Computer-Assisted Image Processing System for Early Assessment of Lung Nodule Malignancy

Cancers

Ahmed Shaffie¹  Ayman El-Baz¹  Ahmed Soliman¹  Amr Eledkawy²  Victor van Berkel³ 

[1] BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA;Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt;Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, KY 40202, USA;

关键词: lung cancer;    HOG;    LBP;    MGRF;    CSS;    spherical harmonics;   

DOI  :  10.3390/cancers14051117

来源: DOAJ

【 摘 要 】

Lung cancer is one of the most dreadful cancers, and its detection in the early stage is very important and challenging. This manuscript proposes a new computer-aided diagnosis system for lung cancer diagnosis from chest computed tomography scans. The proposed system extracts two different kinds of features, namely, appearance features and shape features. For the appearance features, a Histogram of oriented gradients, a Multi-view analytical Local Binary Pattern, and a Markov Gibbs Random Field are developed to give a good description of the lung nodule texture, which is one of the main distinguishing characteristics between benign and malignant nodules. For the shape features, Multi-view Peripheral Sum Curvature Scale Space, Spherical Harmonics Expansion, and a group of some fundamental morphological features are implemented to describe the outer contour complexity of the nodules, which is main factor in lung nodule diagnosis. Each feature is fed into a stacked auto-encoder followed by a soft-max classifier to generate the initial malignancy probability. Finally, all these probabilities are combined together and fed to the last network to give the final diagnosis. The system is validated using 727 nodules which are subset from the Lung Image Database Consortium (LIDC) dataset. The system shows very high performance measures and achieves $92.55\%$, $91.70\%$, and $93.40\%$ for the accuracy, sensitivity, and specificity, respectively. This high performance shows the ability of the system to distinguish between the malignant and benign nodules precisely.

【 授权许可】

Unknown