An Automatic Detection Method for GGO Candidate Regions Employing Four Statistical Features from the Thoracic MDCT Images

Abstract

Detection of abnormal areas such as lung nodule, ground glass opacity (GGO) on multi detector computed tomography (MDCT) images is a difficult task for radiologists. It is because subtle lesions such as small lung nodules tend to be low in contrast, and a large number of CT slice images required for visual screening times. In order to detect the abnormalities by use of computer aided diagnosis (CAD) system, some technical method have been proposed in medical field. Despite of these efforts, their approach did not succeed because of difficulty of image processing in detecting 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 detection of GGO areas from thoracic MDCT images by use of four statistical features. The proposed technique applied 32 thoracic MDCT image sets in the performed experiment, and 77% of recognition rates were achieved. Obtained some experimental results are shown along with a discussion.