http://www.hindawi.comThe latest articles from Hindawi Publishing Corporation© 2012, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/journals/ijbi/2012/864827/Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 40962 or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 10242 and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images.David S. Smith, John C. Gore, Thomas E. Yankeelov, and E. Brian WelchCopyright © 2012 David S. Smith et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2012/531319/The registration of intraoperative ultrasound (US) images with preoperative magnetic resonance (MR) images is a challenging problem due to the difference of information contained in each image modality. To overcome this difficulty, we introduce a new probabilistic function based on the matching of cerebral hyperechogenic structures. In brain imaging, these structures are the liquid interfaces such as the cerebral falx and the sulci, and the lesions when the corresponding tissue is hyperechogenic. The registration procedure is achieved by maximizing the joint probability for a voxel to be included in hyperechogenic structures in both modalities. Experiments were carried out on real datasets acquired during neurosurgical procedures. The proposed validation framework is based on (i) visual assessment, (ii) manual expert estimations , and (iii) a robustness study. Results show that the proposed method (i) is visually efficient, (ii) produces no statistically different registration accuracy compared to manual-based expert registration, and (iii) converges robustly. Finally, the computation time required by our method is compatible with intraoperative use.Pierrick Coupé, Pierre Hellier, Xavier Morandi, and Christian BarillotCopyright © 2012 Pierrick Coupé et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/632195/An algorithm was developed to segment solid pulmonary nodules attached to the chest wall in computed tomography scans. The pleural surface was estimated and used to segment the nodule from the chest wall. To estimate the surface, a robust approach was used to identify points that lie on the pleural surface but not on the nodule. A 3D surface was estimated from the identified surface points. The segmentation performance of the algorithm was evaluated on a database of 150 solid juxtapleural pulmonary nodules. Segmented images were rated on a scale of 1 to 4 based on visual inspection, with 3 and 4 considered acceptable. This algorithm offers a large improvement in the success rate of juxtapleural nodule segmentation, successfully segmenting 98.0% of nodules compared to 81.3% for a previously published plane-fitting algorithm, which will provide for the development of more robust automated nodule measurement methods.Artit C. Jirapatnakul, Yury D. Mulman, Anthony P. Reeves, David F. Yankelevitz, and Claudia I. HenschkeCopyright © 2011 Artit C. Jirapatnakul et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/467563/Deconvolution-based analysis of CT and MR brain perfusion data is widely used in clinical practice and it is still a topic of ongoing research activities. In this paper, we present a comprehensive derivation and explanation of the underlying physiological model for intravascular tracer systems. We also discuss practical details that are needed to properly implement algorithms for perfusion analysis. Our description of the practical computer implementation is focused on the most frequently employed algebraic deconvolution methods based on the singular value decomposition. In particular, we further discuss the need for regularization in order to obtain physiologically reasonable results. We include an overview of relevant preprocessing steps and provide numerous references to the literature. We cover both CT and MR brain perfusion imaging in this paper because they share many common aspects. The combination of both the theoretical as well as the practical aspects of perfusion analysis explicitly emphasizes the simplifications to the underlying physiological model that are necessary in order to apply it to measured data acquired with current CT and MR scanners.Andreas Fieselmann, Markus Kowarschik, Arundhuti Ganguly, Joachim Hornegger, and Rebecca FahrigCopyright © 2011 Andreas Fieselmann et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/953806/The goal of this study was to provide a feasibility assessment for PET imaging of multiple sclerosis (MS) lesions based on their decreased myelin content relative to the surrounding normal-appearing brain tissue. The imaging agent evaluated for this purpose is a molecule that binds strongly and specifically to myelin basic protein. Physiology-based pharmacokinetic modeling combined with PET image simulation applied to a brain model was used to examine whether such an agent would allow the differentiation of artificial lesions 4–10 mm in diameter from the surrounding normal-looking white and gray matter. Furthermore, we examined how changes in agent properties, model parameters, and experimental conditions can influence imageability, identifying a set of conditions under which imaging of MS lesions might be feasible. Based on our results, we concluded that PET imaging