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2007 Contribution to book Restricted

A general approach to shape characterization for biomedical problems
Moroni D., Perner P., Salvetti O.
In this paper, we present a general approach to shape characterization and deformation analysis of 2D/3D deformable visual objects. In particular, we define a reference dynamic model, encoding morphological and functional properties of an objects class, capable to analyze different scenarios in heart left ventricle analysis. The proposed approach is suitable for generalization to the analysis of periodically deforming anatomical structures, where it could provide useful support in medical diagnosis. Preliminary results in heart left ventricle analysis are discussed.Source: Advances in Mass Data Analysis of Signals and Images in Medicine, Biotechnology and Chemistry, edited by Petra Perner & Ovidio Salvetti, pp. 136–145, 2007
DOI: 10.1007/978-3-540-76300-0

See at: academic.microsoft.com Restricted | link.springer.com Restricted | link.springer.com Restricted | link.springer.com Restricted | CNR ExploRA Restricted | www.springerlink.com Restricted


2007 Contribution to book Restricted

Automatic fuzzy-neural based segmentation of microscopic cell images
Colantonio S., Gurevich I. B., Salvetti O.
In this paper, we propose a novel, completely automated method for the segmentation of lymphatic cell nuclei represented in microscopic specimen images. Actually, segmenting cell nuclei is the first, necessary step for developing an automated application for the early diagnostics of lymphatic system tumours. The proposed method follows a two-step approach to, firstly, find the nuclei and, then, to refine the segmentation by means of a neural model, able to localize the borders of each nucleus. Experimental results have shown the feasibility of the method.Source: Advances in Mass Data Analysis of Signals and Images in Medicine, Biotechnology and Chemistry, edited by Perner Petra, Salvetti Ovidio, pp. 115–127, 2007
DOI: 10.1007/978-3-540-76300-0

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2007 Conference article Restricted

Blind source separation applied to spectral unmixing: comparing different measures of nongaussianity
Caiafa C. F., Salerno E., Proto A. N.
We report some of our results of a particular blind source separation technique applied to spectral unmixing of remote-sensed hyperspectral images. Different nongaussianity measures are introduced in the learning procedure, and the results are compared to assess their relative efficiencies, with respect to both the output signal-to-interference ratio and the overall computational complexity. This study has been conducted on both simulated and real data sets, and the first results show that skewness is a powerful and unexpensive tool to extract the typical sources that charcterize remote-sensed images.Source: Knowledge-Based Intelligent Information and Engineering Systems. 11th International Conference KES 2007, XVII Italian Workshop on Neural Networks, pp. 1–8, Vietri sul Mare, Italy, 12-14 September 2007
DOI: 10.1007/978-3-540-74829-8

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2007 Journal article Restricted

