Interpretation of plain x-ray images using fuzzy logic to detect and classify bone tumors
C Radiographic is a conventional x-ray image, typically the first imaging test used to diagnose bone tumor. Probably the most common use of x-ray image is to assist the medical experts in detecting the early stages of benign tumor growth, identifying tumor suspicious location and monitoring the...
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| Format: | Thesis |
| Language: | English |
| Published: |
Universiti Malaysia Sarawak, (UNIMAS)
2010
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| Online Access: | http://ir.unimas.my/id/eprint/14864/ http://ir.unimas.my/id/eprint/14864/2/Yin%20Ping%20%28fulltext%29.pdf |
| Summary: | C
Radiographic is a conventional x-ray image, typically the first imaging test used to diagnose
bone tumor. Probably the most common use of x-ray image is to assist the medical experts in
detecting the early stages of benign tumor growth, identifying tumor suspicious location and
monitoring the progression of degenerative tumor. Reading of x-ray image is usually done by
medical experts visually. The diagnosis process requires human expert's cognitive. It depends
extremely on the knowledge and long term diagnosis experiences of the medical experts. At
the earliest stage of bone tumors, when they are small and difficult to recognize, the
radiological finding can lead to potential misidentification and increase the frequency of
human error. Meanwhile, different medical experts have different perception of bone tumors
because the variable distributions of tumor appeared in x-ray images have presented
ambiguity. To help in overcoming such problems, an x-ray interpretation method based on
fuzzy logic has been developed in this studyýThis method allows the interpretation of plain xray
images to be performed semi automatically involving minimum number of input variables
in the detection and classification of bone tumor. In order to ensure that all the abnormalities
present as benign or malignant are classified properly, an image enhancement method has
been developed to refine the input image based on direct manipulation of pixel in the partial
domain. The proposed enhancement method employs image filtering technique with
combination of image registration to increase the contrast of tumor region. The developed
method has been extensively tested and compared against the Mamdani's fuzzy inference
methods in term of accuracy using test samples that were obtained from humeral parts with
various intensities on the x-ray images. The result showed that a 87.36% of accuracy rate was
achieved in bone tumor detection and a 98.38% of sensitivity was achieved in the classification of bone tumor. Demonstrations of the experiment results show the feasibility of
the proposed method in detecting the distributed abnormalities and classifying any
abnormalities present as benign and malignant tumor. |
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