has the potential to become a useful complementary method to MRI for MS diagnosis and therapy monitoring.Maria I. Zavodszky, John F. Graf, and Cristina A. Tan HehirCopyright © 2011 Maria I. Zavodszky et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/817250/The highly disordered refractive index distribution in skin causes multiple scattering of incident light and limits optical imaging and therapeutic depth. We hypothesize that localized mechanical compression reduces scattering by expulsing unbound water from the dermal collagen matrix, increasing protein concentration and decreasing the number of index mismatch interfaces between tissue constituents. A swept-source optical coherence tomography (OCT) system was used to assess changes in thickness and group refractive index in ex vivo porcine skin, as well as changes in signal intensity profile when imaging in vivo human skin. Compression of ex vivo porcine skin resulted in an effective strain of −58.5%, an increase in refractive index from 1.39 to 1.50, and a decrease in water volume fraction from 0.66 to 0.20. In vivo OCT signal intensity increased by 1.5 dB at a depth of 1 mm, possibly due to transport of water away from the compressed regions. These finding suggest that local compression could be used to enhance light-based diagnostic and therapeutic techniques.Abhijit A. Gurjarpadhye, William C. Vogt, Yajing Liu, and Christopher G. RylanderCopyright © 2011 Abhijit A. Gurjarpadhye et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/729732/We describe a computational framework for the quantitative assessment of contractile responses of isolated neonatal cardiac myocytes. To the best of our knowledge, this is the first report on a practical and accessible method for the assessment of contractility in neonatal cardiocytes. The proposed methodology is comprised of digital video recording of the contracting cell, signal preparation, representation by polar Fourier descriptors, and contractility assessment. The different processing stages are variants of mathematically sound and computationally robust algorithms very well established in the scientific community. The described computational approach provides a comprehensive assessment of the neonatal cardiac myocyte contraction without the need of elaborate instrumentation. The versatility of the methodology allows it to be employed in determining myocyte contractility almost simultaneously with the acquisition of the Ca2+ transient and other correlates of cell contraction. The proposed methodology can be utilized to evaluate changes in contractile behavior resulting from drug intervention, disease models, transgeneity, or other common applications of neonatal cardiocytes.Carlos Bazan, David Torres Barba, Peter Blomgren, and Paul PaoliniCopyright © 2011 Carlos Bazan et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/621905/We propose a medical image segmentation approach based on the Active Shape Model theory. We apply this method for cervical vertebra detection. The main advantage of this approach is the application of a statistical model created after a training stage. Thus, the knowledge and interaction of the domain expert intervene in this approach. Our application allows the use of two different models, that is, a global one (with several vertebrae) and a local one (with a single vertebra). Two modes of segmentation are also proposed: manual and semiautomatic. For the manual mode, only two points are selected by the user on a given image. The first point needs to be close to the lower anterior corner of the last vertebra and the second near the upper anterior corner of the first vertebra. These two points are required to initialize the segmentation process. We propose to use the Harris corner detector combined with three successive filters to carry out the semiautomatic process. The results obtained on a large set of X-ray images are very promising.Mohammed Benjelloun, Saïd Mahmoudi, and Fabian LecronCopyright © 2011 Mohammed Benjelloun et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/426503/Diffuse Optical Tomography (DOT) is an optical imaging modality that has various clinical applications. However, the spatial resolution and quantitative accuracy of DOT is poor due to strong photon scatting in biological tissue. Structural a priori information from another high spatial resolution imaging modality such as Magnetic Resonance Imaging (MRI) has been demonstrated to significantly improve DOT accuracy. In addition, a contrast agent can be used to obtain differential absorption images of the lesion by using dynamic contrast enhanced DOT (DCE-DOT). This produces a relative absorption map that consists of subtracting a reconstructed baseline image from reconstructed images in which optical contrast is included. In this study, we investigated and compared different reconstruction methods and analysis approaches for regular endogenous DOT and DCE-DOT with and without MR anatomical a priori information for arbitrarily-shaped objects. Our phantom and animal studies have shown that superior image quality and higher accuracy can be achieved using DCE-DOT together with MR structural a priori information. Hence, implementation