Discrimination of Pneumoconiosis X-Ray Images Scanned with a CCD Scanner
Author(s) -
Masahide Minami,
KojiAbe,
Munehiro Nakamura
Publication year - 2012
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2012.p0069
Subject(s) - pneumoconiosis , computer science , scanner , artificial intelligence , computer vision , pattern recognition (psychology) , radiology , medicine , pathology
This paper presents a discrimination of pneumoconiosis X-ray images obtained with a common CCD scanner. Since the current computer-aided diagnosis systems of pneumoconiosis are not practical due to high costs of usage, features for measuring abnormalities of pneumoconiosis are proposed as variables for the discrimination in this paper. In the images, abnormal levels of pneumoconiosis could depend on density distribution in each of intercostal and rib areas. Therefore, the proposed method measures the abnormalities by extracting characteristics of the distribution in the areas. Besides, using the abnormalities, the proposed method discriminates chest X-ray images into normal or abnormal cases of pneumoconiosis. Experimental results of the discriminations for 56 right-lung images have shown that the proposed abnormalities are well extracted for the discrimination.
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