A closed-form nonparametric Bayesian estimator in the wavelet domain of images using an approximate alpha-stable prior - Comment
Achim A., Kuruoglu E. E., Bezerianos A., Tsakalides P.
The purpose of this commentary is to point out that the paper by Boubchir and Fadili (B-F) [Boubchir, L., Fadili, J.M., 2006. A closedform nonparametric Bayesian estimator in the wavelet domain of images using an approximate alpha-stable prior. Pattern Recognit. Lett. 27, 1370-1382] is little more than a paraphrase of earlier work in [Achim, A., Bezerianos, A., Tsakalides, P., 2001. Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. Med. Imag. 20 (August), 772-783; Kuruoglu, E.E., Molina, C., Fitzgerald, W.J., 1998. Approximation of a-stable probability densities using finite Gaussian mixtures. In: Proc. EUSIPCO'98 (September); Portilla, J., Strela, V., Wainwright, M.J., Simoncelli, E.P., 2003. Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE. Trans. Image. Proc. 12 (November), 1338-1351]. Essentially, B-F purport to propose an algorithm for image denoising based on scale mixtures of Gaussians in the wavelet domain, an approach well documented in [Portilla, J., Strela, V., Wainwright, M.J., Simoncelli, E.P., 2003. Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE. Trans. Image. Proc. 12 (November), 1338-1351]. In achieving this, B-F make use of a known method for approximating alpha-stable distributions previously proposed by Kuruoglu and co-workers [Kuruoglu, E.E., 1998. Signal processing in alpha-stable noise environments:Aleast lp-norm approach. Ph.D. thesis, University of Cambridge, Cambridge; Kuruoglu, E.E., Molina, C., Fitzgerald, W.J., 1998. Approximation of alpha-stable probability densities using finite Gaussian mixtures. In: Proc. USIPCO'98 (September)], but without referring to their work. Together, the above observations do not entitle B-F to claim to have developed a new algorithm. In addition, we show that B-F [2006; Fadili, J.M., Boubchir, L., 2005. Analytical form for a Bayesian wavelet estimator of images using the Bessel K form densities. IEEE Trans. Image Process. 14 (2), 231-240] include unfair comments and comparison vis-a`-vis of the method proposed in our early work [Achim, A., Bezerianos, A., Tsakalides, P., 2001. Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. Med. Imag. (August) 20, 772-783].Source: Pattern recognition letters 28 (2007): 1845–1847.

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2007 Conference article Restricted

Extracting astrophysical sources from channel-dependent convolutional mixtures by correlated component analysis in the frequency domain
Bedini L., Salerno E.
A second-order statistical technique (FD-CCA) for semi-blind source separation from multiple-sensor data is presented. It works in the Fourier domain and allows us to both learn the unknown mixing operator and estimate the source cross-spectra before applying the proper source separation step. If applied to small sky patches, our algorithm can be used to extract diffuse astrophysical sources from the mixed maps obtained by radioastronomical surveys, even though their resolution depends on the measurement channel. Unlike the independent component analysis approach, FD-CCA does not need mutual independence between sources, but exploits their spatial autocorrelations. We describe our algorithm, derived from a previous pixel-domain strategy, and present some results from simulated data.Source: Knowledge-Based Intelligent Information and Engineering Systems. 11th International Conference KES 2007, XVII Italian Workshop on Neural Networks, pp. 9–16, Vietri sul Mare, Italy, 12-14 September 2007
DOI: 10.1007/978-3-540-74829-8

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2007 Journal article Restricted

Image separation using particle filters
Costagli M., Kuruoglu E. E.
In this work, we will analyze the problem of source separation in the case of superpositions of different source images, which need to be extracted from a set of noisy observations. This problem occurs, for example, in the field of astrophysics, where the contributions of various Galactic and extra-Galactic components need to be separated from a set of observed noisy mixtures. Most of the previous work on the problem performed blind source separation, assuming noiseless models, and in the few cases when noise is taken into account, it is assumed that it is Gaussian and space-invariant. In this paper we review the theoretical fundamentals of particle filtering, an advanced Bayesian estimation method which can deal with non-Gaussian non-linear models and additive space-varying noise, and we introduce a hierarchical model and a fusion of multiple particle filters for the solution of the image separation problem. Our simulations on realistic astrophysical data show that the particle filter approach provides significantly better results in comparison with one of the most widespread algorithms for source separation (FastICA), especially in the case of low SNR.Source: Digital signal processing (Print) 17 (2007): 935–946. doi:10.1016/j.dsp.2007.04.003
DOI: 10.1016/j.dsp.2007.04.003

See at: Digital Signal Processing Restricted | Digital Signal Processing Restricted | Digital Signal Processing Restricted | Digital Signal Processing Restricted | Digital Signal Processing Restricted | CNR ExploRA Restricted | Digital Signal Processing Restricted