of a combined MRI-DOT system to image ICG enhancement can potentially be a promising tool for breast cancer imaging.Michael Ghijsen, Yuting Lin, Mitchell Hsing, Orhan Nalcioglu, and Gultekin GulsenCopyright © 2011 Michael Ghijsen et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/370701/Molecular imaging, including fluorescence imaging (FMI), bioluminescence imaging (BLI), positron emission tomography (PET), single-photon emission-computed tomography (SPECT), and computed tomography (CT), has a pivotal role in the process of tumor and relevant drug research. CT, especially Micro-CT, can provide the anatomic information for a region of interest (ROI); PET and SPECT can provide functional information for the ROI. BLI and FMI can provide optical information for an ROI. Tumor angiogenesis and relevant drug development is a lengthy, high-risk, and costly process, in which a novel drug needs about 10–15 years of testing to obtain Federal Drug Association (FDA) approval. Molecular imaging can enhance the development process by understanding the tumor mechanisms and drug activity. In this paper, we focus on tumor angiogenesis, and we review the characteristics of molecular imaging modalities and their applications in tumor angiogenesis and relevant drug research.Xibo Ma, Jie Tian, Xin Yang, and Chenghu QinCopyright © 2011 Xibo Ma et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/868632/Aim. To determine the level of the conus medullaris-Tuffier's line, and conus medullaris-Tuffier's line distance using imaging and evaluate their relation to age and gender. Methods. We performed a cross-sectional study of 189 adult participants, who underwent MR imaging of lumbosacral spine. Each vertebra was divided into 3 equal segments (upper, middle, and lower), and intervertebral disc space was also assumed as one segment. All segments from T12 upper segment to L5S1 intervertebral disc were numbered consecutively. The position of conus medullaris and Tuffier's line was determined by the vertebral segment or intervertebral disc space at the same level. The patients were stratified into high/low conus medullaris position (cutpoint: L1 middle segment) and short/long conus-Tuffier's distance (cutpoint: 14 segments). Results. Women with low conus were significantly more than men, in patients older than 50 years old (72.7% in females versus 55.3% in males; P<.05), whereas there was not such a sexual dimorphism in patients younger than 50 years old. Similarly, short conus-Tuffier's distance was more frequent among women than men in patients older than 50 years old (59.7% in females versus 39.5% in males; P<.05), whereas there was not any gender difference in patients younger than 50 years old. Conus-Tuffier's distance was negatively correlated with age (r=−0.32, P<.001) in all studied population. Conclusion. Anatomical landmarks vary according to age and gender, with a lower end of conus medullaris in women, so clinicians should use more caution on the identification of the appropriate site for lumbar puncture, particularly in elderly women.Maryam Rahmani, Seyed Mehran Vaziri Bozorg, Ahmad Reza Ghasemi Esfe, Afsaneh Morteza, Omid Khalilzadeh, Elham Pedarzadeh, and Madjid ShakibaCopyright © 2011 Maryam Rahmani et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/270247/This paper presents an automatic detection method for thin boundaries of silver-stained endothelial cells (ECs) imaged using light microscopy of endothelium mono-layers from rabbit aortas. To achieve this, a segmentation technique was developed, which relies on a rich feature space to describe the spatial neighbourhood of each pixel and employs a Support Vector Machine (SVM) as a classifier. This segmentation approach is compared, using hand-labelled data, to a number of standard segmentation/thresholding methods commonly applied in microscopy. The importance of different features is also assessed using the method of minimum Redundancy, Maximum Relevance (mRMR), and the effect of different SVM kernels is also considered. The results show that the approach suggested in this paper attains much greater accuracy than standard techniques; in our comparisons with manually labelled data, our proposed technique is able to identify boundary pixels to an accuracy of 93%. More significantly, out of a set of 56 regions of image data, 43 regions were binarised to a useful level of accuracy. The results obtained from the image segmentation technique developed here may be used for the study of shape and alignment of ECs, and hence patterns of blood flow, around arterial branches.Saadia Iftikhar, Andrew R. Bond, Asim I. Wagan, Peter D. Weinberg, and Anil A. BharathCopyright © 2011 Saadia Iftikhar et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/693795/Single photon emission computed tomography (SPECT) imaging is widely implemented in nuclear medicine as its clinical role in the diagnosis and management of several diseases is, many times, very helpful (e.g., myocardium perfusion imaging). The quality of SPECT images are degraded by several factors such as noise because of the limited number of counts, attenuation, or scatter of photons. Image filtering is necessary to compensate these effects