Lung disease stratification using amalgamation of Riesz and Gabor transforms in machine learning framework. (1st October 2017)

• Record Type: Journal Article
• Authors: Than, Joel C.M.; Saba, Luca; Noor, Norliza M.; Rijal, Omar M.; Kassim, Rosminah M.; Yunus, Ashari; Suri, Harman S.; Porcu, Michele; Suri, Jasjit S.
• Is Part Of: Computers in biology and medicine. Volume 89(2017)
• Page Start: 197
• Page End: 211
• Publication Date: 2017-10-01
• Subjects: Lung cancer -- Computer tomography -- CADx cascaded system -- Riesz transforms -- Gabor transforms -- Amalgamation -- Risk stratification -- Performance; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.08.014
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Lung disease risk stratification is important for both diagnosis and treatment planning, particularly in biopsies and radiation therapy. Manual lung disease risk stratification is challenging because of: (a) large lung data sizes, (b) inter- and intra-observer variability of the lung delineation and (c) lack of feature amalgamation during machine learning paradigm. This paper presents a two stage CADx cascaded system consisting of: (a) semi-automated lung delineation subsystem (LDS) for lung region extraction in CT slices followed by (b) morphology-based lung tissue characterization, thereby addressing the above shortcomings. LDS primarily uses entropy-based region extraction while ML-based lung characterization is mainly based on an amalgamation of directional transforms such as Riesz and Gabor along with texture-based features comprising of100 greyscale features using the K-fold cross-validation protocol (K = 2, 3, 5 and 10). The lung database consisted of 96 patients: 15 normal and 81 diseased. We use five high resolution Computed Tomography (HRCT) levels representing different anatomy landmarks where disease is commonly seen. We demonstrate the amalgamated ML stratification accuracy of99.53%, an increase of2% against the conventional non-amalgamation ML system that uses alone Riesz-based feature embedded with feature selection based on feature strength. The robustness of the system was determined based on the reliability and stability that showed a reliability index of0.99 and the deviation in risk stratification accuracies less than5% . Our CADx system shows 10% better performance when compared against the mean of five other prominent studies available in the current literature covering over one decade.