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).

Date Published: 12 May 2004
PDF: 6 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535558

Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)