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Editorial Mathematical Methods and Applications in Medical ...
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Editorial Mathematical Methods and Applications in Medical Imaging 2014 Liang Li, 1,2 Tianye Niu, 3 and Yi Gao 4 1 Department of Engineering Physics, Tsinghua University, Beijing 100084, China 2 Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing 100084, China 3 Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang 310000, China 4 Departments of Biomedical Informatics, Computer Science, and Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794-8322, USA Correspondence should be addressed to Liang Li; [email protected] Received 21 April 2015; Accepted 21 April 2015 Copyright © 2015 Liang Li et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Medical imaging applies different techniques to acquire human images for clinical purposes, including diagnosis, monitoring, and treatment guidance. As a typical multidis- ciplinary field, medical imaging requires the improvements in both science and engineering to implement and maintain its noninvasive feature. Computational and mathematical methods are involved with imaging theories, models, recon- struction algorithms, image processing, quantitative imag- ing techniques, acceleration techniques, and multimodal imaging techniques. e main purpose of this issue is to bridge the gap between mathematical methods and their applications in medical imaging. is special issue covers most of the common medical imaging modalities, such as CT, PET, SPECT, MRI, ultrasound, phase contrast, and various image processing methods, such as segmentation, registration, fusion, identification, and denoising. is special issue has 21 papers which were reviewed by at least two reviewers. Seven papers are involved with medical image reconstruction. Few-view CT scanning is a promising low-dose CT imaging mode in applications. B. Yan et al. pro- pose an iterative few-view CT image reconstruction based on nonuniform fast Fourier transform and alternating direction total variation minimization. Different from Yan’s algorithm, H. Qi et al. propose an adaptive TpV regularization algorithm which uses variable value instead of the traditional constant p-norm of the image gradient magnitude. M. Brambilla et al. assess the robustness and reliability of an adaptive threshold- ing algorithm for the Biological Target Volume estimation incorporating PET reconstruction parameters. M. Chang et al. study the automatic exposure control strategies and propose a few-view prereconstruction guided tube current modulation method by keeping the SNR of the sinogram proximately invariable. J. Jang et al. propose a reconstruction method to quantify the distribution of blood flow velocity fields, a potentially useful index of cardiac dysfunction, inside the leſt ventricle from color flow ultrasound images. X-ray grating interferometry offers more information compared with traditional X-ray attenuation imaging especially for the study of weakly absorbing samples. X. Jiang et al. propose a low-dose differential phase reconstruction algorithm which adopts a differential algebraic reconstruction technique with the explicit filtering based sparse regularization rather than the common total variation. B. Wang and L. Li write a review paper on recent developments of dual-dictionary learning method in medical image analysis and reconstruction, which also discusses its role in the future studies and potential applications in medical imaging. As a typical and important application, there are 13 papers involved with various medical image processing methods including segmentation, registration, fusion, denoising, and detection. F. Akram et al. present a region based image segmentation algorithm using active contours with signed Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine Volume 2015, Article ID 685036, 2 pages http://dx.doi.org/10.1155/2015/685036
Transcript of Editorial Mathematical Methods and Applications in Medical ...
EditorialMathematical Methods and Applications inMedical Imaging 2014
Liang Li,1,2 Tianye Niu,3 and Yi Gao4
1Department of Engineering Physics, Tsinghua University, Beijing 100084, China2Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing 100084, China3Sir Run Run Shaw Hospital and Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang 310000, China4Departments of Biomedical Informatics, Computer Science, and Applied Mathematics and Statistics,Stony Brook University, Stony Brook, NY 11794-8322, USA
Correspondence should be addressed to Liang Li; [email protected]
Received 21 April 2015; Accepted 21 April 2015
Copyright © 2015 Liang Li et al. This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Medical imaging applies different techniques to acquirehuman images for clinical purposes, including diagnosis,monitoring, and treatment guidance. As a typical multidis-ciplinary field, medical imaging requires the improvementsin both science and engineering to implement and maintainits noninvasive feature. Computational and mathematicalmethods are involved with imaging theories, models, recon-struction algorithms, image processing, quantitative imag-ing techniques, acceleration techniques, and multimodalimaging techniques. The main purpose of this issue is tobridge the gap between mathematical methods and theirapplications in medical imaging. This special issue coversmost of the common medical imaging modalities, suchas CT, PET, SPECT, MRI, ultrasound, phase contrast, andvarious image processing methods, such as segmentation,registration, fusion, identification, and denoising.
