Computer-Aided Detection (CAD) systems are becoming widespread supporting tools to radiologists' diagnosis,
especially in screening contexts. However, a large amount of false positive (FP) alarms would inevitably lead
both to an undesired possible increase in time for diagnosis, and to a reduction in radiologists' confidence in
CAD as a useful tool. Most CAD systems implement as final step of the analysis a classifier which assigns a
score to each entry of a list of findings; by thresholding this score it is possible to define the system performance
on an annotated validation dataset in terms of a FROC curve (sensitivity vs. FP per scan). To use a CAD as
a supportive tool for most clinical activities, an operative point has to be chosen on the system FROC curve,
according to the obvious criterion of keeping the sensitivity as high as possible, while maintaining the number
of FP alarms still acceptable. The strategy proposed in this study is to choose an operative point with high
sensitivity on the CAD FROC curve, then to implement in cascade a further classification step, constituted by
a smarter classifier. The key issue of this approach is that the smarter classifier is actually a meta-classifier of
more then one decision system, each specialized in rejecting a particular type of FP findings generated by the
The application of this approach to a dataset of 16 lung CT scans previously processed by the <sup>VBNA</sup>CAD
system is presented. The lung CT <sup>VBNA</sup>CAD performance of 87.1% sensitivity to juxtapleural nodules with 18.5
FP per scan is improved up to 10.1 FP per scan while maintaining the same value of sensitivity. This work has
been carried out in the framework of the MAGIC-V collaboration.
The computer-aided detection (CAD) system we applied on the ANODE09 dataset is devoted to identify pulmonary
nodules in low-dose and thin-slice computed tomography (CT) images: we developed two different
systems for internal (CAD<sub>I</sub>) and juxtapleural nodules (CAD<sub>JP</sub>) in the framework of the italian MAGIC-5 collaboration.
The basic modules of CADI subsystem are: a 3D dot-enhancement algorithm for nodule candidate
identification and an original approach, we referred as Voxel-Based Neural Approach (VBNA), to reduce the
amount of false-positive findings based on a neural classifier working at the voxel level. To detect juxtapleural
nodules we developed the CADJP subsystem based on a procedure enhancing regions where many pleura surface
normals intersect, followed by a VBNA classification. We present both the FROC curves we obtained on the 5
annotated ANODE09 example dataset, and on all the ANODE09 50 test cases.
A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical
Computed Tomography (CT) images was developed in the framework of the MAGIC-5 Italian project. One of the main
goals of this project is to build a distributed database of lung CT scans in order to enable automated image analysis
through a data and cpu GRID infrastructure.
The basic modules of our lung-CAD system, a dot-enhancement filter for nodule candidate selection and a neural
classifier for false-positive finding reduction, are described. The system was designed and tested for both internal and
sub-pleural nodules. The results obtained on the collected database of low-dose thin-slice CT scans are shown in terms of
free response receiver operating characteristic (FROC) curves and discussed.