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Proceedings Paper

Computer-aided diagnosis for interval change analysis of lung nodule features in serial CT examinations
Author(s): Lubomir Hadjiiski; Ted W. Way; Berkman Sahiner; Heang-Ping Chan; Philip Cascade; Naama Bogot; Ella Kazerooni; Chuan Zhou
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Paper Abstract

A CAD system was developed to extract and analyze features from corresponding malignant and benign lung nodules on temporal pairs of CT scans. The lung nodules on the current and prior CT scans were automatically segmented using a 3-dimensional (3D) active contour model. Three-dimensional run length statistics (RLS) texture features, 3D morphological and gray-level features were extracted from each nodule. In addition, 3D nodule profile features (PROF) that describe the gray level variation inside and outside the nodule surface were extracted by estimating the gradient magnitude values along the radial vectors from the nodule centroid to a band of voxels surrounding the nodule surface. Interval change features were calculated as the difference between the corresponding features extracted from the prior and the current scans of the same nodule. Stepwise feature selection with simplex optimization was used to select the best feature subset from the feature space that combined both the interval change features and features from the single current exam. A linear discriminant classifier was used to merge the selected features for classification of malignant and benign nodules. In this preliminary study, a data set of 103 nodule temporal pairs (39 malignant and 64 benign) was used. A leave-one-case-out resampling scheme was used for feature selection and classification. An average of 5 features was selected from the training subsets. The most frequently selected features included a difference PROF feature and 4 RLS features. The classifier achieved a test Az of 0.85±0.04. In comparison a classifier using features extracted from the current CT scans alone achieved a test Az of 0.78±0.05. This study indicates that our CAD system using interval change information is useful for classification of lung nodules on CT scans.

Paper Details

Date Published: 29 March 2007
PDF: 7 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 651411 (29 March 2007); doi: 10.1117/12.713770
Show Author Affiliations
Lubomir Hadjiiski, Univ. of Michigan (United States)
Ted W. Way, Univ. of Michigan (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Heang-Ping Chan, Univ. of Michigan (United States)
Philip Cascade, Univ. of Michigan (United States)
Naama Bogot, Univ. of Michigan (United States)
Ella Kazerooni, Univ. of Michigan (United States)
Chuan Zhou, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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