2007 Journal article Restricted

Long correlation Gaussian random fields: parameter estimation and noise reduction
Caiafa C., Proto A., Kuruoglu E. E.
In this paper, a parametric model for Gaussian random fields (GRFs) with long-correlation feature, namely the long correlation GRF (LC-GRF), is studied. Important properties of the model are derived and used for developing new parameter estimation algorithms and for constructing an optimum noise reduction filter. In particular, a novel iterative maximum likelihood estimation (MLE) algorithm is proposed for estimating the parameters of the model from a sample image, and the expectation-maximization (EM) algorithm is proposed for estimating the signal and noise variances given a noisy image. The optimal Wiener filter is derived making use of the parametric form of the model for the noise reduction under additive white Gaussian noise (WGN). Also the theoretic performance of the filter is obtained and its behavior is analyzed in terms of the long-correlation feature of the model. The effectiveness of the presented algorithms is demonstrated through experimental results on synthetic generated GRFs. An application to the restoration of cosmic microwave background (CMB) images in the presence of additive WGN is also presented.Source: Digital signal processing (Print) 17 (2007): 819–835. doi:10.1016/j.dsp.2007.01.001
DOI: 10.1016/j.dsp.2007.01.001

See at: Digital Signal Processing Restricted | Digital Signal Processing Restricted | Digital Signal Processing Restricted | Digital Signal Processing Restricted | CNR ExploRA Restricted | Digital Signal Processing Restricted


2007 Journal article Restricted

Object tracking in a stereo and infrared vision system
Colantonio S., Benvenuti M., Di Bono M. G., Pieri G., Salvetti O.
In this paper, we deal with the problem of real-time detection, recognition and tracking of moving objects in open and unknown environments using an infrared (IR) and visible vision system. A thermo-camera and two stereo visible-cameras synchronized are used to acquire multi-source information: three-dimensional data about target geometry and its thermal information are combined to improve the robustness of the tracking procedure. Firstly, target detection is performed by extracting its characteristic features from the images and then by storing the computed parameters on a specific database; secondly, the tracking task is carried on using two different computational approaches. A Hierarchical Artificial Neural Network (HANN) is used during active tracking for the recognition of the actual target, while, when partial occlusions or masking occur, a database retrieval method is used to support the search of the correct target followed. A prototype has been tested on case studies regarding the identification and tracking of animals moving at night in an open environment, and the surveillance of known scenes for unauthorized access control.Source: Infrared physics & technology 49 (2007): 266–271. doi:10.1016/j.infrared.2006.06.028
DOI: 10.1016/j.infrared.2006.06.028

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2007 Journal article Restricted

Real-time measurement and analysis of translational and rotational speeds of moving objects in microscope fields
Coltelli P., Evangelisti M., Evangelista V., Gualtieri P.
This paper describes a digital system designed for the automatic detection and measurement of the velocity of moving objects in images acquired by means of a common TV-camera mounted onto a microscope. The main characteristics of this system are the following: 1) it can perform a realtime gray level difference between two successive frames in order to detect moving objects and to suppress stationary objects (subtraction procedure); usually the delay between two successive frames varies linearly from 40 msec to 1920 msec; 2) it reduces the size of images resulting from the subtraction procedure (difference images) and stores them in the frame memory; the result of these operation, all performed in real-time, is a film of time sequences; 3) it performs an automatic labelization in order to recognize the moving microorganisms and to calculate their area in each difference image; 4) it calculates and plots the variation of the average area of the cells moving in the microscope field; 5) it completes the analysis in few seconds.Source: Lecture notes in computer science 4826 (2007): 128–135.

