International Journal of Biomedical Imaging The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. High-Speed GPU-Based Fully Three-Dimensional Diffuse Optical Tomographic System Thu, 10 Apr 2014 13:49:09 +0000 We have developed a graphics processor unit (GPU-) based high-speed fully 3D system for diffuse optical tomography (DOT). The reduction in execution time of 3D DOT algorithm, a severely ill-posed problem, is made possible through the use of (1) an algorithmic improvement that uses Broyden approach for updating the Jacobian matrix and thereby updating the parameter matrix and (2) the multinode multithreaded GPU and CUDA (Compute Unified Device Architecture) software architecture. Two different GPU implementations of DOT programs are developed in this study: (1) conventional C language program augmented by GPU CUDA and CULA routines (C GPU), (2) MATLAB program supported by MATLAB parallel computing toolkit for GPU (MATLAB GPU). The computation time of the algorithm on host CPU and the GPU system is presented for C and Matlab implementations. The forward computation uses finite element method (FEM) and the problem domain is discretized into 14610, 30823, and 66514 tetrahedral elements. The reconstruction time, so achieved for one iteration of the DOT reconstruction for 14610 elements, is 0.52 seconds for a C based GPU program for 2-plane measurements. The corresponding MATLAB based GPU program took 0.86 seconds. The maximum number of reconstructed frames so achieved is 2 frames per second. Manob Jyoti Saikia, Rajan Kanhirodan, and Ram Mohan Vasu Copyright © 2014 Manob Jyoti Saikia et al. All rights reserved. MRI Volume Fusion Based on 3D Shearlet Decompositions Thu, 10 Apr 2014 08:53:57 +0000 Nowadays many MRI scans can give 3D volume data with different contrasts, but the observers may want to view various contrasts in the same 3D volume. The conventional 2D medical fusion methods can only fuse the 3D volume data layer by layer, which may lead to the loss of interframe correlative information. In this paper, a novel 3D medical volume fusion method based on 3D band limited shearlet transform (3D BLST) is proposed. And this method is evaluated upon MRI and quantitative susceptibility mapping data of 4 human brains. Both the perspective impression and the quality indices indicate that the proposed method has a better performance than conventional 2D wavelet, DT CWT, and 3D wavelet, DT CWT based fusion methods. Chang Duan, Shuai Wang, Xue Gang Wang, and Qi Hong Huang Copyright © 2014 Chang Duan et al. All rights reserved. Despeckle Filtering for Multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) Texture Analysis of Ultrasound Images of the Intima-Media Complex Sun, 09 Mar 2014 12:55:30 +0000 The intima-media thickness (IMT) of the common carotid artery (CCA) is widely used as an early indicator of cardiovascular disease (CVD). Typically, the IMT grows with age and this is used as a sign of increased risk of CVD. Beyond thickness, there is also clinical interest in identifying how the composition and texture of the intima-media complex (IMC) changed and how these textural changes grow into atherosclerotic plaques that can cause stroke. Clearly though texture analysis of ultrasound images can be greatly affected by speckle noise, our goal here is to develop effective despeckle noise methods that can recover image texture associated with increased rates of atherosclerosis disease. In this study, we perform a comparative evaluation of several despeckle filtering methods, on 100 ultrasound images of the CCA, based on the extracted multiscale Amplitude-Modulation Frequency-Modulation (AM-FM) texture features and visual image quality assessment by two clinical experts. Texture features were extracted from the automatically segmented IMC for three different age groups. The despeckle filters hybrid median and the homogeneous mask area filter showed the best performance by improving the class separation between the three age groups and also yielded significantly improved image quality. C. P. Loizou, V. Murray, M. S. Pattichis, M. Pantziaris, A. N. Nicolaides, and C. S. Pattichis Copyright © 2014 C. P. Loizou et al. All rights reserved. Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation Sun, 02 Mar 2014 16:10:52 +0000 Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities. Guodong Wang, Jie Xu, Qian Dong, and Zhenkuan Pan Copyright © 2014 Guodong Wang et al. All rights reserved. A Framework for the Objective Assessment of Registration Accuracy Mon, 10 Feb 2014 14:46:07 +0000 Validation and accuracy assessment are the main bottlenecks preventing the adoption of image processing algorithms in the clinical practice. In the classical approach, a posteriori analysis is performed through objective metrics. In this work, a different approach based on Petri nets is proposed. The basic idea consists in predicting the accuracy of a given pipeline based on the identification and characterization of the sources of inaccuracy. The concept is demonstrated on a case study: intrasubject rigid and affine registration of magnetic resonance images. Both synthetic and real data are considered. While synthetic data allow the benchmarking of the performance with respect to the ground truth, real data enable to assess the robustness of the methodology in real contexts as well as to determine the suitability of the use of synthetic data in the training phase. Results revealed a higher correlation and a lower dispersion among the metrics for simulated data, while the opposite trend was observed for pathologic ones. Results show that the proposed model not only provides a good prediction performance but also leads to the optimization of the end-to-end chain in terms of accuracy and robustness, setting the ground for its generalization to different and more complex scenarios. Francesca Pizzorni Ferrarese, Flavio Simonetti, Roberto Israel Foroni, and Gloria Menegaz Copyright © 2014 Francesca Pizzorni Ferrarese et al. All rights reserved. Registration of the Cone Beam CT and Blue-Ray Scanned Dental Model Based on the Improved ICP Algorithm Sun, 05 Jan 2014 12:28:09 +0000 Multimodality image registration and fusion has complementary significance for guiding dental implant surgery. As the needs of the different resolution image registration, we develop an improved Iterative Closest Point (ICP) algorithm that focuses on the registration of Cone Beam Computed Tomography (CT) image and high-resolution Blue-light scanner image. The proposed algorithm includes two major phases, coarse and precise registration. Firstly, for reducing the matching interference of human subjective factors, we extract feature points based on curvature characteristics and use the improved three point’s translational transformation method to realize coarse registration. Then, the feature point set and reference point set, obtained by the initial registered transformation, are processed in the precise registration step. Even with the unsatisfactory initial values, this two steps registration method can guarantee the global convergence and the convergence precision. Experimental results demonstrate that the method has successfully realized the registration of the Cone Beam CT dental model and the blue-ray scanner model with higher accuracy. So the method could provide researching foundation for the relevant software development in terms of the registration of multi-modality medical data. Xue Mei, Zhenhua Li, Songsong Xu, and Xiaoyan Guo Copyright © 2014 Xue Mei et al. All rights reserved. Blind Deconvolution for Ultrasound Sequences Using a Noninverse Greedy Algorithm Sun, 29 Dec 2013 09:21:06 +0000 The blind deconvolution of ultrasound sequences in medical ultrasound technique is still a major problem despite the efforts made. This paper presents a blind noninverse deconvolution algorithm to eliminate the blurring effect, using the envelope of the acquired radio-frequency sequences and a priori Laplacian distribution for deconvolved signal. The algorithm is executed in two steps. Firstly, the point spread function is automatically estimated from the measured data. Secondly, the data are reconstructed in a nonblind way using proposed algorithm. The algorithm is a nonlinear blind deconvolution which works as a greedy algorithm. The results on simulated signals and real images are compared with different state of the art methods deconvolution. Our method shows good results for scatters detection, speckle noise suppression, and execution time. Liviu-Teodor Chira, Corneliu Rusu, Clovis Tauber, and Jean-Marc Girault Copyright © 2013 Liviu-Teodor Chira et al. All rights reserved. An Investigation of Calibration Phantoms for CT Scanners with Tube Voltage Modulation Wed, 25 Dec 2013 15:28:55 +0000 The effects of calibration phantoms on the correction results of the empirical artifacts correction method (ECCU) for the case of tube modulation were investigated. To improve the validity of the ECCU method, the effect of the geometry parameter of a typical