6533b7d0fe1ef96bd125a5b5
RESEARCH PRODUCT
MULTI-SCALE ANALYSIS OF LUNG COMPUTED TOMOGRAPHY IMAGES
Alessandra ReticoA. Preite MartinezFrancesco BagagliI. De MitriR. MagroIlaria GoriG. GarganoMatteo SantoroC. FulcheriSimone StumboS. DonadioMaria Evelina FantacciMaria Evelina Fantaccisubject
LungReceiver operating characteristicmedicine.diagnostic_testComputer sciencebusiness.industryFOS: Physical sciencesPattern recognitionComputed tomographyCADFilter (signal processing)Physics - Medical PhysicsScale analysis (statistics)Reduction (complexity)Computerized Tomography (CT) and Computed Radiography (CR ).medicine.anatomical_structuremedicineSegmentationMedical Physics (physics.med-ph)Artificial intelligencebusinessInstrumentationMedical-image reconstruction methods and algorithms computer-aided soMathematical Physicsdescription
A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2007-01-01 |