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2007 Contribution to book Restricted

Semi-automatic semantic tagging of 3D images from pancreas cells
Little S., Salvetti O., Perner P.
Detailed, consistent semantic annotation of large collections of multimedia data is difficult and time-consuming. In domains such as eScience, digital curation and industrial monitoring, fine-grained high-quality labeling of regions enables advanced semantic querying, analysis and aggregation and supports collaborative research. Manual annotation is inefficient and too subjective to be a viable solution. Automatic solutions are often highly domain or application specific, require large volumes of annotated training corpi and, if using a 'black box' approach, add little to the overall scientific knowledge. This article evaluates the use of simple artificial neural networks to semantically annotate micrographs and discusses the generic process chain necessary for semi-automatic semantic annotation of images.Source: MDA 2006/2007, edited by Petra Perner and Ovidio Salvetti, pp. 69–79, 2007

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2007 Contribution to book Restricted

Statistical analysis of electrophoresis time series for improving basecalling in DNA sequencing
Tonazzini A., Bedini L.
In automated DNA sequencing, the final algorithmic phase, referred to as basecalling, consists of the translation of four time signals in the form of peak sequences (electropherogram) to the corresponding sequence of bases. Commercial basecallers detect the peaks based on heuristics, and are very efficient when the peaks are distinct and regular in spread, amplitude and spacing. Unfortunately, in the practice the signals are subject to several degradations, among which peak superposition and peak merging are the most frequent. In these cases the experiment must be repeated and human intervention is required. Recently, there have been attempts to provide methodological foundations to the problem and to use statistical models for solving it. In this paper, we exploit a priori information and Bayesian estimation to remove degradations and recover the signals in an impulsive form which makes basecalling straightforward.Source: Advances in Mass Data Analysis of Signals and Images in Medicine, Biotechnology and Chemistry, edited by Petra Perner, Ovidio Salvetti, pp. 146–155, 2007
DOI: 10.1007/978-3-540-76300-0

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2007 Contribution to book Restricted

Statistical analysis of microspectroscopy signals for algae classification and phylogenetic comparison
Tonazzini A., Coltelli P., Gualtieri P.
We performed microspectroscopic evaluation of the pigment composition of the photosynthetic compartments of algae belonging to different taxonomic divisions and higher plants. In cite{Bar07}, a supervised Gaussian bands decompositions was performed for the pigment spectra, the algae spectrum was modelled as the linear mixture, with unknown coefficients, of the pigment spectra, and a user-guided fitting algorithm was employed. The method provided a reliable discrimination among chlorophylls $a$, $b$ and $c$, phycobiliproteins and carotenoids. Comparative analysis of absorption spectra highlighted the evolutionary grouping of the algae into three main lineages in accordance with the most recent endosymbiotic theories. In this paper, we adopt an unsupervised statistical estimation approach to automatically perform both Gaussian bands decomposition of the pigments and algae fitting. In a fully Bayesian setting, we propose estimating both the algae mixture coefficients and the parameters of the pigment spectra decomposition, on the basis of the alga spectrum alone. As a priori information to stabilize this highly underdetermined problem, templates for the pigment spectra are assumed to be available, though, due to their measurements outside the protein moiety, they differ in shape from the real spectra of the pigments present in nature by unknown, slight displacements and contraction/dilatation factors. We propose a classification system subdivided into two phases. In the first, the learning phase, the parameters of the Gaussians decomposition and the shape factors are estimated. In the second phase, the classification phase, the now known real spectra of the pigments are used as a base set to fit any other spectrum of algae. The unsupervised method provided results comparable to those of the previous, supervised method.Source: Advances in Mass Data Analysis of Signals and Images in Medicine, Biotechnology and Chemistry, edited by Petra Perner and Ovidio Salvetti, pp. 58–68, 2007
DOI: 10.1007/978-3-540-76300-0

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2007 Conference article Restricted

Thesaurus-based ontology on image analysis
Colantonio S., Gurevich I. B., Martinelli M., Salvetti O., Trusova Y.
The paper is devoted to the development of an ontology of the domain "Image processing, analysis, recognition, and understanding" based on the existing image analysis thesaurus. Such an ontology could be used to support a wide range of tasks, including automated image analysis, algorithmic knowledge reuse, intelligent information retrieval, etc. Main steps and first results of the ontology development process are described.Source: Semantic Multimedia. Second International Conference on Semantic and Digital Media Technologies, SAMT 2007, pp. 113–116, Genova, Italy, 5-7 December 2007
DOI: 10.1007/978-3-540-77051-0
DOI: 10.1007/978-3-642-23017-2

