Detection of Lung Nodules in Thoracic MDCT Images Based on Temporal Changes from Previous and Current Images
Shinya Maeda, Yasuyuki Tomiyama, Hyoungseop Kim,
Noriaki Miyake, Yoshinori Itai, Joo Kooi Tan, Seiji Ishikawa,
and Akiyoshi Yamamoto
Department of Control Engineering, Kyushu Institute of Technology, 1-1 Sensui, Tobata, Kitakyushu 804-8550, Japan
Temporal subtraction enhances temporal change by subtracting images captured at different times. Medical images captured currently (current images) and in previous examination (previous images) are subtracted to enhance new lesions and temporal change in existing lesion shadows. Temporal subtraction using chest MultiDetector-Row Computed Tomography (MDCT) images and currently being developed is to be applied to nodule detection in pulmonary regions. Nodule detection using conventional temporal subtraction, however, yields many false-positive results for those 20 mm or less in diameter, requiring improvement. We discuss improvements in nodule detection accuracy using temporal subtraction, first extracting rough nodules from temporal subtraction images as candidate shadows. Features are then acquired from current, previous, and temporal subtraction images. We use intensity features in previous images and shape features in the current images and in features used in conventional methods. Using acquired features, we build a neural network classifier, then extract final pulmonary candidates in unknown shadows.
Noriaki Miyake, Yoshinori Itai, Joo Kooi Tan, Seiji Ishikawa, and
and Akiyoshi Yamamoto, “Detection of Lung Nodules in Thoracic MDCT Images Based on Temporal Changes from Previous and Current Images,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.6, pp. 707-713, 2011.
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