Interpretation of plain x-ray images using fuzzy logic to detect and classify bone tumors

Yeck, Yin Ping (2010) Interpretation of plain x-ray images using fuzzy logic to detect and classify bone tumors. Masters thesis, Universiti Malaysia Sarawak.

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Abstract

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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