Combination of computer-aided detection algorithms for automatic lung nodule identification

Niccolò Camarlinghi, Ilaria Gori, Alessandra Retico, Roberto Bellotti, Paolo Bosco, Piergiorgio Cerello, Gianfranco Gargano, Ernesto Lopez Torres, Rosario Megna, Marco Peccarisi, Maria Evelina Fantacci

Research output: Contribution to journalArticlepeer-review


Purpose The aim of this work is to evaluate the potential of combining different computer-aided detection (CADe) methods to increase the actual support for radiologists of automated systems in the identification of pulmonary nodules in CT scans. Methods The outputs of three different CADe systems developed by researchers of the Italian MAGIC-5 collaboration were combined. The systems are: the CAMCADe (based on a Channeler-Ant-Model which segments vessel tree and nodule candidates and a neural classifier), the RGVPCADe (a Region-Growing- Volume-Plateau algorithm detects nodule candidates and a neural network reduces false positives); the VBNACADe (two dedicated procedures, based respectively on a 3D dot-enhancement algorithm and on intersections of pleura surface normals, identifies internal and juxtapleural nodules, and a Voxel-Based-Neural-Approach reduces false positives. A dedicated OsiriX plugin implemented with the Cocoa environments of MacOSX allows annotating nodules and visualizing singles and combined CADe findings. Results The combined CADe has been tested on thin slice (lower than 2 mm) CTs of the LIDC public research database and the results have been compared with those obtained by the single systems. The FROC (Free Receiver Operating Characteristic) curves show better results than the best of the single approaches. Conclusions Has been demonstrated that the combination of different approaches offers better results than each single CADe system. A clinical validation of the combined CADe as second reader is being addressed bymeans of the dedicated OsiriX plugin.

Original languageEnglish
Pages (from-to)455-464
Number of pages10
JournalInternational journal of computer assisted radiology and surgery
Issue number3
Publication statusPublished - May 2012


  • Computed tomography
  • Computer-aided detection
  • Lung cancer
  • Medical image analysis
  • Pattern recognition

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Surgery


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