and, therefore, to improve image quality. The goal of filtering in tomographic images is to suppress statistical noise and simultaneously to preserve spatial resolution and contrast. The aim of this work is to describe the most widely used filters in SPECT applications and how these affect the image quality. The choice of the filter type, the cut-off frequency and the order is a major problem in clinical routine. In many clinical cases, information for specific parameters is not provided, and findings cannot be extrapolated to other similar SPECT imaging applications. A literature review for the determination of the mostly used filters in cardiac, brain, bone, liver, kidneys, and thyroid applications is also presented. As resulting from the overview, no filter is perfect, and the selection of the proper filters, most of the times, is done empirically. The standardization of image-processing results may limit the filter types for each SPECT examination to certain few filters and some of their parameters. Standardization, also, helps in reducing image processing time, as the filters and their parameters must be standardised before being put to clinical use. Commercial reconstruction software selections lead to comparable results interdepartmentally. The manufacturers normally supply default filters/parameters, but these may not be relevant in various clinical situations. After proper standardisation, it is possible to use many suitable filters or one optimal filter.Maria Lyra and Agapi PloussiCopyright © 2011 Maria Lyra and Agapi Ploussi. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/606857/Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.Cheng Chen, John A. Ozolek, Wei Wang, and Gustavo K. RohdeCopyright © 2011 Cheng Chen et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/913893/Imaging processes built on the Compton scattering effect have been under continuing investigation since it was first suggested in the 50s. However, despite many innovative contributions, there are still formidable theoretical and technical challenges to overcome. In this paper, we review the state-of-the-art principles of the so-called scattered radiation emission imaging. Basically, it consists of using the cleverly collected scattered radiation from a radiating object to reconstruct its inner structure. Image formation is based on the mathematical concept of compounded conical projection. It entails a Radon transform defined on circular cone surfaces in order to express the scattered radiation flux density on a detecting pixel. We discuss in particular invertible cases of such conical Radon transforms which form a mathematical basis for image reconstruction methods. Numerical simulations performed in two and three space dimensions speak in favor of the viability of this imaging principle and its potential applications in various fields.M. K. Nguyen, T. T. Truong, M. Morvidone, and H. ZaidiCopyright © 2011 M. K. Nguyen et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/361589/Background. This study presents a semiautomated approach for volumetric analysis of lung tumors and evaluates the feasibility of using volumes as an alternative to line lengths as a basis for response evaluation criteria in solid tumors (RECIST). The overall goal for the implementation was to accurately, precisely, and efficiently enable the analyses of lesions in the lung under the guidance of an operator. Methods. An anthropomorphic phantom with embedded model masses and 71 time points in 10 clinical cases with advanced lung cancer was analyzed using a semi-automated workflow. The implementation was done using the Cognition Network Technology. Results. Analysis of the phantom showed an average accuracy of 97%. The analyses of the clinical cases showed both intra- and interreader variabilities of approximately 5% on average with an upper 95% confidence interval of 14% and 19%, respectively. Compared to line lengths, the use of volumes clearly shows enhanced sensitivity with respect to determining response to therapy. Conclusions. It is feasible to perform volumetric analysis efficiently with high accuracy and low variability, even in patients with late-stage cancer who have complex lesions.C. Bendtsen, M. Kietzmann, R. Korn, P. D. Mozley, G. Schmidt, and G. BinnigCopyright © 2011 C. Bendtsen et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/874702/Cell proliferation is critical to the outgrowth of biological structures including the face and limbs. This cellular process has traditionally been studied via sequential histological sampling of these tissues. The length and tedium of traditional sampling is a major impediment to analyzing the large datasets required to accurately model cellular processes. Computerized cell localization and quantification is critical for high-throughput morphometric analysis of developing embryonic tissues. We have developed the Incremental Cell Search (ICS), a novel software tool that expedites the analysis of relationships between morphological outgrowth and cell proliferation in embryonic tissues. Based on an estimated average cell size and stain color, ICS rapidly indicates the approximate location and amount of cells in