A Novel CT-Based Descriptors for Precise Diagnosis of Pulmonary Nodules

Document Type

Conference Proceeding

Source of Publication

Proceedings - International Conference on Image Processing, ICIP

Publication Date

9-1-2019

Abstract

© 2019 IEEE. Early diagnosis of pulmonary nodules is critical for lung cancer clinical management. In this paper, a novel framework for pulmonary nodule diagnosis, using descriptors extracted from single computed tomography (CT) scan, is introduced. This framework combines appearance and shape descriptors to give an indication of the nodule prior growth rate, which is the key point for diagnosis of lung nodules. Resolved Ambiguity Local Binary Pattern and 7th Order Markov Gibbs Random Field are developed to describe the nodule appearance without neglecting spatial information. Spherical harmonics expansion and some primitive geometric features are utilized to describe how the nodule shape is complicated. Ultimately, all descriptors are combined using denoising autoencoder to classify the nodule, whether malignant or benign. Training, testing, and parameter tuning of all framework modules are done using a set of 727 nodules extracted from the Lung Image Database Consortium (LIDC) dataset. The proposed system diagnosis accuracy, sensitivity, and specificity were 94.95%, 94.62%, 95.20% respectively, all of which show that our system has promise to reach the accepted clinical accuracy threshold.

ISBN

9781538662496

ISSN

1522-4880

Publisher

IEEE Computer Society

Volume

2019-September

First Page

1400

Last Page

1404

Disciplines

Computer Sciences

Keywords

Autoencoder, Computer Aided Diagnosis, Computer Tomography, MGRF, RALBP, Spherical Harmonics

Scopus ID

85076803179

Indexed in Scopus

yes

Open Access

no

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