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Medical Image Analysis Through A Texture Based Computer Aided Diagnosis Framework
Danilo Avola, Luigi Cinque, Giuseppe Placidi
Pages - 144 - 152     |    Revised - 15-09-2012     |    Published - 24-10-2012
Volume - 6   Issue - 5    |    Publication Date - October 2012  Table of Contents
Texture Analysis, Medical Imaging, Pattern Recognition, Feature Extraction, Segmentation
Current medical imaging scanners allow to obtain high resolution digital images with a complex informative content expressed by the textural aspect of the membranes covering organs and tissues (hereinafter objects). These textural information can be exploited to develop a descriptive mathematical model of the objects to support heterogeneous activities within medical field. This paper presents a framework based on the texture analysis to model the objects contained in the layout of diagnostic images. By each specific model, the framework automatically also defines a connected application supporting, on the related objects, different fixed targets, such as: segmentation, mass detection, reconstruction, and so on. The framework is tested on MRI images and results are reported.
CITED BY (3)  
1 Placidi, G., Avola, D., Petracca, A., Spezialetti, M., Cinque, L., & Levialdi, S. Novel Approches in Image Processing (IP) and Human Centred Systems (HCSs).
2 Nair, V. V., & Thushana, Y. S. (2013). Texture features from Chaos Game Representation Images of Genomes. International Journal of Image Processing (IJIP), 7(2), 183.
3 Avola, D., Cinque, L., & Placidi, G. (2013). Customized First and Second Order Statistics Based Operators to Support Advanced Texture Analysis of MRI Images. Computational and mathematical methods in medicine, 2013.
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A. Histace, B. Matuszewski and Y. Zhang. “Segmentation of Myocardial Boundaries in Tagged Cardiac MRI Using Active Contours: A Gradient-Based Approach Integrating Texture Analysis.” International Journal of Biomedical Imaging (IJBI'09), Hindawi Publishing Corporation, pp. 1-8, 2009.
D. Avola and L. Cinque. “Encephalic NMR Image Analysis by Textural Interpretation.” In Proceedings of the 2008 ACM Symposium on Applied Computing. Wainwright (SAC’08,March 16-20), ACM Press, 2008, pp. 1338-1342.
D. Avola and L. Cinque. “Encephalic NMR Tumor Diversification by Textural Interpretation.In Proceedings of the 15th International Conference on Image Analysis and Processing (ICIAP'09), Springer-Verlag, vol.5716, 2009, pp. 394-403.
D. Avola, L. Cinque and M. Di Girolamo. “A Novel T-CAD Framework to Support Medical Image Analysis and Reconstruction”. In Proceedings of the 16th International Conference on Image Analysis and Processing (ICIAP’11), Springer-Verlag vol. 6979, 2011, pp. 414-423.
D. Avola, L. Cinque and M. Di Girolamo. “Texture Based Approaches to Support Medical Image Analysis. Internal Technical Report in Medical Image Proessing. DSI - Sapienza University of Rome, ITR-MIP '10, Int. Res. on Medical Imaging, 2010.
D.J. Heeger and J.R. Bergen. “Pyramid-Based Texture Analysis/Synthesis.” In Proceeding of 22th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH'95), NY, USA, ACM Press, 1995, pp. 229-238.
J. Niranjan and B. Michael. “Non-Parametric Mixture Model Based Evolution of Level Sets and Application to Medical Image.” International Journal of Computer Vision (IJCV'10),Springer Verlag, 88(1), pp. 52-68, 2010.
J. Schmid, J. Kim and N. Magnenat-Thalmann. “Robust Statistical Shape Models for MRI Bone Segmentation in Presence of Small Field of View.” International Journal of Medical Image Analysis (IJMIA'11), 15(1), pp. 155-168, 2011.
J. Wu, S. Poehlman, M.D. Noseworthy and M.V. Kamath. “Texture Feature Based Automated Seeded Region Growing in Abdominal MRI Segmentation.” Journal in Biomedical Science and Engineering (JBiSE'09), Research Publishing, vol. 2, pp. 1-8,2009.
L. Tesar, D. Smutek, A. Shimizu and H. Kobatake. “Medical Image Segmentation Using Co-Occurrence Matrix Based Texture Features Calculated on Weighted Region.” In Proceedings of the 3th Conference on IASTED International Conference: Advances in Computer Science and Technology (ACST'07), ACTA Press, 2007, pp. 243-248.
Q. Li and Z. Shi. “Texture Image Retrieval Using Compact Texton Co-Occurrence Matrix Descriptor.” In Proceedings of the 11th ACM International Conference on Multimedia Information Retrieval (MIR'10, March 29-31), USA, ACM Press, 2010, pp. 83-90.
Y. Zhou and J. Bai. “Atlas-Based Fuzzy Connectedness Segmentation and Intensity Nonuniformity Correction Applied to Brain MRI.” IEEE Transaction on Biomedical Engineering. Sponsored by IEEE Engineering in Medicine and Biomedicine Society, IEEE CS Press, 54(1), pp. 122-129, 2007.
Mr. Danilo Avola
Dept. of Life, Health and Environmental Sciences - Italy
Professor Luigi Cinque
Sapienza University of Rome - Italy
Miss Giuseppe Placidi
Dept. of Life, Health and Environmental Sciences - Italy