D. Romani
University of Bologna
20 Papers
128 Citations
D. Romani is an academic researcher from University of Bologna. The author has contributed to research in topics: Image resolution & Dosimetry. The author has an hindex of 8, co-authored 20 publications.
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Papers
An SVM classifier to separate false signals from microcalcifications in digital mammograms
Armando Bazzani,Alessandro Bevilacqua,Dante Bollini,Rosa Brancaccio,Renato Campanini,Nico Lanconelli,A. Riccardi,D. Romani +7 more
TL;DR: This paper compares the SVM classifier with an MLP (multi-layer perceptron) in the false-positive reduction phase of the detection scheme: a detected signal is considered either microcalcification or false signal, according to the value of a set of its features.
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•Proceedings Article
Automatic detection of clustered microcalcifications in digital mammograms using an SVM classifier.
Armando Bazzani,Alessandro Bevilacqua,Dante Bollini,Rosa Brancaccio,Renato Campanini,Nico Lanconelli,A. Riccardi,D. Romani,Gianluca Zamboni +8 more
- 01 Jan 2000
TL;DR: This paper investigates the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms using a combination of two different methods, based on difference-image techniques and gaussianity statistical tests.
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Patent
Method and apparatus for the automatic detection of microcalcifications in digital signals of mammary tissue
Armando Bazzani,Alessandro Bevilacqua,Rosa Brancaccio,Renato Campanini,Nico Lanconelli,A. Riccardi,D. Romani +6 more
- 27 Dec 2000
TL;DR: In this paper, a method for automatic detection of microcalcifications in a digital signal representing at least one image of at least a portion of mammary tissue was proposed, using a classifier known as a Support Vector Machine (SVM).
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System for automatic detection of clustered microcalcifications in digital mammograms
Armando Bazzani,Dante Bollini,Rosa Brancaccio,Renato Campanini,Nico Lanconelli,D. Romani,Alessandro Bevilacqua +6 more
TL;DR: This paper investigates the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms with a sensitivity of 91.4% with 0.4 false positive cluster per image on the 40 images of the Nijmegen database.
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An Innovative CCD-Based High-Resolution CT System for Analysis of Trabecular Bone Tissue
Fabio Baruffaldi,Matteo Bettuzzi,Davide Bianconi,Rosa Brancaccio,S. Cornacchia,Nico Lanconelli,Lucia Mancini,Maria Pia Morigi,A. Pasini,Egon Perilli,D. Romani,Alberto Rossi,Franco Casali +12 more
TL;DR: In this paper, a linear system with a pixel size of 22.5 mum and a field-of-view (FOV) 13 cm long and about 1 mm high is presented.
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