Multi-scale analysis of lung computed tomography images

I. Gori; F. Bagagli; M. E. Fantacci; A. Preite Martinez; A. Retico; I. De Mitri; S. Donadio; C. Fulcheri; G. Gargano; R. Magro; M. Santoro; S. Stumbo

doi:10.1088/1748-0221/2/09/p09007

arXiv · 0904.2476

Multi-scale analysis of lung computed tomography images

I. Gori·F. Bagagli·M. E. Fantacci·A. Preite Martinez·A. Retico·I. De Mitri·S. Donadio·C. Fulcheri·G. Gargano·R. Magro·M. Santoro·S. Stumbo

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Abstract

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.

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I. Gori, F. Bagagli, M. E. Fantacci, A. Preite Martinez, A. Retico, I. De Mitri, S. Donadio, C. Fulcheri, G. Gargano, R. Magro, M. Santoro, S. Stumbo. 2009-04-16. Multi-scale analysis of lung computed tomography images. https://doi.org/10.1088/1748-0221/2/09/p09007

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