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IEEE Transactions on Medical Imaging

Siyamalan Manivannan, Caroline Cobb, Stephen Burgess, Emanuele Trucco
We propose a novel multiple instance learning method to assess the visibility (visible/not visible) of the retinal nerve fiber layer (RNFL) in fundus camera images. Using only image-level labels, our approach learns to classify the images as well as to localize the RNFL visible regions. We transform the original feature space to a discriminative subspace, and learn a region-level classifier in that subspace. We propose a margin-based loss function to jointly learn this subspace and the region-level classifier...
January 16, 2017: IEEE Transactions on Medical Imaging
Saiprasad Ravishankar, Brian Moore, Raj Nadakuditi, Jeffrey Fessler
Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery from undersampled measurements. In the context of inverse problems in dynamic imaging, recent research has demonstrated the promise of sparsity and low-rank techniques. For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements...
January 10, 2017: IEEE Transactions on Medical Imaging
Renaud Hedouin, Olivier Commowick, Elise Bannier, Benoit Scherrer, Maxime Taquet, Simon Warfield, Christian Barillot
By shortening the acquisition time of MRI, Echo Planar Imaging (EPI) enables the acqui- sition of a large number of images in a short time, com- patible with clinical constraints as required for diffusion or functional MRI. However such images are subject to large, local distortions disrupting their correspondence with the underlying anatomy. The correction of those distortions is an open problem, especially in regions where large deformations occur. We propose a new block-matching registration method to perform EPI distortion correction based on the acquisition of two EPI with opposite phase encoding directions (PED)...
January 9, 2017: IEEE Transactions on Medical Imaging
Hsin-Hon Lin, Shin-Lei Peng, Jay Wu, Tian-Yu Shih, Keh-Shih Chuang, Cheng-Ting Shih
Osteoporosis is a disease characterized by a degradation of bone structures. Various methods have been developed to diagnose osteoporosis by measuring bone mineral density (BMD) of patients. However, BMDs from these methods were not equivalent and were incomparable. In addition, partial volume effect introduces errors in estimating bone volume from computed tomography (CT) images using image segmentation. In this study, a two-compartment model (TCM) was proposed to calculate bone volume fraction (BV/TV) and BMD from CT images...
December 30, 2016: IEEE Transactions on Medical Imaging
Daniele Ancora, Athanasios Zacharopoulos, Jorge Ripoll, Giannis Zacharakis
Diffuse Optical Tomography commonly neglects or assumes as insignificant the presence of optically clear regions in biological tissues, estimating their contribution as a small pertur-bation to light transport. The inaccuracy introduced by this prac-tice is examined in detail in the context of a complete, based on realistic geometry, virtual fluorescence Diffuse Optical Tomogra-phy experiment where a mouse head is imaged in the presence of cerebral spinal fluid. Despite the small thickness of such layer, we point out that an error is introduced when neglecting it from the model with possibly reduction in the accuracy of the reconstruc-tion and localization of the fluorescence distribution within the brain...
December 29, 2016: IEEE Transactions on Medical Imaging
Jonathan Suever, Gregory Wehner, Linyuan Jing, David Powell, Sean Hamlet, Jonathan Grabau, Dimitri Mojsejenko, Kristin Andres, Christopher Haggerty, Brandon Fornwalt
Mechanics of the left ventricle (LV) are important indicators of cardiac function. The role of right ventricular (RV) mechanics is largely unknown due to the technical limitations of imaging its thin wall and complex geometry and motion. By combining 3D Displacement Encoding with Stimulated Echoes (DENSE) with a post-processing pipeline that includes a local coordinate system, it is possible to quantify RV strain, torsion, and synchrony. In this study, we sought to characterize RV mechanics in 50 healthy individuals and compare these values to their LV counterparts...
December 29, 2016: IEEE Transactions on Medical Imaging
Ziba Gandomkar, Kevin Tay, William Ryder, Patrick C Brennan, Claudia Mello-Thoms
This study introduces an individualized tool for identifying mammogram interpretation errors, called eye-Computer Assisted Perception (iCAP). iCAP consists of two modules, one which processes areas marked by radiologists as suspicious for cancer and classifies these as False Positive (FP) or True Positive (TP) decisions, while the second module classifies fixated but not marked locations as False Negative (FN) or True-Negative (TN) decisions. iCAP relies on both radiologists' gaze-related parameters, extracted from eye tracking data, and image-based features...