single-material calibration phantom (water calibration phantom) on the ECCU algorithm was investigated. Dual-material calibration phantoms (such as water-bone calibration phantom), geometry arrangement, and the area-ratio of dual-material calibration phantoms were also studied. Preliminary results implied that, to assure the effectiveness of the ECCU algorithm, the polychromatic projections of calibration phantoms must cover the polychromatic projection data of the scanning object. However, the projection range of a water calibration phantom is limited by the scan field of view (SFOV), thus leading to methodological limitations. A dual-material phantom of a proper size and material can overcome the limitations of a single-material phantom and achieve good correction effects. Jing Zou, Xiaodong Hu, Hanyu Lv, and Xiaotang Hu Copyright © 2013 Jing Zou et al. All rights reserved. Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms Mon, 23 Dec 2013 10:38:36 +0000 Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique’s performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided. Ammara Masood and Adel Ali Al-Jumaily Copyright © 2013 Ammara Masood and Adel Ali Al-Jumaily. All rights reserved. Robust Vessel Segmentation in Fundus Images Thu, 12 Dec 2013 11:39:35 +0000 One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods. A. Budai, R. Bock, A. Maier, J. Hornegger, and G. Michelson Copyright © 2013 A. Budai et al. All rights reserved. Improving Image Quality in Medical Images Using a Combined Method of Undecimated Wavelet Transform and Wavelet Coefficient Mapping Sat, 07 Dec 2013 14:02:05 +0000 We propose a method for improving image quality in medical images by using a wavelet-based approach. The proposed method integrates two components: image denoising and image enhancement. In the first component, a modified undecimated discrete wavelet transform is used to eliminate the noise. In the second component, a wavelet coefficient mapping function is applied to enhance the contrast of denoised images obtained from the first component. This methodology can be used not only as a means for improving visual quality of medical images but also as a preprocessing module for computer-aided detection/diagnosis systems to improve the performance of screening and detecting regions of interest in images. To confirm its superiority over existing state-of-the-art methods, the proposed method is experimentally evaluated via 30 mammograms and 20 chest radiographs. It is demonstrated that the proposed method can further improve the image quality of mammograms and chest radiographs, as compared to two other methods in the literature. These results reveal the effectiveness and superiority of the proposed method. Du-Yih Tsai, Eri Matsuyama, and Hsian-Min Chen Copyright © 2013 Du-Yih Tsai et al. All rights reserved. A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain Wed, 27 Nov 2013 09:16:38 +0000 Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network. Xiaojin Li, Xintao Hu, Changfeng Jin, Junwei Han, Tianming Liu, Lei Guo, Wei Hao, and Lingjiang Li Copyright © 2013 Xiaojin Li et al. All rights reserved. Comparison of User-Directed and Automatic Mapping of the Planned Isocenter to Treatment Space for Prostate IGRT Thu, 21 Nov 2013 14:55:03 +0000 Image-guided radiotherapy (IGRT), adaptive radiotherapy (ART), and online reoptimization rely on accurate mapping of the radiation beam isocenter(s) from planning to treatment space. This mapping involves rigid and/or nonrigid registration of planning (pCT) and intratreatment (tCT) CT images. The purpose of this study was to retrospectively compare a fully automatic approach, including a non-rigid step, against a user-directed rigid method implemented in a clinical IGRT protocol for prostate cancer. Isocenters resulting from automatic and clinical mappings were compared to reference isocenters carefully determined in each tCT. Comparison was based on displacements from the reference isocenters and prostate dose-volume histograms (DVHs). Ten patients with a total of 243 tCTs were investigated. Fully automatic registration was found to be as accurate as the clinical protocol but more precise for all patients. The average of the unsigned and offsets and the standard deviations (σ) of the signed offsets computed over all images were (avg. ±  σ (mm)): 1.1 ± 1.4, 1.8 ± 2.3, 2.5 ± 3.5 for the clinical protocol and 0.6 ± 0.8, 1.1 ± 1.5 and 1.1 ± 1.4 for the automatic method. No failures or outliers from automatic mapping