and identify the pleural contours. A convexity model is then used together with a Hounsfield unit threshold to detect pleural thickenings. The assessment of the detected pleural thickenings is based on a spline-based model of the healthy pleura. Results: Tests were carried out on 14 data sets from three patients. In all cases, pleural contours were reliably identified, and pleural thickenings detected. PC-based Computation times were 85 min for a data set of 716 slices, 35 min for 401 slices, and 4 min for 75 slices, resulting in an average computation time of about 5.2 s per slice. Visualizations of pleurae and detected thickeningswere provided. Conclusion: Results obtained so far indicate that our approach is able to assist physicians in the tedious task of finding and quantifying pleural thickenings in CT data. In the next step, our system will undergo an evaluation in a clinical test setting using routine CT data to quantifyits performance.

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