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2007 Contribution to journal Restricted

Preface - Advances in mass data analysis of signals and images in medicine, biotechnology and chemistry
Perner P., Salvetti O.
The automatic analysis of images and signals in medicine, biotechnology, and chemistry is a challenging and demanding field. Signal-producing procedures by microscopes, spectrometers, and other sensors have found their way into wide fields of medicine, biotechnology, economy, and environmental analysis. With this arises the problem of the automatic mass analysis of signal information. Signal-interpreting systems which generate automatically the desired target statements from the signals are therefore of compelling necessity. The continuation of mass analyses on the basis of classical procedures leads to investments of proportions that are not feasible. New procedures and system architectures are therefore required. The scope of the International Conference on Mass Data Analysis of Images and Signals in Medicine, Biotechnology and Chemistry MDA (www.mda-signals.de) is to bring together researchers, practitioners, and industry people who are dealing with mass analysis of images and signals to present and discuss recent research in these fields.

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2007 Contribution to journal Restricted

Editorial activity - Special Issue on Bayesian Source Separation
Kuruoglu E., Knuth K.
The signal processing problem known as source separation has rapidly grown in the last decade from being viewed as something of a curiosity to becoming a fundamental signal processing problem that is ubiquitous across scientific disciplines. Source separation problems are characterized by a set of sources that either emit or modulate signals that propagate to one or several detectors. The signal processing goal is to "separate" the recorded signals into the set of "source" signals. This basic situation appears in a wide variety of contexts ranging from the more traditional mixing of sound signals as in the Cocktail Party Problem to mixtures of source signals of spatial extent in images, such as in astrophysical applications where the objects of study are optically thin (transparent) or magnetic resonance imaging where the effects of several distinct processes are superimposed. At this point in time, source separation has been widely studied, resulting in an extensive array of source separation algorithms. Much of the effort has gone into developing what are known as blind source separation algorithms, referring to the fact that these algorithms are provided with a minimum amount of information about the nature of the recorded signals. These blind algorithms are extremely useful, as they are specifically designed to be generally applicable to a wide array of problems. Many of these techniques work well in a wide variety of situations, including those where noise is an issue.

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2007 Journal article Restricted

The TAU2 polyphonic music terminal. The project, its realisation and role in computer music
Bertini G.
The development of computer music carrying out at CNUCE and IEI, both CNR Institutes, in collaboration with L. Cherubini Conservatory of Music in Florence by a M° Pietro Grossi initiatives, are described. The ambitious goal was the construction of an audio terminal to interface like any peripheral device to the mainframe IBM 360/67 of the CNUCE, to allow the electronic production of sounds and the execution of polyphonic and polytimbric music in real time using the program TAUMUS. In the first part of the paper something about the experiments developed to use the new "electronic instrumentation"in music are introduced, citing also the state of the art and the problems encountered using the systems at the time. In the following, detailed solution adopted for TAU2 design and realization are described, in order to generate sounds by additive synthesis, avoiding performance in differed time (off-line). The definition and choice of how to use the system, the production of the pieces, interactions with the musicians, teaching and demonstrations with TAU2-TAUMUS, have combined to create an original musical environment that contributed notably to the development of musical informatics in Italy.Source: Musica tecnologia (Testo stamp.) 1 (2007): 339–366.