were observed, while 8 outliers occurred for the clinical protocol. Zijie Xu, Ronald Chen, Andrew Wang, Andrea Kress, Mark Foskey, An Qin, Timothy Cullip, Gregg Tracton, Sha Chang, Joel Tepper, Di Yan, and Edward Chaney Copyright © 2013 Zijie Xu et al. All rights reserved. Contrast Improvement in Sub- and Ultraharmonic Ultrasound Contrast Imaging by Combining Several Hammerstein Models Thu, 07 Nov 2013 08:55:36 +0000 Sub- and ultraharmonic (SUH) ultrasound contrast imaging is an alternative modality to the second harmonic imaging, since, in specific conditions it could produce high quality echographic images. This modality enables the contrast enhancement of echographic images by using SUH present in the contrast agent response but absent from the nonperfused tissue. For a better access to the components generated by the ultrasound contrast agents, nonlinear techniques based on Hammerstein model are preferred. As the major limitation of Hammerstein model is its capacity of modeling harmonic components only, in this work we propose two methods allowing to model SUH. These new methods use several Hammerstein models to identify contrast agent signals having SUH components and to separate these components from harmonic components. The application of the proposed methods for modeling simulated contrast agent signals shows their efficiency in modeling these signals and in separating SUH components. The achieved gain with respect to the standard Hammerstein model was 26.8 dB and 22.8 dB for the two proposed methods, respectively. Fatima Sbeity, Sébastien Ménigot, Jamal Charara, and Jean-Marc Girault Copyright © 2013 Fatima Sbeity et al. All rights reserved. Endoscopy-MR Image Fusion for Image Guided Procedures Sat, 02 Nov 2013 13:46:47 +0000 Minimally invasive endoscope based abdominal procedures provide potential advantages over conventional open surgery such as reduced trauma, shorter hospital stay, and quick recovery. One major limitation of using this technique is the narrow view of the endoscope and the lack of proper 3D context of the surgical site. In this paper, we propose a rapid and accurate method to align intraoperative stereo endoscopic images of the surgical site with preoperative Magnetic Resonance (MR) images. Gridline light pattern is projected on the surgical site to facilitate the registration. The purpose of this surface-based registration is to provide 3D context of the surgical site to the endoscopic view. We have validated the proposed method on a liver phantom and achieved the surface registration error of  mm. Anwar Abdalbari, Xishi Huang, and Jing Ren Copyright © 2013 Anwar Abdalbari et al. All rights reserved. Measurement of Intervertebral Cervical Motion by Means of Dynamic X-Ray Image Processing and Data Interpolation Thu, 31 Oct 2013 10:58:33 +0000 Accurate measurement of intervertebral kinematics of the cervical spine can support the diagnosis of widespread diseases related to neck pain, such as chronic whiplash dysfunction, arthritis, and segmental degeneration. The natural inaccessibility of the spine, its complex anatomy, and the small range of motion only permit concise measurement in vivo. Low dose X-ray fluoroscopy allows time-continuous screening of cervical spine during patient’s spontaneous motion. To obtain accurate motion measurements, each vertebra was tracked by means of image processing along a sequence of radiographic images. To obtain a time-continuous representation of motion and to reduce noise in the experimental data, smoothing spline interpolation was used. Estimation of intervertebral motion for cervical segments was obtained by processing patient’s fluoroscopic sequence; intervertebral angle and displacement and the instantaneous centre of rotation were computed. The RMS value of fitting errors resulted in about 0.2 degree for rotation and 0.2 mm for displacements. Paolo Bifulco, Mario Cesarelli, Maria Romano, Antonio Fratini, and Mario Sansone Copyright © 2013 Paolo Bifulco et al. All rights reserved. Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain Thu, 10 Oct 2013 15:44:41 +0000 In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR. Hossein Rabbani, Milan Sonka, and Michael D. Abramoff Copyright © 2013 Hossein Rabbani et al. All rights reserved. Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Data to Constrain a Positron Emission Tomography Kinetic Model: Theory and Simulations Thu, 03 Oct 2013 10:46:20 +0000 We show how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data can constrain a compartmental model for analyzing dynamic positron emission tomography (PET) data. We first develop the theory that enables the use of DCE-MRI data to separate whole tissue time activity curves (TACs) available from dynamic PET data into individual TACs associated with the blood space, the extravascular-extracellular space (EES), and the extravascular-intracellular space (EIS). Then we simulate whole tissue TACs over a range of physiologically relevant kinetic parameter values and show that using appropriate DCE-MRI data can separate the PET TAC into the three components with accuracy that is noise dependent. The simulations show that accurate blood, EES, and EIS TACs can be obtained as evidenced by concordance correlation coefficients >0.9 between the true and estimated TACs. Additionally, provided that the estimated DCE-MRI parameters are within 10% of their true values, the errors in the PET kinetic parameters are within approximately 20% of their true values. The parameters returned by this approach may provide new information on the transport of a tracer in a variety of dynamic PET studies. Jacob U. Fluckiger, Xia Li, Jennifer G. Whisenant, Todd E. Peterson, John C. Gore, and Thomas E. Yankeelov Copyright © 2013 Jacob U. Fluckiger et al. All rights reserved. Comparison of Super Resolution Reconstruction Acquisition Geometries for Use in Mouse Phenotyping Mon, 23 Sep 2013 11:41:07 +0000 3D isotropic imaging at high spatial resolution (30–100 microns) is important for comparing mouse phenotypes. 3D imaging at high spatial resolutions is limited by long acquisition times and is not possible in many in vivo settings. Super resolution reconstruction (SRR) is a postprocessing technique that has been proposed to improve spatial resolution in the slice-select direction using multiple 2D multislice acquisitions. Any 2D multislice acquisition can be used for SRR. In this study, the effects of using three different low-resolution acquisition geometries (orthogonal, rotational, and shifted) on SRR images were evaluated and compared to a known standard. Iterative back projection was used for the reconstruction of all three acquisition geometries. The results of the study indicate that super resolution reconstructed images based on orthogonally acquired low-resolution images resulted in reconstructed images with higher SNR and CNR in less acquisition time than those based on rotational and shifted acquisition geometries. However, interpolation artifacts were observed in SRR images based on orthogonal acquisition geometry, particularly when the slice thickness was greater than six times the inplane voxel size. Reconstructions based on rotational geometry appeared smoother than those based on orthogonal geometry, but they required two times longer to acquire than the orthogonal LR images. Niranchana Manivannan, Bradley D. Clymer, Anna Bratasz, and Kimerly A. Powell Copyright © 2013 Niranchana Manivannan et al. All rights reserved. Skin Parameter Map Retrieval from a Dedicated Multispectral Imaging System Applied to Dermatology/Cosmetology Wed, 18 Sep 2013 14:15:32 +0000 In vivo quantitative assessment of skin lesions is an important step in the evaluation of skin condition. An objective measurement device can help as a valuable tool for skin analysis. We propose an explorative new multispectral camera specifically developed for dermatology/cosmetology applications. The multispectral imaging system provides images of skin reflectance at different wavebands covering visible and near-infrared domain. It is coupled with a neural network-based algorithm for the reconstruction of reflectance cube of cutaneous data. This cube contains only skin optical reflectance spectrum in each pixel of the bidimensional spatial information. The reflectance cube is analyzed by an algorithm based on a Kubelka-Munk model combined with evolutionary algorithm. The technique allows quantitative measure of cutaneous tissue and retrieves five skin parameter maps: melanin concentration, epidermis/dermis thickness, haemoglobin concentration, and the oxygenated hemoglobin. The results retrieved on healthy participants by the algorithm are in good accordance with the data from the literature. The usefulness of the developed technique was proved during two experiments: a clinical study based on vitiligo and melasma skin lesions and a skin oxygenation experiment (induced ischemia) with healthy participant where normal tissues are recorded at normal state and when temporary ischemia is induced. Romuald