The thesis develops a model (that includes a conceptual framework and an implementation) for analysing and classifying traditional X-ray images (MACXI) according to the severity of diseases as a Computer-Aided-Diagnosis tool with three initial objectives. . The first objective was to interpret X-ray images by transferring expert knowledge into a knowledge base (CXKB): to help medical staff to concentrate only on the interest areas of the images. . The second objective was to analyse and classify X-ray images according to the severity of diseases through the knowledge base equipped with an image processor (CXIP). . The third objective was to demonstrate the effectiveness and limitations of several image-processing techniques for analysing traditional chest X-ray images. A database was formed based on collection of expert diagnosis details for lung images. Five important features from lung images, as well as diagnosis rules were identified and simplified. The expert knowledge was transformed into a Knowledge base (KB) for analysing and classifying traditional X-ray images according to the severity of diseases (CXKB). Finally, an image processor named CXIP was developed to extract the features of lung images features and image classification. CXKB contains 63 distinct lung diseases with detailed descriptions. Some 80-chest X-ray images with diagnosis details were collected for the database from different sources, including online medical resources. A total of 61 images were used to determine the important features; 19 chest X-ray images were not used because of low visibility or the difficulty of diagnosis. Finally, only 12 images were selected after examining the diagnosis details, image clarity, image completeness, and image orientation. The most important features of lung diseases are a pattern of lesions with different levels of intensity or brightness. The other major anatomical structures of the chest are the hilum area, the rib area, the trachea area, and the heart area. Seven different severity levels of diseases were determined. Development and simplification of rules based on the image library were analysed, developed, and tested against the 12 images. A level of severity was labelled for each image based on a personal understanding of all the image and diagnosis details. Then, MACXI processed the selected 12 images to determine the level of severity. These 12 images were fed into the CXIP for recognition of the features and classification of the images to an accurate level of severity. Currently, the processor has the ability to identify diseased lung areas with approximately 80% success rate. A step by step demonstration of several image processing techniques that were used to build the processor is given to highlight the effectiveness and limitations of the techniques for analysing traditional chest X-ray images is also presented.
|Date of Award
|Kim-Thang Le (Supervisor) & Dharmendra Sharma AM PhD (Supervisor)