C. D'Elia

Publications (13)7.47 Total impact

• Conference Proceeding: Detection of Clusters of Microcalcifications in Mammograms: A Multi Classifier Approach

  C. D'Elia, C. Marrocco, M. Molinara, F. Tortorella
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  ABSTRACT: Mammography is a not invasive diagnostic technique widely used for early cancer detection in women breast. A particularly significant clue of such disease is the presence of clusters of microcalcifications. The automatic detection and classification of such clusters is a very difficult task because of the small size of the microcalcifications and of the poor quality of the digital mammograms. In literature, all the proposed methods for the automatic detection focus on the single microcalcification. In this paper, an approach that moves the final decision on the regions identified by the segmentation in the phase of clustering is proposed. To this aim, the output of a classifier on the single microcalcifications is used as input data in a clustering algorithms which produce the detected clusters. As final output the system highlights the suspicious clusters, leaving to the specialist the diagnosis responsibility. The approach has been successfully tested on a standard database of 40 mammographic images, publicly available.
  Computer-Based Medical Systems, 2008. CBMS '08. 21st IEEE International Symposium on; 07/2008
• Conference Proceeding: Using Bayesian Network for combining classifiers

  C. De Stefano, C. D'Elia, A. Marcelli, A.S. di Freca
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  ABSTRACT: In the framework of multiple classifier systems, we suggest to reformulate the classifier combination problem as a pattern recognition one. Following this approach, each input pattern is associated to a feature vector composed by the output of the classifiers to be combined. A Bayesian Network is used to automatically infer the probability distribution for each class and eventually to perform the final classification. We propose to use Bayesian Networks because they not only provide a basis for efficient probabilistic inference, but also a natural and compact way to encode exponentially sized joint probability distributions. Two systems adopting an ensemble of Back-Propagation neural network and an ensemble of Learning Vector Quantization neural network, respectively, have been tested on the Image database from the UCI repository. The performance of the proposed systems have been compared with those exhibited by multi-expert systems adopting the same ensembles, but the Majority Vote, the Weighted Majority vote and the Borda Count for combining them.
  Image Analysis and Processing, 2007. ICIAP 2007. 14th International Conference on; 10/2007
• Conference Proceeding: Automated Content Extraction from SAR Data

  B. Aiazzi, S. Baronti, L. Alparone, G. Cuozzo, C. D'Elia, G. Schirinzi
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  ABSTRACT: Segmentation algorithms are often used in many image processing applications like compression, restoration, content extraction, and classification. In particular as for content extraction works carried out in the past decade have demonstrated that multi-frequency fully polarimetric SAR observations are particularly interesting, thanks to physical properties of the backscattered signal at various frequencies and polarizations. To achieve a good classification, the main difficulty is that SAR images are often embedded in heavy speckle. Segmentation of multi/hyperspectral (optical) imagery is obtained by means of algorithms based on image models, which exploit the spatial dependencies of land-covers. Unfortunately, speckle noise hides such spatial dependencies in observed SAR data. With the aim of investigating on a content extraction algorithm capable of discriminating cover classes present in the observed SAR image, heterogeneity features are used here to emphasize spatial dependencies in the data. Thus, observed pixel values are mapped into features, that take "similar" values on "similar" textures. This allows for using the same procedure of the optical case. Obviously, homogeneity/heterogeneity feature and segmentation quality are fundamental for classification accuracy. Here, the problem is tackled through the joint use of information theoretic SAR features and of a segmentation algorithm based on Markov Random Fields (MRFs).
  Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on; 09/2006
• Conference Proceeding: SAR image segmentation through information-theoretic heterogeneity features and tree-structured Markov random fields

  B. Aiazzi, S. Baronti, L. Alparone, G. Cuozzo, C. D'Elia, G. Schirinzi
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  ABSTRACT: First Page of the Article
  Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. 2005 IEEE International; 08/2005
• Conference Proceeding: Application of overcomplete ICA to SAR image compression

  A. Budillon, G. Cuozzo, C. D'Elia, G. Schirinzi
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  ABSTRACT: In this paper the application of a transform coding technique, based on overcomplete independent component analysis (ICA), for the compression of single look intensity synthetic aperture radar (SAR) images is explored. The method has the advantage of representing the image through almost statistically independent coefficients, with an assigned distribution, so that a scalar entropy constrained quantizer, optimized for the coefficients statistics, can be used. Numerical results on ERS-1 data are presented.
  Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. 2005 IEEE International; 08/2005
• Conference Proceeding: An MRF based technique for speckle reduction in SAR images

  C. D'Elia, G. Ferraiuolo, V. Pascazio, G. Schirnzi
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  ABSTRACT: SAR images are affected by speckle that affects radiometric resolution and class discrimination capabilities. Recently, different speckle reduction techniques based on maximum a posteriori (MAP) estimation have been proven to have very good performances. These techniques are based on the introduction of an a priori statistical model of the speckle free image to be estimated. We propose a MAP method using more than one sub-band filtered intensity images and a Markov random field (MRF) a priori model. The method has been experimented on simulated and real images
  Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International; 10/2004
• Conference Proceeding: A method based on tree-structured Markov random field for forest area classification