histological images of labeled embryonic tissue and provides estimates of cell counts in regions with saturated fluorescence and blurred cell boundaries. This capacity opens the door to high-throughput 3D and 4D quantitative analyses of developmental patterns.Oscar E. Meruvia-Pastor, Jung Soh, Eric J. Schmidt, Julia C. Boughner, Mei Xiao, Heather A. Jamniczky, Benedikt Hallgrímsson, and Christoph W. SensenCopyright © 2011 Oscar E. Meruvia-Pastor et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/196460/The need for automated identification of a disease makes the issue of medical biometrics very current in our society. Not all biometric tools available provide real-time feedback. We introduce gas discharge visualization (GDV) technique as one of the biometric tools that have the potential to identify deviations from the normal functional state at early stages and in real time. GDV is a nonintrusive technique to capture the physiological and psychoemotional status of a person and the functional status of different organs and organ systems through the electrophotonic emissions of fingertips placed on the surface of an impulse analyzer. This paper first introduces biometrics and its different types and then specifically focuses on medical biometrics and the potential applications of GDV in medical biometrics. We also present our previous experience with GDV in the research regarding autism and the potential use of GDV in combination with computer science for the potential development of biological pattern/biomarker for different kinds of health abnormalities including cancer and mental diseases.Nataliya Kostyuk, Phyadragren Cole, Natarajan Meghanathan, Raphael D. Isokpehi, and Hari H. P. CohlyCopyright © 2011 Nataliya Kostyuk et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/305968/Medical imaging system simulators are tools that provide a means to evaluate system architecture and create artificial image sets that are appropriate for specific applications. We have modified SIMRI, a Bloch equation-based magnetic resonance image simulator, in order to successfully generate high-resolution 3D MR images of the Montreal brain phantom using Blue Gene/L systems. Results show that redistribution of the workload allows an anatomically accurate 2563 voxel spin-echo simulation in less than 5 hours when executed on an 8192-node partition of a Blue Gene/L system.K. G. Baum, G. Menezes, and M. HelgueraCopyright © 2011 K. G. Baum et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2011/641618/Cerenkov luminescence imaging (CLI) is a cost-effective molecular imaging tool for biomedical applications of radiotracers. The introduction of Cerenkov luminescence tomography (CLT) relative to planar CLI can be compared to the development of X-ray CT based on radiography. With CLT, quantitative and localized analysis of a radiopharmaceutical distribution becomes feasible. In this contribution, a feasibility study of in vivo radiopharmaceutical imaging in heterogeneous medium is presented. Coupled with a multimodal in vivo imaging system, this CLT reconstruction method allows precise anatomical registration of the positron probe in heterogeneous tissues and facilitates the more widespread application of radiotracers. Source distribution inside the small animal is obtained from CLT reconstruction. The experimental results demonstrated that CLT can be employed as an available in vivo tomographic imaging of charged particle emitters in a heterogeneous medium.Jianghong Zhong, Chenghu Qin, Xin Yang, Shuping Zhu, Xing Zhang, and Jie TianCopyright © 2011 Jianghong Zhong et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2010/781095/Microwave breast cancer detection is based on the dielectric contrast between healthy and malignant tissue. This radar-based imaging method involves illumination of the breast with an ultra-wideband pulse. Detection of tumors within the breast is achieved by some selected focusing technique. Image formation algorithms are tailored to enhance tumor responses and reduce early-time and late-time clutter associated with skin reflections and heterogeneity of breast tissue. In this contribution, we evaluate the performance of the so-called cross-correlated back projection imaging scheme by using a scanning system in phantom experiments. Supplementary numerical modeling based on commercial software is also presented. The phantom is synthetically scanned with a broadband elliptical antenna in a mono-static configuration. The respective signals are pre-processed by a data-adaptive RLS algorithm in order to remove artifacts caused by antenna reverberations and signal clutter. Successful detection of a 7 mm diameter cylindrical tumor immersed in a low permittivity medium was achieved in all cases. Selecting the widely used delay-and-sum (DAS) beamforming algorithm as a benchmark, we show that correlation based imaging methods improve the signal-to-clutter ratio by at least 10 dB and improves spatial resolution through a reduction of the imaged peak full-width half maximum (FWHM) of about 40–50%.S. Jacobsen and Y. BirkelundCopyright © 2010 S. Jacobsen and Y. Birkelund. All rights reserved.http://www.hindawi.com/journals/ijbi/2010/425891/In