Characterization Of Lung Nodule Malignancy Using Hybrid Shape And Appearance Features

Keywords

Conformal mapping; Deep convolutional neural network; Nodule characterization; Random forest; Spherical harmonics

Abstract

Computed tomography imaging is a standard modality for detecting and assessing lung cancer. In order to evaluate the malignancy of lung nodules,clinical practice often involves expert qualitative ratings on several criteria describing a nodule’s appearance and shape. Translating these features for computer-aided diagnostics is challenging due to their subjective nature and the difficulties in gaining a complete description. In this paper,we propose a computerized approach to quantitatively evaluate both appearance distinctions and 3D surface variations. Nodule shape was modeled and parameterized using spherical harmonics,and appearance features were extracted using deep convolutional neural networks. Both sets of features were combined to estimate the nodule malignancy using a random forest classifier. The proposed algorithm was tested on the publicly available Lung Image Database Consortium dataset,achieving high accuracy. By providing lung nodule characterization,this method can provide a robust alternative reference opinion for lung cancer diagnosis.

Publication Date

1-1-2016

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

9900 LNCS

Number of Pages

662-670

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-319-46720-7_77

Socpus ID

84996503636 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84996503636

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