  G. Cuozzo, C. D'Elia, V. Puzzolo
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  ABSTRACT: The forest cover classification is extremely important for land use planning and management. In this framework, the application of pixel based classifications of middle resolution images is well assessed while the usefulness of segmentation processes and object classification is still improving. In this paper, a method based on tree-structured Markov random field (TS-MRF) is applied to Landsat TM images in order to assess the capability of the TS-MRF segmentation algorithm for discriminating forest-non forest covers in a test area located in the Eastern Italian Alps of Trentino. In particular, the regions of interest are selected from the image using a two step process based on a segmentation algorithm and an analysis process. The segmentation is achieved applying a MRF a-prior model, which takes into account the spatial dependencies in the image, and the TS-MRF optimisation algorithm which segments recursively the image in smaller regions using a binary tree structure. The analysis process links to each object identified by the segmentation a set of features related to the geometry (like shape, smoothness, etc.), to the spectral signature and to the neighbour regions (contextual features). These features were used in this study for classifying each object as forest or non-forest thought a simple supervised classification algorithm based on a thresholds built on the feature values obtained from a set of training objects. This method already allowed the detection of the forest area within the study area with an accuracy of 90%, while better performances could be achieved using more sophisticated classification algorithm, like Neural Networks and Support Vector Machine.
  Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International; 10/2004
• Conference Proceeding: Improved tree-structured segmentation of remote sensing images

  C. D'Elia, G. Poggi, G. Scarpa
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  ABSTRACT: First Page of the Article
  Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International; 08/2003
• Article: A tree-structured Markov random field model for Bayesian image segmentation.

  Ciro D'Elia, Giovanni Poggi, Giuseppe Scarpa
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  ABSTRACT: We present a new image segmentation algorithm based on a tree-structured binary MRF model. The image is recursively segmented in smaller and smaller regions until a stopping condition, local to each region, is met. Each elementary binary segmentation is obtained as the solution of a MAP estimation problem, with the region prior modeled as an MRF. Since only binary fields are used, and thanks to the tree structure, the algorithm is quite fast, and allows one to address the cluster validation problem in a seamless way. In addition, all field parameters are estimated locally, allowing for some spatial adaptivity. To improve segmentation accuracy, a split-and-merge procedure is also developed and a spatially adaptive MRF model is used. Numerical experiments on multispectral images show that the proposed algorithm is much faster than a similar reference algorithm based on "flat" MRF models, and its performance, in terms of segmentation accuracy and map smoothness, is comparable or even superior.
  IEEE Transactions on Image Processing 02/2003; 12(10):1259-73. · 3.04 Impact Factor
• Article: Self-organizing codebooks for trellis-coded VQ

  C. D'Elia, G. Poggi
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  ABSTRACT: Because of its computational complexity, vector quantization (VQ) can work only on relatively small vectors, which severely limits its encoding performance. Trellis-coded VQ (TCVQ) circumvents this problem by using VQ in combination with a trellis encoding strategy, thus treating very large blocks of data at once. TCVQ is based on a size-N VQ codebook which is recursively partitioned to form two (possibly nearly optimal) codebooks of size N/2, four of size N/4, etc. Here we show that such a tree of codebooks can be easily designed, without the need of any postprocessing, by means of the Kohonen algorithm. Numerical experiments show the effectiveness of the proposed approach.
  IEEE Signal Processing Letters 01/2003; · 1.39 Impact Factor
• Conference Proceeding: Advances in the segmentation and compression of multispectral images

  C. D'Elia, G. Poggi, G. Scarpa
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  ABSTRACT: Presents a new low-complexity technique for the segmentation of multispectral images, based on the use of a tree-structured Markov random field model. The image is associated with a binary tree, and is segmented recursively through a sequence of local splits based on a maximum a posteriori probability rule. To improve the reliability of the process, merging of nodes is now considered besides splitting, so as to allow for the re-shaping of incorrect region boundaries. Experimental results show that the new algorithm increases the fitness of the segmentation to the actual features of the image
  Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International; 02/2001
• Article: Compression of SAR raw data through range focusing and variable-rate trellis-coded quantization.

  C D'Elia, G Poggi, L Verdoliva
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  ABSTRACT: There is an ever-growing interest in the compression of SAR data because of the huge resources required for storage and transmission. This is especially true for spaceborne sensors, given the limited capacity of the downlink channel. Unfortunately, SAR data lack the useful properties on which compression algorithms rely; indeed, these are present in the focused images, but focusing is too complex for on-board implementation at this time. Poggi et al. (2000) proposed to perform on the satellite only the low-complexity range focusing, which increases the data correlation and better concentrates their energy. These properties were then exploited by adopting a variable-rate vector quantizer, with a clear performance improvement with respect to reference techniques. However, vector quantization (VQ) is too complex for actual on-board implementation, and therefore, here we replace VQ with trellis-coded VQ. To limit complexity, only small vectors are used, which reduces VQ's ability to exploit data dependencies; on the other hand, trellis coding allows one to encode large blocks of data at once, and to obtain a better partition of the input space. Experiments on real SAR data show that the overall performance is comparable to that of Poggi et al., but the complexity is much lower, making on-board implementation possible.
  IEEE Transactions on Image Processing 02/2001; 10(9):1278-87. · 3.04 Impact Factor
• Conference Proceeding: Compression of SAR raw data via range focusing and trellis coded quantization

  C. D'Elia, G. Poggi, L. Verdoliva
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  ABSTRACT: Recently, there has been a growing interest in the compression of SAR data. Unfortunately, raw data do not exhibit the statistical properties that allow for an efficient compression, and that are present, instead, in the focused data. On the other hand, focusing is too complex to be implemented on board. The authors propose to carry out only the low-complexity range focusing on the satellite, so as to restore part of the correlation and better concentrate the energy in a few samples. To exploit these properties, while limiting the computational complexity, they resort to a variable-rate encoding scheme based on trellis-coded vector quantization. Experiments on real SAR data show a satisfactory performance both in terms of rate-distortion results and of complexity
  Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International; 02/2000