NDLI: Learning orientation invariant contextual features for nodule detection in lung CT scans

Content Provider	IEEE Xplore Digital Library
Author	Junjie Bai Xiaojie Huang Shubao Liu Qi Song Bhagalia, R.
Copyright Year	2015
Description	Author affiliation: GE Global Res., Niskayuna, NY, USA (Junjie Bai; Xiaojie Huang; Shubao Liu; Qi Song; Bhagalia, R.)
Abstract	This work combines model-based local shape analysis and data-driven local contextual feature learning for improved detection of pulmonary nodules in low dose computed tomography (LDCT) chest scans. We reduce orientation-induced appearance variability by performing intensity-weighted principal component analysis (PCA) to estimate the local orientation at each candidate location. Random comparison primitives defined in a local coordinate system are used to describe the local context around a nodule candidate. A random forest is trained to learn and combine a subset of these primitives into discriminative orientation invariant contextual features and classify nodule candidates. Validation using 99 CT scans from the publicly available Lung Image Database Consortium (LIDC) demonstrates the benefit of combining geometric modeling and data-driven machine learning. The proposed method reduces more than 80% of false positives of the baseline model-based method consistently over a wide range of sensitivity levels (70%-90%).
Starting Page	1135
Ending Page	1138
File Size	879260
Page Count	4
File Format	PDF
ISBN	9781479923748
DOI	10.1109/ISBI.2015.7164072
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lungs Computed tomography Computational modeling Measurement Context Feature extraction Biomedical imaging random forest Nodule detection lung CT orientation invariance contextual feature
Content Type	Text
Resource Type	Article

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