CT Based Lung Cancer Detection Using Spatially Localized Integral Transforms
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Date
2018-06
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Addis Ababa University
Abstract
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.
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Keywords
Lung cancer, CADe, Computed Tomograpghy, Rotation-invariance, Space Frequency Localized Integral Transform, Feature Extraction, Spatial Image Enhancement