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.
| Identifer | oai:union.ndltd.org:ADTP/219586 |
| Date | January 2007 |
| Creators | Al-Kabir, Zul Waker Mohammad, N/A |
| Publisher | University of Canberra. Information Sciences & Engineering |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | ), Copyright Zul Waker Mohammad Al-Kabir |
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