Translator Disclaimer
12 May 2004 Application of the LDM algorithm to identify small lung nodules on low-dose MSCT scans
Author Affiliations +
Abstract
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).
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Binsheng Zhao, Michelle S. Ginsberg, Robert A. Lefkowitz, Li Jiang, Cathleen Cooper, and 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
PROCEEDINGS
6 PAGES


SHARE
Advertisement
Advertisement
Back to Top