December 28, 2016: IEEE Transactions on Medical Imaging
Gabriel Ramos-Llorden, Arnold Jan den Dekker, Jan Sijbers
An important factor influencing the quality of magnetic resonance (MR) images is the reconstruction method that is employed, and specifically, the type of prior knowledge that is exploited during reconstruction. In this work, we introduce a new type of prior knowledge, partial discreteness, where a small number of regions in the image are assumed to be homogeneous and can be well represented by a constant magnitude. In particular, we mathematically formalize the partial discreteness property based on a Gaussian Mixture Model (GMM) and derive a partial discreteness image representation that characterizes the salient features of partially discrete images: a constant intensity in homogeneous areas and texture in heterogeneous areas...
December 23, 2016: IEEE Transactions on Medical Imaging
Jing Liu, Qiong He, Jianwen Luo
A novel beamforming technique, named compressed sensing based synthetic transmit aperture (CS-STA) is proposed to speed up the acquisition of ultrasound imaging. This technique consists of three steps. First, the ultrasound transducer transmits randomly apodized plane waves for a number of times and receives the backscattered echoes. Second, the recorded backscattered echoes are used to recover the full channel dataset of synthetic transmit aperture (STA) with a compressed sensing (CS) reconstruction algorithm...
December 23, 2016: IEEE Transactions on Medical Imaging
Xin Li, Abhinav Jha, Michael Ghaly, Fatma Elshahaby, Jonathan Links, Eric Frey
The Hotelling Observer (HO) is widely used to evaluate image quality in medical imaging. However, applying it to data that are not multivariate-normally (MVN) distributed is not optimal. In this paper, we apply two multi-template linear observer strategies to handle such data. First, the entire data ensemble is divided into sub-ensembles that are exactly or approximately MVN and homoscedastic. Next, a different linear observer template is estimated for and applied to each sub-ensemble. The first multi-template strategy, adapted from previous work, applies the HO to each sub-ensemble, calculates the area under the receiver operating characteristics curve (AUC) for each sub-ensemble, and averages the AUCs from all the sub-ensembles...
December 22, 2016: IEEE Transactions on Medical Imaging
Wu Qiu, Yimin Chen, Jessica Kishimoto, Sandrine Ribaupierre, Bernard Chiu, Aaron Fenster, Bijoy Menon, Jing Yuan
Preterm neonates with a very low birth weight of less than 1,500 grams are at increased risk for developing intraventricular hemorrhage (IVH), which is a major cause of brain injury in preterm neonates. Quantitative measurements of ventricular dilatation or shrinkage play an important role in monitoring patients and evaluating treatment options. 3D ultrasound (US) has been developed to monitor ventricle volume as a biomarker for ventricular changes. However, ventricle volume as a global indicator does not allow for precise analysis of local ventricular changes, which could be linked to specific neurological problems often seen in the patient population later in life...
December 22, 2016: IEEE Transactions on Medical Imaging
Costas Arvanitis, Calum Crake, Nathan McDannold, Gregory Clement
In the present proof of principle study, we evaluated the homogenous angular spectrum method for passive acoustic mapping (AS-PAM) of microbubble oscillations using simulated and experimental data. In the simulated data we assessed the ability of AS-PAM to form 3D maps of a single and multiple point sources. Then, in the two dimensional limit, we compared the 2D maps from AS-PAM with alternative frequency and time domain passive acoustic mapping (FD- and TD-PAM) approaches. Finally, we assessed the ability of AS-PAM to visualize microbubble activity in vivo with data obtained during 8 different experiments of FUS-induced blood-brain barrier disruption in 3 nonhuman primates, using a clinical MR-guided FUS system...
December 21, 2016: IEEE Transactions on Medical Imaging
Lequan Yu, Hao Chen, Qi Dou, Jing Qin, Pheng Ann Heng
Automated melanoma recognition in dermoscopy images is a very challenging task due to the low contrast of skin lesions, the huge intraclass variation of melanomas, the high degree of visual similarity between melanoma and non-melanoma lesions, and the existence of many artifacts in the image. In order to meet these challenges, we propose a novel method for melanoma recognition by leveraging very deep convolutional neural networks (CNNs). Compared with existing methods employing either low-level hand-crafted features or CNNs with shallower architectures, our substantially deeper networks (more than 50 layers) can acquire richer and more discriminative features for more accurate recognition...
