DETECTION OF GGO CANDIDATE REGIONS BY USING EDGE ENHANCEMENT FILTER AND STATISTICAL FEATURES

Detection of abnormal areas such as lung nodule, ground glass opacity on multi detector computed tomography (MDCT) images is a difficult task for radiologists, It is because subtle lesions such as small lung nodules, ground glass opacity (GGO) tend to be low in contrast, and a large number of computed tomography (CT) images require a long visual screening times. To detect the abnormalities by use of computer aided diagnosis (CAD) system, some technical methods have been proposed. Despite of these efforts, their approach did not succeed because of difficulty of image processing in detecting of the GGO areas exactly. Thus they did not reach to the stage of automatic detection employing unknown thoracic MDCT data sets. In this paper, we develop a CAD system for automatic detecting of GGO candidates areas from, thoracic MDCT images by use of a selective enhancement filter and statistical features which is obtained density features. The proposed technique applied to 29 MDCT image sets. Prom this database, classification rates of a true positive rate of 82%, false positive rate of 42.89% and number of false positive 2.6/slice under the receiver operating characteristic analysis were achieved. The aim of this study is segmentation of lungs region and detection of abnormal area using thoracic MDCT image sets. This study also tried to decrease the amount of false positive rates and increase the amount of true positive rates so that the accuracy of performance.