This special issue has 21 papers which were reviewed by atleast two reviewers. Seven papers are involved with medicalimage reconstruction. Few-view CT scanning is a promisinglow-dose CT imaging mode in applications. B. Yan et al. pro-pose an iterative few-view CT image reconstruction based onnonuniform fast Fourier transform and alternating directiontotal variation minimization. Different from Yan’s algorithm,H.Qi et al. propose an adaptive TpV regularization algorithmwhich uses variable𝑝 value instead of the traditional constantp-norm of the image gradient magnitude. M. Brambilla et al.
assess the robustness and reliability of an adaptive threshold-ing algorithm for the Biological Target Volume estimationincorporating PET reconstruction parameters. M. Changet al. study the automatic exposure control strategies andpropose a few-view prereconstruction guided tube currentmodulation method by keeping the SNR of the sinogramproximately invariable. J. Jang et al. propose a reconstructionmethod to quantify the distribution of blood flow velocityfields, a potentially useful index of cardiac dysfunction, insidethe left ventricle from color flow ultrasound images. X-raygrating interferometry offers more information comparedwith traditional X-ray attenuation imaging especially for thestudy of weakly absorbing samples. X. Jiang et al. propose alow-dose differential phase reconstruction algorithm whichadopts a differential algebraic reconstruction technique withthe explicit filtering based sparse regularization rather thanthe common total variation. B. Wang and L. Li write a reviewpaper on recent developments of dual-dictionary learningmethod in medical image analysis and reconstruction, whichalso discusses its role in the future studies and potentialapplications in medical imaging.
As a typical and important application, there are 13 papersinvolved with various medical image processing methodsincluding segmentation, registration, fusion, denoising, anddetection. F. Akram et al. present a region based imagesegmentation algorithm using active contours with signed
Hindawi Publishing CorporationComputational and Mathematical Methods in MedicineVolume 2015, Article ID 685036, 2 pageshttp://dx.doi.org/10.1155/2015/685036
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pressure force function. It has the potential to contempora-neously trace high intensity or dense regions in an imageby evolving the contour inwards. Local feature calculationis important for delineation of hippocampus, a well-knownbiomarker for Alzheimer disease and other neurologicaland psychiatric diseases. S. Tangaro et al. compare fourdifferent techniques for feature selection from a set of315 features extracted for each voxel. The authors obtaincomparable state-of-the-art performances by using only 23features for each voxel. M. Vlachos and E. Dermatas presenta finger vein segmentation method from infrared imagesbased on a modified separable Mumford-Shah model andlocal entropy thresholding method. In order to improvethe performance of the registration in presence of tumorshrinkage between planning CT images and posttreatmentCT images, J. Wang et al. propose a registration methodby combining an image modification procedure and a fastsymmetric Demons algorithm. L. Zhao and K. Jia proposea diffeomorphic image registration algorithm for capturinglarge and complex deformation by using a two-layer deepadaptive registration framework. S. Mazaheri et al. presentan ultrasound image fusion method which weights theimage information within the overlapping regions by usinga combination of principal component analysis and discretewavelet transform. It is expected to increase the segment-ability of echocardiography features and decrease impact ofnoise and artifacts. For the purpose of quantitative analysisof the dynamic behavior about membrane-bound secretoryvesicles, J. Wu et al. present a method to automaticallyidentify the fusion events between VAMP2-pHluorin labeledGLUT4 storage vesicles and the plasma membrane in TIRFmicroscopy image sequences. J. Zhang et al. present amethodautomatically detecting the hinge point of mitral annulus inechocardiography by combining local context feature withadditive support vector machines classifier. R. Xiao et al.present a seed point detectionmethod by using adaptive ridgepoint extraction for coronary artery segmentation in X-rayangiogram image. L. Liu et al. present an adhesion pulmonarynodule detection method for 2D lung CT images based onthe dot-filter and centerline extracting algorithm. R. Takaloet al. present an improved autoregressive model to reducenoise in SPECT images.This AR filter may be applied in bothprojection image and SPECT reconstruction image filtration.Nonlocal means filtering is an effective algorithm to removethe mottled noise by using large-scale similarity informationin low-doseCT.However, it is very time-consuming. L. Zhanget al. present an optimized parallelization method for NLMfiltering by avoiding the repeated computation with row-wiseintensity calculation and the symmetry weight calculation.M. Martin-Fernandez and S. Villullas propose a MRI imagedenoising method by performing a shrinkage of waveletcoefficients based on the conditioned probability of noise.Instead of using an estimator of noise variance, its parametersare calculated by means of the Expectation Maximizationmethod. B. Yu et al. present a method on estimating thebinomial proportions of sensitive or stigmatizing attributesin the population of interest in successive sampling on twooccasions.
We hope that this special issue may represent the state ofthe art and would attract wide attention of the researchers inmedical imaging field.
Acknowledgment
Finally, we are grateful for the tremendous efforts by theauthors and the reviewers.
Liang LiTianye Niu
Yi Gao
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