Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm Journal Article

Authors: Zhao, B.; Gamsu, G.; Ginsberg, M. S.; Jiang, L.; Schwartz, L. H.
Article Title: Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm
Abstract: Increasingly, computed tomography (CT) offers higher resolution and faster acquisition times. This has resulted in the opportunity to detect small lung nodules, which may represent lung cancers at earlier and potentially more curable stages. However, in the current clinical practice, hundreds of such thin-sectional CT images are generated for each patient and are evaluated by a radiologist in the traditional sense of looking at each image in the axial mode. This results in the potential to miss small nodules and thus potentially miss a cancer. In this paper, we present a computerized method for automated identification of small lung nodules on multislice CT (MSCT) images. The method consists of three steps: (i) separation of the lungs from the other anatomic structures, (ii) detection of nodule candidates in the extracted lungs, and (iii) reduction of false-positives among the detected nodule candidates. A three-dimensional lung mask can be extracted by analyzing density histogram of volumetric chest images followed by a morphological operation. Higher density structures including nodules scattered throughout the lungs can be identified by using a local density maximum algorithm. Information about nodules such as size and compact shape are then incorporated into the algorithm to reduce the detected nodule candidates which are not likely to be nodules. The method was applied to the detection of computer simulated small lung nodules (2 to 7 mm in diameter) and achieved a sensitivity of 84.2% with, on average, five false-positive results per scan. The preliminary results demonstrate the potential of this technique for assisting the detection of small nodules from chest MSCT images. (c) 2003 American College of Medical Physics.
Keywords: methodology; laboratory diagnosis; computer assisted tomography; tomography, x-ray computed; algorithms; algorithm; computer assisted diagnosis; three dimensional imaging; imaging, three-dimensional; computer simulation; image processing, computer-assisted; image processing; false positive reactions; radiographic image interpretation, computer-assisted; lung coin lesion; coin lesion, pulmonary; humans; human; article
Journal Title: Journal of Applied Clinical Medical Physics
Volume: 4
Issue: 3
ISSN: 1526-9914
Publisher: American College of Medical Physics  
Date Published: 2003-01-01
Start Page: 248
End Page: 260
Language: English
PUBMED: 12841796
PROVIDER: scopus
DOI: 10.1120/jacmp.v4i3.2522
Notes: Export Date: 12 September 2014 -- Source: Scopus
Altmetric Score
MSK Authors
  1. Michelle S Ginsberg
    160 Ginsberg
  2. Lawrence H Schwartz
    282 Schwartz
  3. Binsheng Zhao
    46 Zhao
  4. Li Jiang
    6 Jiang