Magnetic Resonance Imaging typical clinical settings, both low- and high-resolution images of different types are routinarily acquired. In some cases, the acquired low-resolution images have to be upsampled to match with other high-resolution images for posterior analysis or postprocessing such as registration or multimodal segmentation. However, classical interpolation techniques are not able to recover the high-frequency information lost during the acquisition process. In the present paper, a new superresolution method is proposed to reconstruct high-resolution images from the low-resolution ones using information from coplanar high resolution images acquired of the same subject. Furthermore, the reconstruction process is constrained to be physically plausible with the MR acquisition model that allows a meaningful interpretation of the results. Experiments on synthetic and real data are supplied to show the effectiveness of the proposed approach. A comparison with classical state-of-the-art interpolation techniques is presented to demonstrate the improved performance of the proposed methodology.José V. Manjón, Pierrick Coupé, Antonio Buades, D. Louis Collins, and Montserrat RoblesCopyright © 2010 José V. Manjón et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2010/983963/This paper presents a new, automatic method of accurately extracting lesions from CT data. It first determines, at each voxel, a five-dimensional (5D) feature vector that contains intensity, shape index, and 3D spatial location. Then, nonparametric mean shift clustering forms superpixels from these 5D features, resulting in an oversegmentation of the image. Finally, a graph cut algorithm groups the superpixels using a novel energy formulation that incorporates shape, intensity, and spatial features. The mean shift superpixels increase the robustness of the result while reducing the computation time. We assume that the lesion is part spherical, resulting in high shape index values in a part of the lesion. From these spherical subregions, foreground and background seeds for the graph cut segmentation can be automatically obtained. The proposed method has been evaluated on a clinical CT dataset. Visual inspection on different types of lesions (lung nodules and colonic polyps), as well as a quantitative evaluation on 101 solid and 80 GGO nodules, both demonstrate the potential of the proposed method. The joint spatial-intensity-shape features provide a powerful cue for successful segmentation of lesions adjacent to structures of similar intensity but different shape, as well as lesions exhibiting partial volume effect.Xujiong Ye, Gareth Beddoe, and Greg SlabaughCopyright © 2010 Xujiong Ye et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2010/654506/Introduction. Spatially registering SPECT with CT makes it possible to anatomically localize SPECT tracers. In this study, an accurate method for the coregistration of ultra-high-resolution SPECT volumes and multiple cone-beam CT volumes is developed and validated, which does not require markers during animal scanning. Methods. Transferable animal beds were developed with an accurate mounting interface. Simple calibration phantoms make it possible to obtain both the spatial transformation matrix for stitching multiple CT scans of different parts of the animal and to register SPECT and CT. The spatial transformation for image coregistration is calculated once using Horn's matching algorithm. Animal images can then be coregistered without using markers. Results. For mouse-sized objects, average coregistration errors between SPECT and CT in X, Y, and Z directions are within 0.04 mm, 0.10 mm, and 0.19 mm, respectively. For rat-sized objects, these numbers are 0.22 mm, 0.14 mm, and 0.28 mm. Average 3D coregistration errors were within 0.24 mm and 0.42 mm for mouse and rat imaging, respectively. Conclusion. Extending the field-of-view of cone-beam CT by stitching is improved by prior registration of the CT volumes. The accuracy of registration between SPECT and CT is typically better than the image resolution of current ultra-high-resolution SPECT.Changguo Ji, Frans van der Have, Hugo Gratama van Andel, Ruud Ramakers, and Freek BeekmanCopyright © 2010 Changguo Ji et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2010/105610/Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.Mhd Saeed Sharif, Maysam Abbod, Abbes Amira, and Habib ZaidiCopyright © 2010 Mhd Saeed Sharif et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2010/974957/3D surfaces are important geometric models for many objects of interest in image analysis and Computational Anatomy. In this paper, we describe a Bayesian inference scheme for estimating a template surface from a set of observed surface data. In order to achieve this, we use the geodesic shooting approach to construct a statistical model for the generation and the observations of random surfaces. We develop a mode approximation EM algorithm to infer the maximum a posteriori estimation of initial momentum μ, which determines the template surface. Experimental results of caudate, thalamus, and hippocampus data are presented.Jun Ma, Michael I. Miller, and Laurent YounesCopyright © 2010 