Diffuse Lung Diseases (DLD), impute to 15% of respiratory practice and are accountable for a large class of disorders, primarily affecting lung parenchyma. As a part of the diagnostic workup by the physician, a chest CT image is often required in addition to a thorough medical history and physical examination. The reliable identification of the features among interstitial lung diseases and the patterns they may take is challenging, particularly given the volume of data on a CT scan that must be processed by the radiologist. It has been shown that even among expert chest radiologists there is significant inter-observer and intra-observer variability.
To make an objective quantitative and qualitative assessment of lung disease patterns, an accurate and reliable computer aided diagnostic system is likely to be extremely useful to assist with dealing with data volume for an expert radiologist. There will also be the opportunity to improve sensitivity and specificity in a non-expert radiologist group. Literature suggests that computer based pattern classifiers can discern image abnormalities due to lung diseases such as consolidation, cyst, emphysema, fibrosis, ground glass opacity, honey combing, nodularity, reticulation, scar and tree-in-bud.
Researchers have focused on developing algorithms to quantify and analyse the surface changes of the lung, since DLD patterns often manifest as texture differences within the lung parenchyma. Research reported in this thesis has incorporated texture quantification, fractal analysis and scale invariant feature transform methods as complementary feature extraction techniques to improve the classification accuracy, especially in the presence of large number of classes associated with interstitial diseases. Classification of ten lung pathologies and healthy lung regions are validated based on different combination of diseases using leave-one-out and 5-fold cross validation techniques and an Artificial Neural Network (ANN).
Classification accuracy based on features selected using scale invariant feature transform method alone generates 99% accuracy for up to four classes and more than 71% for up to eleven classes using an ANN. Classification accuracy is 85% for eleven classes using a combination of scale invariant feature transform, texture and fractal based features. Classification accuracies improve for higher number of classes (> 5) when the combination of above mentioned features are incorporated. Detailed classification accuracies for several DLD features compared to a healthy lung, and combinations of DLD features, such as fibrosis, reticulation, honey combing in comparison with healthy lung are evaluated throughout this thesis. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/19286 |
| Date | January 2016 |
| Creators | Alemzadeh, Mehrdad |
| Contributors | Kamath, Markad v., Computer Science |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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