Jolivot, Yannick Benezeth, and Franck Marzani Copyright © 2013 Romuald Jolivot et al. All rights reserved. Computational Representation of White Matter Fiber Orientations Tue, 20 Aug 2013 08:12:35 +0000 We present a new methodology based on directional data clustering to represent white matter fiber orientations in magnetic resonance analyses for high angular resolution diffusion imaging. A probabilistic methodology is proposed for estimating intravoxel principal fiber directions, based on clustering directional data arising from orientation distribution function (ODF) profiles. ODF reconstructions are used to estimate intravoxel fiber directions using mixtures of von Mises-Fisher distributions. The method focuses on clustering data on the unit sphere, where complexity arises from representing ODF profiles as directional data. The proposed method is validated on synthetic simulations, as well as on a real data experiment. Based on experiments, we show that by clustering profile data using mixtures of von Mises-Fisher distributions it is possible to estimate multiple fiber configurations in a more robust manner than currently used approaches, without recourse to regularization or sharpening procedures. The method holds promise to support robust tractographic methodologies and to build realistic models of white matter tracts in the human brain. Adelino R. Ferreira da Silva Copyright © 2013 Adelino R. Ferreira da Silva. All rights reserved. Microwave Imaging of Human Forearms: Pilot Study and Image Enhancement Mon, 19 Aug 2013 11:53:01 +0000 We present a pilot study using a microwave tomography system in which we image the forearms of 5 adult male and female volunteers between the ages of 30 and 48. Microwave scattering data were collected at 0.8 to 1.2 GHz with 24 transmitting and receiving antennas located in a matching fluid of deionized water and table salt. Inversion of the microwave data was performed with a balanced version of the multiplicative-regularized contrast source inversion algorithm formulated using the finite-element method (FEM-CSI). T1-weighted MRI images of each volunteer’s forearm were also collected in the same plane as the microwave scattering experiment. Initial “blind” imaging results from the utilized inversion algorithm show that the image quality is dependent on the thickness of the arm’s peripheral adipose tissue layer; thicker layers of adipose tissue lead to poorer overall image quality. Due to the exible nature of the FEM-CSI algorithm used, prior information can be readily incorporated into the microwave imaging inversion process. We show that by introducing prior information into the FEM-CSI algorithm the internal anatomical features of all the arms are resolved, significantly improving the images. The prior information was estimated manually from the blind inversions using an ad hoc procedure. Colin Gilmore, Amer Zakaria, Stephen Pistorius, and Joe LoVetri Copyright © 2013 Colin Gilmore et al. All rights reserved. Automated Segmentation and Object Classification of CT Images: Application to In Vivo Molecular Imaging of Avian Embryos Mon, 12 Aug 2013 09:09:32 +0000 Background. Although chick embryogenesis has been studied extensively, there has been growing interest in the investigation of skeletogenesis. In addition to improved poultry health and minimized economic loss, a greater understanding of skeletal abnormalities can also have implications for human medicine. True in vivo studies require noninvasive imaging techniques such as high-resolution microCT. However, the manual analysis of acquired images is both time consuming and subjective. Methods. We have developed a system for automated image segmentation that entails object-based image analysis followed by the classification of the extracted image objects. For image segmentation, a rule set was developed using Definiens image analysis software. The classification engine was implemented using the WEKA machine learning tool. Results. Our system reduces analysis time and observer bias while maintaining high accuracy. Applying the system to the quantification of long bone growth has allowed us to present the first true in ovo data for bone length growth recorded in the same chick embryos. Conclusions. The procedures developed represent an innovative approach for the automated segmentation, classification, quantification, and visualization of microCT images. MicroCT offers the possibility of performing longitudinal studies and thereby provides unique insights