Restoration, Enhancement and Analysis of Lung Nodular Images for Prompt Detection of Abnormalities
Date
2020
Authors
G, Savitha.
Journal Title
Journal ISSN
Volume Title
Publisher
National Institute of Technology Karnataka, Surathkal
Abstract
Detection of lung cancer in the Computed Tomography (CT) images when the lung
nodules are in the sub-solid state (early stage) results in higher survival rate of the
patients. Two Computer Aided Detection (CAD) systems for identifying the sub-solid
nodules in lung CT images are developed as a part of this thesis.
The first system adopts a pipeline approach which is carried-out in two phases.
The first phase employs a series of algorithms for denoising, segmentation of region of
interest and feature selection followed by a classification to separate nodules and nonnodules. In the second phase, Histogram of Gradients method is used to categorize the
nodules identified in first phase as solid or sub-solid. Sensitivity of the system is observed to be more than 90% with just 3 false positive observations per scan. Both supervised and unsupervised classification models adopted for identifying sub-solid nodules
give consistent and reliable results with an average accuracy above 93% when tested
with Lung Image Database Consortium (LIDC) and International - Early Lung Cancer Action Program (I-ELCAP) databases. The accuracy of the system is categorically
higher compared to the present state-of-the-art models employed for sub-solid nodule
classification.
The second system adopts a deep learning approach for identifying sub-solid nodules, making use of a Deep Convolution Neural Network (DCNN) incorporated within
the Conditional Random Field (CRF) framework. Adopting CRF framework reduces
the occurrence of false positives. It is further observed that the overall accuracy of
the system is increased from 83 to 89.5 percentage when tested with LIDC/IDRI and
I-ELCAP databases. Though, the accuracy of the system is lower than the pipeline
based model (mentioned above), the model does not demand any pre or post processing
technique including the region of interest segmentation. The accuracy obtained for this
system is comparatively higher than the state of the art deep learning models employed
for sub-solid nodule classification. Moreover, a detailed cross comparative analysis of
the systems proposed in this thesis is done to analyze their performance.
Description
Keywords
Department of Mathematical and Computational Sciences, Computer Aided Detection System, pulmonary nodule detection, sub-solid/partsolid nodule identification, Computed Tomography Images, Gray Level Co-variance Matrix, Deep Learning Convolution Neural Network, Conditional Random Field