2003
DOI: 10.1118/1.1624771
|View full text |Cite
|
Sign up to set email alerts
|

Computer‐aided diagnosis in high resolution CT of the lungs

Abstract: A computer-aided diagnosis (CAD) system is presented to automatically distinguish normal from abnormal tissue in high-resolution CT chest scans acquired during daily clinical practice. From high-resolution computed tomography scans of 116 patients, 657 regions of interest are extracted that are to be classified as displaying either normal or abnormal lung tissue. A principled texture analysis approach is used, extracting features to describe local image structure by means of a multi-scale filter bank. The use … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
111
0

Year Published

2006
2006
2018
2018

Publication Types

Select...
5
3
1

Relationship

0
9

Authors

Journals

citations
Cited by 129 publications
(111 citation statements)
references
References 25 publications
0
111
0
Order By: Relevance
“…6,7 ROIs of 100 Â 25 pixels were selected to characterize the healthy bone. It was the minimum area that included only one kind of tissue (cortical, trabecular, or flat bone).…”
Section: Regions Of Interestmentioning
confidence: 99%
“…6,7 ROIs of 100 Â 25 pixels were selected to characterize the healthy bone. It was the minimum area that included only one kind of tissue (cortical, trabecular, or flat bone).…”
Section: Regions Of Interestmentioning
confidence: 99%
“…In this study, the 3D LBP-TOP uses three orthogonal planes that intersect in the center voxel 5 as shown in Figure 11. www.ijacsa.thesai.org In the experiment we select 4 and 8 neighbors with 1 voxel radius as a local neighborhood.…”
Section: ) Small Cell Carcinoma (6 Patients) 2) Tubercoluma Of the Lmentioning
confidence: 99%
“…[2,3,4] uses measures on co-occurrence matrices, measures on run-length matrices, moments of the attenuation or intensity histogram, and in some cases fractal dimension as features. Sluimer et al [5] used a filter bank of Gaussians and Gaussian derivatives.…”
Section: Introductionmentioning
confidence: 99%
“…Depth of each region i.e. Watershed is computed as a difference between the largest and smallest valued pixel in that watershed [6].…”
Section: B Flow Of Steps Involved In Watershed Segmentationmentioning
confidence: 99%