into the morpho- and embryogenesis of live chick embryos. Alexander Heidrich, Jana Schmidt, Johannes Zimmermann, and Hans Peter Saluz Copyright © 2013 Alexander Heidrich et al. All rights reserved. Automated Diagnosis of Otitis Media: Vocabulary and Grammar Wed, 07 Aug 2013 11:15:04 +0000 We propose a novel automated algorithm for classifying diagnostic categories of otitis media: acute otitis media, otitis media with effusion, and no effusion. Acute otitis media represents a bacterial superinfection of the middle ear fluid, while otitis media with effusion represents a sterile effusion that tends to subside spontaneously. Diagnosing children with acute otitis media is difficult, often leading to overprescription of antibiotics as they are beneficial only for children with acute otitis media. This underscores the need for an accurate and automated diagnostic algorithm. To that end, we design a feature set understood by both otoscopists and engineers based on the actual visual cues used by otoscopists; we term this the otitis media vocabulary. We also design a process to combine the vocabulary terms based on the decision process used by otoscopists; we term this the otitis media grammar. The algorithm achieves 89.9% classification accuracy, outperforming both clinicians who did not receive special training and state-of-the-art classifiers. Anupama Kuruvilla, Nader Shaikh, Alejandro Hoberman, and Jelena Kovačević Copyright © 2013 Anupama Kuruvilla et al. All rights reserved. Evaluation of Interpolation Effects on Upsampling and Accuracy of Cost Functions-Based Optimized Automatic Image Registration Thu, 01 Aug 2013 08:41:59 +0000 Interpolation has become a default operation in image processing and medical imaging and is one of the important factors in the success of an intensity-based registration method. Interpolation is needed if the fractional unit of motion is not matched and located on the high resolution (HR) grid. The purpose of this work is to present a systematic evaluation of eight standard interpolation techniques (trilinear, nearest neighbor, cubic Lagrangian, quintic Lagrangian, hepatic Lagrangian, windowed Sinc, B-spline 3rd order, and B-spline 4th order) and to compare the effect of cost functions (least squares (LS), normalized mutual information (NMI), normalized cross correlation (NCC), and correlation ratio (CR)) for optimized automatic image registration (OAIR) on 3D spoiled gradient recalled (SPGR) magnetic resonance images (MRI) of the brain acquired using a 3T GE MR scanner. Subsampling was performed in the axial, sagittal, and coronal directions to emulate three low resolution datasets. Afterwards, the low resolution datasets were upsampled using different interpolation methods, and they were then compared to the high resolution data. The mean squared error, peak signal to noise, joint entropy, and cost functions were computed for quantitative assessment of the method. Magnetic resonance image scans and joint histogram were used for qualitative assessment of the method. Amir Pasha Mahmoudzadeh and Nasser H. Kashou Copyright © 2013 Amir Pasha Mahmoudzadeh and Nasser H. Kashou. All rights reserved. Closed Contour Specular Reflection Segmentation in Laparoscopic Images Thu, 01 Aug 2013 08:23:25 +0000 Segmentation of specular reflections is an essential step in endoscopic image analysis; it affects all further processing steps including segmentation, classification, and registration tasks. The dichromatic reflectance model, which is often used for specular reflection modeling, is made for dielectric materials and not for human tissue. Hence, most recent segmentation approaches rely on thresholding techniques. In this work, we first demonstrate the limited accuracy that can be achieved by thresholding techniques and propose a hybrid method which is based on closed contours and thresholding. The method has been evaluated on 269 specular reflections in 49 images which were taken from 27 real laparoscopic interventions. Our method improves the average sensitivity by 16% compared to the state-of-the-art thresholding methods. Jan Marek Marcinczak and Rolf-Rainer Grigat Copyright © 2013 Jan Marek Marcinczak and Rolf-Rainer Grigat. All rights reserved. Breast Tissue 3D Segmentation and Visualization on MRI Mon, 29 Jul 2013 11:51:25 +0000 Tissue segmentation and visualization are useful for breast lesion detection and quantitative analysis. In this paper, a 3D segmentation algorithm based on Kernel-based Fuzzy C-Means (KFCM) is proposed to