3D Shape Analysis for Early Diagnosis of Malignant Lung Nodules

An alternative method for diagnosing malignant lung nodules by their shape rather than conventional growth rate is proposed. The 3D surfaces of the detected lung nodules are delineated by spherical harmonic analysis, which represents a 3D surface of the lung nodule supported by the unit sphere with...

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Bibliographic Details
Published inInformation Processing in Medical Imaging Vol. 22; pp. 772 - 783
Main Authors El-Baz, Ayman, Nitzken, Matthew, Khalifa, Fahmi, Elnakib, Ahmed, Gimel’farb, Georgy, Falk, Robert, El-Ghar, Mohammed Abo
Format Book Chapter Journal Article
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2011
SeriesLecture Notes in Computer Science
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ISBN3642220916
9783642220913
ISSN0302-9743
1011-2499
1611-3349
DOI10.1007/978-3-642-22092-0_63

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Summary:An alternative method for diagnosing malignant lung nodules by their shape rather than conventional growth rate is proposed. The 3D surfaces of the detected lung nodules are delineated by spherical harmonic analysis, which represents a 3D surface of the lung nodule supported by the unit sphere with a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape analysis is carried out in five steps: (i) 3D lung nodule segmentation with a deformable 3D boundary controlled by two probabilistic visual appearance models (the learned prior and the estimated current appearance one); (ii) 3D Delaunay triangulation to construct a 3D mesh model of the segmented lung nodule surface; (iii) mapping this model to the unit sphere; (iv) computing the SHs for the surface, and (v) determining the number of the SHs to delineate the lung nodule. We describe the lung nodule shape complexity with a new shape index, the estimated number of the SHs, and use it for the K-nearest classification to distinguish malignant and benign lung nodules. Preliminary experiments on 327 lung nodules (153 malignant and 174 benign) resulted in the 93.6% correct classification (for the 95% confidence interval), showing that the proposed method is a promising supplement to current technologies for the early diagnosis of lung cancer.
ISBN:3642220916
9783642220913
ISSN:0302-9743
1011-2499
1611-3349
DOI:10.1007/978-3-642-22092-0_63