Download - Editorial Biomedical Signal and Image Processing for ...downloads.hindawi.com/journals/cmmm/2013/578320.pdfKayvanNajarian, 1,2 KevinR.Ward, 2 andShahramShirani 3 Department of Computational

Hindawi Publishing CorporationComputational and Mathematical Methods in MedicineVolume 2013, Article ID 578320, 2 pageshttp://dx.doi.org/10.1155/2013/578320

EditorialBiomedical Signal and Image Processing for Clinical DecisionSupport Systems

Kayvan Najarian,1,2 Kevin R. Ward,2 and Shahram Shirani3

1 Department of Computational Medicine and Bioinformatics, Michigan Center for Integrative Research in Critical Care,University of Michigan, Ann Arbor, USA

2Department of Emergency Medicine, Michigan Center for Integrative Research in Critical Care,University of Michigan, Ann Arbor, USA

3Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada

Correspondence should be addressed to Kayvan Najarian; [email protected]

Received 10 November 2013; Accepted 10 November 2013

Copyright © 2013 Kayvan Najarian 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.

Clinicians and other health care providers are currently beingexpected to make an increasing number of consecutive andcomplex decisions based on a very large amount of complexdata collected from a variety of heterogeneous sources, oftenproduced asynchronously.This is complicated by the additionof a growing number of new diagnostic and monitoringdevices further highlighting the potential for an ever-growingdata stream aswell as the challenge of diagnosing and treatingmultiple patients at one time. It can be argued that thereis much hidden knowledge in various clinical data such asimages, physiologic signals, and others that simply cannot berapidly extracted by the human eye. During the last decade,the need for computational methods, in particular signal andimage processing algorithms, to analyze these complex datasets and provide health care providers with recommendationsand/or predictions has been further highlighted. However,due to the size and complexity of the data produced bymonitoring and imaging systems, the need for more effectivemethods to extract knowledge from these images has notgrown with the same rate. This, of course, will impede thedevelopment of clinical decision support systems.

This special issue of Computational and MathematicalMethods in Medicine serves as a brief update to the cur-rent status of and advances in methods and approachesin biomedical signal and image processing methods usedfor clinical decision support systems. The computationalmethods presented in this special issue cover awide spectrum

of algorithmic approaches applied to a wide range of clinicalapplications.

The paper by X. Li et al. provides a system to iden-tify patients with poststroke mild cognitive impairment bypattern recognition of working memory load-related ERPwhich may improve our assessment of some of the long-termimpacts of stroke. E. Swanly et al. present a methodologyto process CT images in order to spot pulmonary TB in amore effectivemanner. F. Li and F. Porikli give the descriptionof their method that allows tracking of lung tumors inorthogonal X-rays. The paper by N. Saidin et al. presentsa method for computer-aided detection of breast densityfor more accurate detection of breast cancer. This paperalso presents a methodology for visualization of other breastanatomical regions on mammogram. H. Jiang et al. providea hybrid method, based on level-set methods, for extractionof pancreas images from CT scanning with may clinicalapplications where CT is used for detection of potentialdamages to pancreas. The study by M. Jiang et al. focuseson parameter optimization for support vector regression insolving the inverse ECG problem, while H.-T. Wu et al.present the results of their study on quantification of thecomplex fluctuation between R-R intervals series and pho-toplethysmography amplitude series. Both of these methodsmay have wide ranging implications for new physiologicdiagnostic approaches for patients. The paper presented byY.-W. Chen et al. focuses on a computer-aided diagnosis

2 Computational and Mathematical Methods in Medicine

and quantification of cirrhotic livers based on morphologicalanalysis and machine learning. I. Cruz-Aceves et al. presenttheir unsupervised cardiac image segmentation that appliesmultiswarm active contours with a shape prior. Finally, thepaper by H. Jiang et al. provides a liver segmentationmethod,based on snakes model and improved GrowCut algorithmthat is applied to abdominal CT images.

As more advanced imaging and monitoring systemsare designed, it is expected that the algorithms to processthe data produced by these systems need to be evolvedaccordingly. These novel approaches may not only helpextract new knowledge that are not readily possible throughcurrent traditional interpretation but also set the stage forproviding rapid predictive information assisting health careproviders in making better informed decisions. As shown inthe papers presented in this special issue, these changes needto address both size and complexity of the produced data.Theopportunities are rich as are the challenges.

Kayvan NajarianKevin R. Ward

Shahram Shirani

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