Pulmonary Nodules: Automated Detection on CT Images with Morphologic Matching Algorithm—Preliminary Results

Abstract

Institutional review board approval was obtained. Informed patient consent was not required for this retrospective study, which involved review of previously obtained image data. Patient confidentiality was protected; the study was compliant with the Health Insurance Portability and Accountability Act. An automated pulmonary nodule detection program that takes advantage of three-dimensional volumetric data was developed and tested with multi-detector row computed tomographic (CT) images from 20 patients (13 men, seven women; age range, 40-75 years) with pulmonary nodules. A total of 164 nodules 3 mm in diameter and larger were detected by two radiologists in consensus and were used as a reference standard to evaluate the computer-aided detection (CAD) program. The CAD algorithm was structured to process nodules that were categorized into three types: isolated, juxtapleural, and juxtavascular. Overall sensitivity for nodule detection with the CAD program was 95.1% (156 of 164 nodules). The sensitivity according to nodule size was 91.2% (52 of 57 nodules) for nodules 3 mm to less than 5 mm and 97.2% (104 of 107 nodules) for nodules 5 mm and larger. The number of false-positive detections per patient was 6.9 for false nodule structures 3 mm and larger and 4.0 for false nodule structures 5 mm and larger.

Computerized Detection of Lung Nodules in Thin-Section CT Images by Use of Selective Enhancement Filters and an Automated Rule-Based Classifier

“…CAD schemes for lung nodule detection in thin-section CT images have been developed by some investigators [14][15][16][17][18][19][20][21][22]. A major disadvantage of some current CAD schemes is the use of a relatively small database.…”

“…Some employed CT scans with only partial lungs and used the number of false positive per slice (instead of per scan) as their false positive rate [17,22]. Some did not describe how their CAD schemes were evaluated with certain testing methods such as resubstitution, leave-one-out, or cross-validation [16,21]. We believe that comparison of our CAD scheme with these CAD schemes is inappropriate.…”

“…Since 2001, some investigators have reported their efforts in the development of CAD schemes for lung nodule detection in thin-section CT images [14][15][16][17][18][19][20][21][22]. Because the section thickness in thin-section CT is small, three-dimensional (3D) image processing and analysis techniques become applicable.…”

Automated detection of lung cancer using statistical and morphological image processing techniques

“…A CAD program that can read a CT data set and automatically detect pulmonary nodules on the images is further investigated [20] . The system should better performance in small nodules.…”

“…The system should better performance in small nodules. The developed CAD systems usually focus on detecting small nodules that may be missed during visual interpretation [17][18][19][20] . Those works provide a CAD system that didn't consider the different features that will help the radiologist to speed up his/her diagnosis and reduce his/her workload.…”