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12 May 2004Application of the LDM algorithm to identify small lung nodules on low-dose MSCT scans
In this work, we present a computer-aided detection (CAD) algorithm for small lung nodules on low-dose MSCT
images. With this technique, identification of potential lung nodules is carried out with a local density maximum (LDM)
algorithm, followed by reduction of false positives from the nodule candidates using task-specific 2-D/3-D features along
with a knowledge-based nodule inclusion/exclusion strategy. Twenty-eight MSCT scans (40/80mAs, 120kVp, 5mm
collimation/2.5mm reconstruction) from our lung cancer screening program that included at least one lung nodule were
selected for this study. Two radiologists independently interpreted these cases. Subsequently, a consensus reading by
both radiologists and CAD was generated to define a “gold standard”. In total, 165 nodules were considered as the “gold
standard” (average: 5.9 nodules/case; range: 1-22 nodules/case). The two radiologists detected 146 nodules (88.5%) and
CAD detected 100 nodules (60.6%) with 8.7 false-positives/case. CAD detected an additional 19 nodules (6 nodules >
3mm and 13 nodules < 3mm) that had been missed by both radiologists. Preliminary results show that the CAD is
capable of detecting small lung nodules with acceptable number of false-positives on low-dose MSCT scans and it can
detect nodules that are otherwise missed by radiologists, though a majority are small nodules (< 3mm).
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Binsheng Zhao, Michelle S. Ginsberg, Robert A. Lefkowitz, Li Jiang, Cathleen Cooper, Lawrence H. Schwartz, "Application of the LDM algorithm to identify small lung nodules on low-dose MSCT scans," Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.535558