CT Based Lung Cancer Detection Using Spatially Localized Integral Transforms

No Thumbnail Available



Journal Title

Journal ISSN

Volume Title


Addis Ababa University


Lung cancer is the leading cause of death among other cancer types. Its survival rate is very limited unless diagnosed/detected early. As a result, early detection is imminent for minimizing the deaths caused by lung cancer. For realizing the early detection and early diagnosis, sophisticated and complex imaging modalities like low dose CT are often utilized. However, the big problem lays ahead on the image interpretation. Due to different factors like image quality, suppressed image features in the spatial domain, radiologists eye sight and radiologists’ expertise level, misdiagnosis and error are the major difficulties to overcome. In that regard, many research works have been reported in the literature to deal with the image interpretation issues. Among other researches, CADe and CADx are considered the most remarkable methods for early detection and diagnosis. However, most of the CADe and CADx systems are not clinically implemented. This is because of their inefficiency, inaccuracy and non-robustness. Aiming for a more robust and accurate detection of lung cancers, a new computer aided scheme has been suggested in this thesis. The scheme implements a rotation invariant, joint space-frequency localized integral transform together with spatial image enhancement for feature extraction of CT lung images. The algorithm has been implemented on a Matlab platform (Matlab 2013a) and validated on CT lung images acquired from TCIA database. Results showed that the algorithm achieved a sensitivity of 97.1%, specificity of 83.33% and overall accuracy of 96.68% in detecting lung cancers showing its great promise.



Lung cancer, CADe, Computed Tomograpghy, Rotation-invariance, Space Frequency Localized Integral Transform, Feature Extraction, Spatial Image Enhancement