Early assessment of malignant lung nodules based on the spatial analysis of detected lung nodules

We propose a novel approach for diagnosing malignant lung nodules based on analyzing the spatial distribution of Hounsfield values for the detected lung nodules. Spatial distribution of image intensities (or Hounsfield values) comprising the malignant nodule appearance is accurately modeled with a n...

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Bibliographic Details
Published in2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) pp. 1463 - 1466
Main Authors El-Baz, A., Soliman, A., McClure, P., Gimel'farb, G., El-Ghar, M. A., Falk, R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2012
Subjects
Accuracy
Cancer
Computed tomography
Design automation
Lung nodules
Lungs
Markov fields
Solid modeling
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Summary:We propose a novel approach for diagnosing malignant lung nodules based on analyzing the spatial distribution of Hounsfield values for the detected lung nodules. Spatial distribution of image intensities (or Hounsfield values) comprising the malignant nodule appearance is accurately modeled with a new rotationally invariant second-order Markov-Gibbs Random Field (MGRF). In this paper, we introduce a new maximum likelihood estimation approach to estimate the neighborhood system of the proposed rotation invariant MGRF and its potentials from a training set of nodule images with normalized intensity ranges. Preliminary experiments on 327 lung nodules (153 malignant and 174 benign) resulted in the 91.1% correct classification (for the 95% confidence interval), showing the proposed method is a promising supplement to current technologies (biopsy-based diagnostic systems) for the early diagnosis of lung cancer.
ISBN:145771857X
9781457718571
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2012.6235847