Literature DB >> 8075188

Lung segmentation in digital radiographs.

E Pietka1.   

Abstract

Computer-assisted interpretation of computer radiography (CR) chest images including lung nodules detection, quantitative texture analysis, etc requires a lung delineation algorithm that restricts the area to be analyzed. This report presents a new lung-segmentation technique. It is performed in three phases. First, a histogram analysis finds a threshold value that eliminates the densest anatomic regions. Then, a gradient analysis separates the lungs from parts of thorax attached to the lungs that have not been removed in the previous phase. A smoothing routine yields the final image. By imposing a testing condition that results from the histogram analysis, underexposed images are not being considered. If being segmented, they exhibit a significant lung penetration. The test increases the accuracy of the procedure and makes it safer for an unsupervised application. The segmentation procedure has been implemented together with preprocessing functions in our clinical picture archiving and communication system.

Entities:  

Mesh:

Year:  1994        PMID: 8075188     DOI: 10.1007/bf03168427

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  15 in total

1.  Orientation correction for chest images.

Authors:  E Pietka; H K Huang
Journal:  J Digit Imaging       Date:  1992-08       Impact factor: 4.056

2.  Image preprocessing for a picture archiving and communication system.

Authors:  M F McNitt-Gray; E Pietka; H K Huang
Journal:  Invest Radiol       Date:  1992-07       Impact factor: 6.016

3.  Regionally adaptive histogram equalization of the chest.

Authors:  R H Sherrier; G A Johnson
Journal:  IEEE Trans Med Imaging       Date:  1987       Impact factor: 10.048

4.  Enhancement of chest radiographs with gradient operators.

Authors:  J S Daponte; M D Fox
Journal:  IEEE Trans Med Imaging       Date:  1988       Impact factor: 10.048

5.  Analysis of the solitary pulmonary nodule by means of digital techniques.

Authors:  C Chiles; R H Sherrier
Journal:  J Thorac Imaging       Date:  1990-01       Impact factor: 3.000

6.  Image feature analysis and computer-aided diagnosis in digital radiography: automated delineation of posterior ribs in chest images.

Authors:  S Sanada; K Doi; H MacMahon
Journal:  Med Phys       Date:  1991 Sep-Oct       Impact factor: 4.071

7.  A computational approach to edge detection.

Authors:  J Canny
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  1986-06       Impact factor: 6.226

8.  [Film digital and texture analysis for digital classification of pulmonary spot opacities].

Authors:  J F Desaga; J Dengler; T Wolf; U Engelmann; D Scheppelmann; H P Meinzer
Journal:  Rontgenblatter       Date:  1988-04

9.  Computerized search of chest radiographs for nodules.

Authors:  W A Lampeter; J C Wandtke
Journal:  Invest Radiol       Date:  1986-05       Impact factor: 6.016

10.  Automated computer screening of chest radiographs for pneumoconiosis.

Authors:  A F Turner; R P Kruger; W B Thompson
Journal:  Invest Radiol       Date:  1976 Jul-Aug       Impact factor: 6.016

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  8 in total

1.  Automated lung segmentation in digital chest tomosynthesis.

Authors:  Jiahui Wang; James T Dobbins; Qiang Li
Journal:  Med Phys       Date:  2012-02       Impact factor: 4.071

2.  Computerized analysis of abnormal asymmetry in digital chest radiographs: evaluation of potential utility.

Authors:  S G Armato; M L Giger; H MacMahon
Journal:  J Digit Imaging       Date:  1999-02       Impact factor: 4.056

3.  Experimental hip fracture load can be predicted from plain radiography by combined analysis of trabecular bone structure and bone geometry.

Authors:  P Pulkkinen; T Jämsä; E-M Lochmüller; V Kuhn; M T Nieminen; F Eckstein
Journal:  Osteoporos Int       Date:  2007-09-22       Impact factor: 4.507

4.  Fully automatic lung segmentation and rib suppression methods to improve nodule detection in chest radiographs.

Authors:  Elaheh Soleymanpour; Hamid Reza Pourreza; Emad Ansaripour; Mehri Sadooghi Yazdi
Journal:  J Med Signals Sens       Date:  2011-07

5.  2D Statistical Lung Shape Analysis Using Chest Radiographs: Modelling and Segmentation.

Authors:  Ali Afzali; Farshid Babapour Mofrad; Majid Pouladian
Journal:  J Digit Imaging       Date:  2021-03-22       Impact factor: 4.903

6.  Review: On Segmentation of Nodules from Posterior and Anterior Chest Radiographs.

Authors:  S K Chaya Devi; T Satya Savithri
Journal:  Int J Biomed Imaging       Date:  2018-10-18

7.  A hierarchical method based on active shape models and directed Hough transform for segmentation of noisy biomedical images; application in segmentation of pelvic X-ray images.

Authors:  Rebecca Smith; Kayvan Najarian; Kevin Ward
Journal:  BMC Med Inform Decis Mak       Date:  2009-11-03       Impact factor: 2.796

8.  Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome.

Authors:  Narathip Reamaroon; Michael W Sjoding; Harm Derksen; Elyas Sabeti; Jonathan Gryak; Ryan P Barbaro; Brian D Athey; Kayvan Najarian
Journal:  BMC Med Imaging       Date:  2020-10-15       Impact factor: 1.930

  8 in total

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