December 21, 2016: IEEE Transactions on Medical Imaging
Dmytro Shulga, Oleksii Morozov, Patrick Hunziker
Optical Diffusion Tomography (ODT) is a modern non-invasive medical imaging modality which requires mathematical modelling of near-infrared light propagation in tissue. Solving the ODT forward problem equation accurately and efficiently is crucial. Typically, the forward problem is represented by a Diffusion PDE and is solved using the Finite Element Method (FEM) on a mesh, which is often unstructured. Tensor B-spline signal processing has the attractive features of excellent interpolation and approximation properties, multiscale properties, fast algorithms and does not require meshing...
December 19, 2016: IEEE Transactions on Medical Imaging
Engin Turetken, Xinchao Wang, Carlos J Becker, Carsten Haubold, Pascal Fua
e propose a novel approach to automatically tracking elliptical cell populations in time-lapse image sequences. Given an initial segmentation, we account for partial occlusions and overlaps by generating an over-complete set of competing detection hypotheses. To this end, we fit ellipses to portions of the initial regions and build a hierarchy of ellipses, which are then treated as cell candidates. We then select temporally consistent ones by solving to optimality an integer program with only one type of flow variables...
December 15, 2016: IEEE Transactions on Medical Imaging
Aria Pezeshk, Nicholas Petrick, Weijie Chen, Berkman Sahiner
The performance of a classifier is largely dependent on the size and representativeness of data used for its training. In circumstances where accumulation and/or labeling of training samples is difficult or expensive, such as medical applications, data augmentation can potentially be used to alleviate the limitations of small datasets. We have previously developed an image blending tool that allows users to modify or supplement an existing CT or mammography dataset by seamlessly inserting a lesion extracted from a source image into a target image...
December 14, 2016: IEEE Transactions on Medical Imaging
Ping Gong, Pengfei Song, Shigao Chen
The development of ultrafast ultrasound imaging brings great opportunities to improve imaging technologies such as shear wave elastography and ultrafast Doppler imaging. In ultrafast imaging, several tilted plane or diverging wave images are coherently combined to form a compounded image, leading to trade-offs among image signal-to-noise ratio (SNR), resolution, and post-compounded frame rate. Multiplane wave (MW) imaging is proposed to solve this trade-off by encoding multiple plane waves with Hadamard matrix during one transmission event (i...
December 12, 2016: IEEE Transactions on Medical Imaging
Gaia Rizzo, Matteo Tonietto, Marco Castellaro, Bernd Raffeiner, Alessandro Coran, Ugo Fiocco, Roberto Stramare, Enrico Grisan
Contrast Enhanced Ultrasound (CEUS) is a sensitive imaging technique to assess tissue vascularity and it can be particularly useful in early detection and grading of arthritis. In a recent study we have shown that a Gamma-variate can accurately quantify synovial perfusion and it is flexible enough to describe many heterogeneous patterns. However, in some cases the heterogeneity of the kinetics can be such that even the Gamma model does not properly describe the curve, with a high number of outliers.
December 8, 2016: IEEE Transactions on Medical Imaging
Yongjian Yu, Jue Wang
We present a fast enclosure transform (ET) to localize complex objects of interest from speckle imagery. This approach explores the spatial confinement on regional features from a sparse image feature representation. Unrelated, broken ridge features surrounding an object are organized collaboratively, giving rise to the enclosureness of the object. Three enclosure likelihood measures are constructed, consisting of the enclosure force, potential energy, and encloser count. In the transform domain, the local maxima manifest the locations of objects of interest, for which only the intrinsic dimension is known a priori...
December 7, 2016: IEEE Transactions on Medical Imaging
Feng-Ying Xie, Haidi Fan, Li Yang, Zhi-Guo Jiang, Ru-Song Meng, Alan Bovik
We develop a novel method for classifying melanocytic tumors as benign or malignant by the analysis of digital dermoscopy images. The algorithm follows three steps: first, lesions are extracted using a self-generating neural network (SGNN); second, features descriptive of tumor color, texture and border are extracted; and third, lesion objects are classified using a classifier based on a neural network ensemble model. In clinical situations, lesions occur that are too large to be entirely contained within the dermoscopy image...
December 1, 2016: IEEE Transactions on Medical Imaging
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