Please use this identifier to cite or link to this item: https://utcc-dspacecris.eval.plus/handle/6626976254/528
Title: Investigation of Chest X-ray Images Based on Medical Knowledge and Balanced Histograms
Authors: Tonpho, Thanatchai 
Leelasantitham, Adisorn 
Kiattisin, Supaporn 
Issue Date: 2010
Publisher: University of the Thai Chamber of Commerce
University of the Thai Chamber of Commerce
Source: Thanatchai Tonpho, Adisorn Leelasantitham, Supaporn Kiattisin (2010) Investigation of Chest X-ray Images Based on Medical Knowledge and Balanced Histograms. UTCC Engineering Research Papers.
Abstract: The primary checking for our health at hospital needs to include a chest x-ray as routine diagnosis because it effectively illustrates the lung diseases especially tuberculosis or lung cancer which are asymptomatic earlier. It is a convenient and quick process with a low cost in comparison with other studies. This paper presents an investigation of the radiographs of lung from the chest x-ray using on medical knowledge and balanced histogram. Selected images of lungs are depicted by the use of an active contour (e.g. snake algorithm) to find two regions of lungs (left and right). Then, such two regions of lungs are represented for two histograms which are profiles of two lung patterns. Such two histograms are compared for normal and abnormal lungs using a method of center of gravity (COG) to demonstrate the difference of both lung radiographs. If two histograms are balance, then the result is a normal case. However, if they are not balance, then it is an abnormal case. For the experimental results, the overall accuracy is at approximately 95% which there are 100 samples of patients for testing their lung images. All samples are previously checked from the medical doctors.
URI: https://scholar.utcc.ac.th/handle/6626976254/528
ISSN: 1906-1625
Rights: This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.
Appears in Collections:EN: Journal Articles

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