Jun Ma et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2010/906067/Motivated by the observation that a retinal fundus image may contain some unique geometric structures within its vascular trees which can be utilized for feature matching, in this paper, we proposed a graph-based registration framework called GM-ICP to align pairwise retinal images. First, the retinal vessels are automatically detected and represented as vascular structure graphs. A graph matching is then performed to find global correspondences between vascular bifurcations. Finally, a revised ICP algorithm incorporating with quadratic transformation model is used at fine level to register vessel shape models. In order to eliminate the incorrect matches from global correspondence set obtained via graph matching, we proposed a structure-based sample consensus (STRUCT-SAC) algorithm. The advantages of our approach are threefold: (1) global optimum solution can be achieved with graph matching; (2) our method is invariant to linear geometric transformations; and (3) heavy local feature descriptors are not required. The effectiveness of our method is demonstrated by the experiments with 48 pairs retinal images collected from clinical patients.Kexin Deng, Jie Tian, Jian Zheng, Xing Zhang, Xiaoqian Dai, and Min XuCopyright © 2010 Kexin Deng et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2010/471408/The peak signal or the signal at a predetermined, fixed time point after D-luciferin injection may be used for the quantitative analysis of in vivo bioluminescence imaging. We repeatedly performed sequential bioluminescence imaging after subcutaneous injection of D-luciferin in mice bearing subcutaneous tumors. The peak time in each measurement became shorter early after cell inoculation, presumably due to gradual establishment of intratumoral vasculature, and reached a plateau of about 10 min on day 10. Although the correlation between the signal at a fixed time point and the peak signal was high, the signal at 5 or 10 min normalized for the peak signal was lower for earlier days, which caused overestimation of tumor growth. The time course of the signals after D-luciferin injection may vary with time after cell inoculation, and this variation should be considered when determining the imaging protocol for quantitative bioluminescence tumor monitoring.Yusuke Inoue, Shigeru Kiryu, Makoto Watanabe, Arinobu Tojo, and Kuni OhtomoCopyright © 2010 Yusuke Inoue et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2010/780262/A method for correction of nonhomogenous illumination based on optimization of parameters of B-spline shading model with respect to Shannon's entropy is presented. The evaluation of Shannon's entropy is based on Parzen windowing method (Mangin, 2000) with the spline-based shading model. This allows us to express the derivatives of the entropy criterion analytically, which enables efficient use of gradient-based optimization algorithms. Seven different gradient- and nongradient-based optimization algorithms were initially tested on a set of 40 simulated retinal images, generated by a model of the respective image acquisition system. Among the tested optimizers, the gradient-based optimizer with varying step has shown to have the fastest convergence while providing the best precision. The final algorithm proved to be able of suppressing approximately 70% of the artificially introduced non-homogenous illumination. To assess the practical utility of the method, it was qualitatively tested on a set of 336 real retinal images; it proved the ability of eliminating the illumination inhomogeneity substantially in most of cases. The application field of this method is especially in preprocessing of retinal images, as preparation for reliable segmentation or registration.Libor Kubecka, Jiri Jan, and Radim KolarCopyright © 2010 Libor Kubecka et al. All rights reserved.http://www.hindawi.com/journals/ijbi/2010/868976/Brain functional connectivity (FC) is often assessed from fMRI data using seed-based methods, such as those of detecting temporal correlation between a predefined region (seed) and all other regions in the brain; or using multivariate methods, such as independent component analysis (ICA). ICA is a useful data-driven tool, but reproducibility issues complicate group inferences based on FC maps derived with ICA. These reproducibility issues can be circumvented with hybrid methods that use information from ICA-derived spatial maps as seeds to produce seed-based FC maps. We report results from five experiments to demonstrate the potential advantages of hybrid ICA-seed-based FC methods, comparing results from regressing fMRI data against task-related a priori time courses, with “back-reconstruction” from a group ICA, and with five hybrid ICA-seed-based FC methods: ROI-based with (1) single-voxel, (2) few-voxel, and (3) many-voxel seed; and dual-regression-based with (4) single ICA map and (5) multiple ICA map seed.Robert E. Kelly, Zhishun Wang, George S. Alexopoulos, Faith M. Gunning, Christopher F. Murphy, Sarah Shizuko Morimoto, Dora Kanellopoulos, Zhiru Jia, Kelvin O. Lim, and Matthew J. HoptmanCopyright © 2010 Robert E. Kelly et al. All rights reserved.