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2007 Journal article Restricted

Digital image analysis to enhance underwritten text in the Archimedes palimpsest
Salerno E., Tonazzini A., Bedini L.
This paper reports some of the results obtained by applying statistical processing techniques to multispectral images of the Archimedes palimpsest. We focused on the possibilities of extracting the faint and highly degraded underwritten text,which constitutes the most ancient source for several treatises by Archimedes. Assuming each image to be generated by a linear mixture of different patterns, characterized by different emissivity spectra, the specific difficulty in separating the underwriting is that the mixture coefficients are unknown. To solve this problem, we rely on statistical techniques that maximize the information content of the processed images. In particular, we assessed the performances of the principal component analysis (PCA) and the independent component analysis (ICA) techniques. On the basis of 14 hyperspectral views of part of the palimpsest, we succeeded to extract clean maps of the primary Archimedes text, the overwritten text, and the mold pattern present in the pages. This goal was not reached in all the cases, because of the nonperfect adherence of the data model to reality. In most cases, however, PCA and ICA produced a significant enhancement of the underwritten text.Source: International journal on document analysis and recognition (Print) 9 (2007): 79–87. doi:10.1007/s10032-006-0028-7
DOI: 10.1007/s10032-006-0028-7

See at: International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | CNR ExploRA Restricted


2007 Journal article Restricted

Fast correction of bleed-through distortion in grayscale documents by a blind source separation technique
Tonazzini A., Salerno E., Bedini L.
Ancient documents are usually degraded by the presence of strong background artifacts. These are often caused by the so-called bleed-through effect, a pattern that interferes with the main text due to seeping of ink from the reverse side. A similar effect, called show-through and due to the nonperfect opacity of the paper, may appear in scans of even modern, well-preserved documents. These degradations must be removed to improve human or automatic readability. For this purpose, when a color scan of the document is available, we have shown that a simplified linear pattern overlapping model allows us to use very fast blind source separation techniques. This approach, however, cannot be applied to grayscale scans. This is a serious limitation, since many collections in our libraries and archives are now only available as grayscale scans or microfilms. We propose here a new model for bleed-through in grayscale document images, based on the availability of the recto and verso pages, and show that blind source separation can be successfully applied in this case too. Some experiments with real-ancient documents are presented and described.Source: International journal on document analysis and recognition (Internet) 10 (2007): 17–25. doi:10.1007/s10032-006-0015-z
DOI: 10.1007/s10032-006-0015-z

See at: International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | International Journal on Document Analysis and Recognition (IJDAR) Restricted | CNR ExploRA Restricted


2007 Journal article Restricted

A new measure on intuitionistic fuzzy set using Hausdorff metric and its application to edge detection
Tamalika C., Swades D., Salvetti O.
Intuitionistic fuzzy set (IFS) proposed by Attanassov has gained much importance to the researchers for its application in various fields such as pattern recognition. It takes into account the membership, non-membership function also another term hesitation degree. Hesitation degree is the lack of knowledge in assigning the membership function. In particular, the similarity measure between IFSs has increased its interest and several algorithms have been developed. In this paper a new method for measuring the distance between two intuitionistic fuzzy sets based on Hausdorff metric is proposed. The distance measure is the intuitionistic fuzzy divergence using Hausdorff metric. Our proposed method has been applied in image processing in detecting the edges of different kinds of practical images, demonstrating to be a tool for processing monochrome images.Source: International journal of fuzzy mathematics and systems 15 (2007): 1–16.

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2007 Journal article Restricted

A two-step approach for automatic microscopic image segmentation using fuzzy clustering and neural discrimination
Colantonio S., Gurevich I. B., Salvetti O.
The early diagnosis of lymphatic system tumors heavily relies on the computerized morphological analysis of blood cells in microscopic specimen images. Automating this analysis necessarily requires an accurate segmentation of the cells themselves. In this paper, we propose a robust method for the automatic segmentation of microscopic images. Cell segmentation is achieved following a coarse-to-fine approach, which primarily consists in the rough identification of the blood cell and, then, in the refinement of the nucleus contours by means of a neural model. The method proposed has been applied to different case studies, revealing its actual feasibility.Source: Pattern recognition and image analysis 17 (2007): 428–437. doi:10.1134/S1054661807030108
DOI: 10.1134/s1054661807030108

See at: Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | Pattern Recognition and Image Analysis Restricted | CNR ExploRA Restricted