Computational Data Science Laboratory (CDS-Lab), ICAR-CNR
The activity concerns the analysis, design, and implementation of machine learning methods for multimedia applications involving bio-medical images and image sequences.
we proposed an automatic approach to gridding (also known as addressing or spot finding) in microarray images,
based on the Orientation Matching and the Discrete Fourier Transforms .
we proposed an approach to the characterization of uncertain lesions, to
detect characteristic profiles of benign and malignant lesions .
Based on suitable multi-value descriptors (scalar, interval, and histogram data) extracted by dermoscopic images, it consists in
selecting through discriminant analysis the most discriminating features and detecting through dynamic clustering the characteristic profiles.
Moreover, we proposed the SDI algorithm for the automatic segmentation of skin lesions in dermoscopic images ,
extensively tested on the lesion segmentation dataset made available for the
ISIC 2017 challenge on Skin Lesion Analysis Towards Melanoma Detection.
segmentation and classification of microscopy images aimed at
identifying specific factors that control the differentiation of Embryonic Stem Cells via large-scale screenings .
HeLa cell image sequences:
segmentation, tracking, and lineage of HeLa cells from phase contrast microscopy time-lapse data .
Omics Imaging: Integrating Imaging and Omics Data .
- Computer Vision and Pattern Recognition Laboratory (CVPRLab), University of Naples Parthenope, Naples, Italy
- Dept. of Dermatology, Second University of Naples, Naples, Italy
- Dept. of Political Science “J. Monnet”, Second University of Naples, Caserta, Italy
- Inst. of Genetics and Biophysics, National Research Council, Naples, Italy
Publications on Biomedical Imaging:
 L. Casalino, M. R. Guarracino, and L. Maddalena, Imaging for High-Throughput Screening of Pluripotent Stem Cells, SIAM Conference on Imaging Science - IS18, Bologna, June 2018.
 L. Antonelli, M. R. Guarracino, L. Maddalena, and M. Sangiovanni, Integrating Imaging and Omics Data: A Review, 17th International Symposium on Mathematical and Computational Biology - BIOMAT 2017, Moscow (Russia), November 2017.
 M. R. Guarracino and L. Maddalena, Segmenting Dermoscopic Images, arXiv:1703.03186, 2017.
 L. Casalino, P. D’Ambra, M. R. Guarracino, A. Irpino, L. Maddalena, F. Maiorano, G. Minchiotti, E. J. Patriarca, Image Analysis and Classification for High-Throughput Screening of Embryonic Stem Cells, in V. Zazzu, M.B. Ferraro, and M.R. Guarracino (Eds.),Mathematical Models in Biology, Springer, pp 17-31, 2015.
 M. Sangiovanni, L. Maddalena, M. Guarracino, Following the Changes: HeLa cells Lineage from Phase Contrast Microscopy Time-Lapse Data in “6th International Workshop on Data Analysis Methods for Software Systems”, pag. 45, dic. 2014.
 Image Segmentation and Classification for High-Throughput Screening of Microscopy Imagery, Conference Horizon 2020@DIITET, CNR, May 26-27, 2014.
 V. Cozza, M.R. Guarracino, L. Maddalena, A. Baroni, Dynamic Clustering Detection through Multi-valued Descriptors of Dermoscopic Images, Statistics in Medicine, John Wiley & Sons, Ltd., Vol. 30, Issue 20, pagg. 2536–2550, DOI: 10.1002/sim.4285, 2011. [Impact Factor: 2.328]
 L. Maddalena, A. Petrosino, Metodi per l'Analisi di Immagini da Microarray, Tutorial “Metodi e Strumenti per l’analisi dei Dati di Espressione Genica”, Naples, December 2007.
Last update: January 19, 2018