A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution computed tomography

Michael F. McNitt-Gray*, Eric M. Hart, Jonathan Goldin, Chih Wei Yao, Denise R. Aberle

*Corresponding author for this work

Research output: Contribution to journalConference article

28 Scopus citations

Abstract

Purpose: To characterize solitary pulmonary nodules (SPN) as benign or malignant based on pattern classification techniques using size, shape, density and texture features extracted from HRCT images. Methods and Materials: HRCT images of patients with a SPN are acquired, routed through a PACS and displayed on a Thoracic Radiology workstation. Using the original data, the SPN is semiautomatically contoured using a nodule/background threshold. The contour is used to calculate size and several shape parameters, including compactness and bending energy. Pixels within the interior of the contour are used to calculate several features including: (1) nodule density-related features, such as representative Hounsfield number and moment of inertia and (2) texture measures based on the spatial gray level dependence matrix and fractal dimension. The true diagnosis of the SPN is established by histology from biopsy or, in the case of some benign nodules, extended follow-up. Multi-dimensional analyses of the features are then performed to determine which features can discriminate between benign and malignant nodules. When a sufficient number of cases are obtained two pattern classifiers, a linear discriminator and a neural network, will be trained and tested using a select subset of features. Results: Preliminary data from nine (9) nodule cases have been obtained and several features extracted. While the representative CT number is a reasonably good indicator, it is an inconclusive predictor of SPN diagnosis when considered by itself. Separation between benign and malignant nodules improves when other features, such as the distribution of density as measured by moment of inertia, are included in the analysis. Conclusion: Software has been developed and preliminary results have been obtained which show that individual features may not be sufficient to discriminate between benign and malignant nodules. However, combinations of these features may be able to discriminate between these two classes. With additional cases and more features, we will be able to perform a feature selection procedure and ultimately to train and test pattern classifiers in this discrimination task.

Original languageEnglish (US)
Pages (from-to)1024-1034
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume2710
DOIs
StatePublished - Dec 1 1996
EventMedical Imaging 1996 Image Processing - Newport Beach, CA, United States
Duration: Feb 12 1996Feb 15 1996

Keywords

  • Computed tomography
  • Image analysis
  • Image processing
  • Pattern classification
  • Shape
  • Solitary pulmonary nodule
  • Texture

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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