editorial advanced signal processing methods for...

Hindawi Publishing CorporationInternational Journal of Biomedical ImagingVolume 2013, Article ID 696878, 2 pageshttp://dx.doi.org/10.1155/2013/696878

EditorialAdvanced Signal Processing Methods for Biomedical Imaging

Juan Ruiz-Alzola,1, 2 Carlos Alberola-Lopez,3 and Carl-Fredrik Westin4

1 Department of Signals and Communications, University of Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain2 Canary Islands Agency for Reseach, Innovation and Information Society, 35003 Las Palmas de Gran Canaria, Spain3 Image Processing Laboratory, ETSI Telecomunicacion, University of Valladolid, 47011 Valladolid, Spain4 Laboratory for Mathematics in Imaging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA

Correspondence should be addressed to Juan Ruiz-Alzola; [email protected]

Received 23 December 2012; Accepted 23 December 2012

Copyright © 2013 Juan Ruiz-Alzola et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Signal processing is a long-established engineering field withbroad applications in so different arenas such as multime-dia (audio, still image, video, graphics, etc.), coding, com-munications, seismology, astronomy, biomedicine, artificialintelligence, and econometrics, to only name some of them.Signal processing stems from cross-fertilized grounds includ-ing mathematical analysis, algebra, numerical analysis, prob-ability, statistics, information theory, discrete mathematics,cybernetics, and computer science. Advances in signal pro-cessing have frequently been pulled by needs in specificapplication domains, so that they are readily incorporated tothe signal processing knowledge base for convenient use indistant areas of application.

Signal and image processing is ubiquitous in modernbiomedical imaging, as it provides essential techniques forimage construction, enhancement, coding, storage, trans-mission, analysis, understanding, and visualization from anyof an increasing number of different multidimensional sens-ing modalities. As biomedical imaging is rapidly evolving,new and more powerful signal and image processing algo-rithms are required to meet the challenges imposed bymodern hugemultidimensionalmultimodal biomedical data,particularly in real clinical settings.

In this special issue, we present high-quality originalresearch papers that address specific challenges for thebiomedical imaging community and benefit of advancedand emerging methods in signal and image processingrequired for novel time-varying high-dimensional structuraland functional biomedical imaging modalities. Interestingnew ideas from other fields of application of signal andimage processing have been highly welcome since their

practical relevance for biomedical imaging is clearly moti-vated.

In particular, this special issue features original researchfor different imagingmodalities, such as generalized diffusiontensor imaging (GDTI), conventional diffusion weightedMRI (DW-MRI), dynamic contrast enhanced MRI (DCE-MRI), functional MRI (fMRI), X-ray mammography andtime-frequency representations (TFR) of electrophysiologicaldata such as electroencephalography (EEG), magnetoen-cephalography (MEG), or local field potentials (LFP). Themedical application areas include central nervous systemfiber tractography, analysis of episodic memory and motorbrain activity, analysis of neural activity patterns, and breastcancer screening and detection. As for the signal and imageprocessing methods, a brief list of topics addressed in thisspecial issue include higher-order tensors, signal estimationand classification, clustering, and multivariate statistics, toname a few.

In “Fast and analytical EAP approximation from a 4th-order tensor,” A. Ghosha and R. Deriche deal with thecomplicated problem of inferring themicrostructure of tissueor fiber bundles from DW-MRI. To this extent they proposea modified GDTI approach, where the higher-Order tensor(HOT) representation of the apparent diffusivity coefficient(ADC) is estimated, from the DW-MRI data, in such away that they provide a closed-form approximation, usingHermite polynomials, to the ensemble average propagator(EAP) that overcomes the computational overload of otherEAP estimation methods from GDTI.The EAP describes theprobability of the diffusing particles, and, hence, the geome-try of the EAP is a direct indicator of themicrostructure of the

2 International Journal of Biomedical Imaging

underlying tissue or fiber bundles.They provide experimentalresults on the fiber bundles estimated from real and syntheticbrain datasets.

In “DCE-MRI and DWI integration for breast lesionsassessment and heterogeneity quantification,” C. A. Mendez etal. address the quantification of breast tumor heterogeneityusing jointly DW-MRI and DCE-MRI, which provide ameasure of cellularity and an indication of blood volume,flow, and vascular permeability, respectively. To that end, theyfirst make an affine followed by an elastic registration of bothdatasets and approach the tissue segmentation by clusteringusing a dissimilarity-based representation (DBR) of the DCEcurves and the ADC maps, followed by K-means. Statisticaltesting is carried out for the ADC maps of the resultingclusters. They provide experimental results from 21 patientswith primary ductal carcinoma.

In “The smoothing artifact of spatially constrained canon-ical correlation analysis in functional MRI,” D. Cordes etal. address the problem of the spatial specificity of theactivation signal in fMRI due to smoothing artifacts thatarise in an extension of the canonical correlation analysis(CCA) termed constrained CCA (cCCA), which is used asa statistic in fMRI to test for functional brain activation in aspecific neighborhood. In particular, they investigate in detailthe smoothing artifact that is associated with each spatialconstraint in cCCA and provide a novel approach in order tocorrect the measure of activation for the smoothing artifactwithin a Bayesian testing framework. They also provideexperimental results on six real cases with six normal subjectscarrying out certain motor and episodic memory tasks.

In “A new GLLD operator for mass detection in digitalmammograms,” N. Gargouri et al. propose an extension tothe local binary pattern (LBP) operator used for textureclassification in order to overcome some limitations that arisefrom the fact that LBP uses local gray-level differences. Inparticular they introduce the gray level and local Difference(GLLD) representation that, in addition to differences, alsouses gray levels and investigate the efficiency of the GLLD-based approach as amethod of feature extraction.They tacklethe detection of abnormal masses on breast X-ray images byperforming a manual region of interest (ROI) delineation,followed by the GLLD representation and standard classi-fication by k-NN, support vector machines, and multilayerperceptrons. Experimental results are from 1000 ROIs fromthe Digital Database for Screening Mammography.

Last but not least, in “A flexible model for feature extrac-tion from brain signals in the time-frequency domain,” R. Hei-deklang and G. Ivanova provide a different perspective sincethey deal with electrophysiological data. Images arise fromthe time-frequency representation (TFR) of these nonstation-ary signals that are obtained in the paper from the smoothedpseudo-Wigner-Ville distribution. A common problem ofTFRs from electrophysiological data is inter- and intrasubjectvariability of the features to be extracted (curves in theTFR), which is here addressed by proposing the smoothnatural Gaussian extension (snaGe) model, characterized bya sequence of multivariate Gaussians located on a parametriccurve described by a cubic B-spline. By combining cubic B-splines with the Gaussian shape, they obtain a “sufficiently

smooth model which inherits both the splines’ flexibility andthe Gaussian standard model’s robustness.” In order to fit themodel to the data, after subsampling, an initial estimation ofthe initial parameters of the curve is obtained by dynamicprogramming, which is refined through iteration, and usingFrechet and other metrics for curve difference. The authorspresent results on real and synthetic EEG data, and theydiscuss the robustness and the sensitivity of the method tonoise.

We hope that the readers enjoy this special issue.

Juan Ruiz-AlzolaCarlos Alberola-LopezCarl-Fredrik Westin

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