separate the breast MR images into different tissues. Then, an improved volume rendering algorithm based on a new transfer function model is applied to implement 3D breast visualization. Experimental results have been shown visually and have achieved reasonable consistency. Hong Song, Xiangfei Cui, and Feifei Sun Copyright © 2013 Hong Song et al. All rights reserved. SIVIC: Open-Source, Standards-Based Software for DICOM MR Spectroscopy Workflows Thu, 18 Jul 2013 09:09:54 +0000 Quantitative analysis of magnetic resonance spectroscopic imaging (MRSI) data provides maps of metabolic parameters that show promise for improving medical diagnosis and therapeutic monitoring. While anatomical images are routinely reconstructed on the scanner, formatted using the DICOM standard, and interpreted using PACS workstations, this is not the case for MRSI data. The evaluation of MRSI data is made more complex because files are typically encoded with vendor-specific file formats and there is a lack of standardized tools for reconstruction, processing, and visualization. SIVIC is a flexible open-source software framework and application suite that enables a complete scanner-to-PACS workflow for evaluation and interpretation of MRSI data. It supports conversion of vendor-specific formats into the DICOM MR spectroscopy (MRS) standard, provides modular and extensible reconstruction and analysis pipelines, and provides tools to support the unique visualization requirements associated with such data. Workflows are presented which demonstrate the routine use of SIVIC to support the acquisition, analysis, and delivery to PACS of clinical 1H MRSI datasets at UCSF. Jason C. Crane, Marram P. Olson, and Sarah J. Nelson Copyright © 2013 Jason C. Crane et al. All rights reserved. Acoustic Angiography: A New Imaging Modality for Assessing Microvasculature Architecture Wed, 17 Jul 2013 10:10:05 +0000 The purpose of this paper is to provide the biomedical imaging community with details of a new high resolution contrast imaging approach referred to as “acoustic angiography.” Through the use of dual-frequency ultrasound transducer technology, images acquired with this approach possess both high resolution and a high contrast-to-tissue ratio, which enables the visualization of microvascular architecture without significant contribution from background tissues. Additionally, volumetric vessel-tissue integration can be visualized by using b-mode overlays acquired with the same probe. We present a brief technical overview of how the images are acquired, followed by several examples of images of both healthy and diseased tissue volumes. 3D images from alternate modalities often used in preclinical imaging, contrast-enhanced micro-CT and photoacoustics, are also included to provide a perspective on how acoustic angiography has qualitatively similar capabilities to these other techniques. These preliminary images provide visually compelling evidence to suggest that acoustic angiography may serve as a powerful new tool in preclinical and future clinical imaging. Ryan C. Gessner, C. Brandon Frederick, F. Stuart Foster, and Paul A. Dayton Copyright © 2013 Ryan C. Gessner et al. All rights reserved. Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation Wed, 10 Jul 2013 09:08:23 +0000 This paper presents a novel fuzzy energy minimization method for simultaneous segmentation and bias field estimation of medical images. We first define an objective function based on a localized fuzzy -means (FCM) clustering for the image intensities in a neighborhood around each point. Then, this objective function is integrated with respect to the neighborhood center over the entire image domain to formulate a global fuzzy energy, which depends on membership functions, a bias field that accounts for the intensity inhomogeneity, and the constants that approximate the true intensities of the corresponding tissues. Therefore, segmentation and bias field estimation are simultaneously achieved by minimizing the global fuzzy energy. Besides, to reduce the impact of noise, the proposed algorithm incorporates spatial information into the membership function using the spatial function which is the summation of the membership functions in the neighborhood of each pixel under consideration. Experimental results on synthetic and real images are given to demonstrate the desirable performance of the proposed algorithm. Wenchao Cui, Yi Wang, Yangyu Fan, Yan Feng, and Tao Lei Copyright © 2013 Wenchao Cui et al. All rights reserved.