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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
Hamed Rabiei, Frederic Richard, Olivier Coulon, Julien Lefevre
Gyrification index (GI) is an appropriate measure to quantify the complexity of the cerebral cortex. There is, however, no universal agreement on the notion of surface complexity and there are various methods in literature that evaluate different aspects of cortical folding. In this paper, we give two intuitive interpretations on folding quantification based on the magnitude and variation of the mean curvature of the cortical surface. We then present a local spectral analysis of the mean curvature to introduce two local gyrification indices that satisfy our interpretations...
November 29, 2016: IEEE Transactions on Medical Imaging
Wuhong Lin, Huawang Wu, Yishu Liu, Dongsheng Lv, Lihua Yang
The fMRI signals are usually filtered before processing and analyzing. This process can result in the loss of information carried by the higher frequency in the low frequency fluctuation. ICA and CCA are two classical methods in fMRI. ICA finds the statistically independent components of the observed data, however these components are usually physiologically uninterpretable without auxiliary procedures. CCA decomposes two sets of data into component pairs in some order, however these components may be mixtures of real signals and noise...
November 21, 2016: IEEE Transactions on Medical Imaging
Leonardo Urbano, Puneet Masson, Matthew VerMilyea, Moshe Kam
We present a fully automated multi-sperm tracking algorithm. It has the demonstrated capability to detect and track simultaneously hundreds of sperm cells in recorded videos while accurately measuring motility parameters over time and with minimal operator intervention. Algorithms of this kind may help in associating dynamic swimming parameters of human sperm cells with fertility and fertilization rates. Specifically, we offer an image processing method, based on radar tracking algorithms, that detects and tracks automatically the swimming paths of human sperm cells in timelapse microscopy image sequences of the kind that is analyzed by fertility clinics...
November 18, 2016: IEEE Transactions on Medical Imaging
Ruud van Sloun, Libertario Demi, Arnoud Postema, Jean de la Rosette, Hessel Wijkstra, Massimo Mischi
Prostate cancer care can benefit from accurate and cost-efficient imaging modalities that are able to reveal prognostic indicators for cancer. Angiogenesis is known to play a central role in the growth of tumors towards a metastatic or a lethal phenotype.With the aim of localizing angiogenic activity in a noninvasive manner, Dynamic Contrast Enhanced Ultrasound (DCEUS) has been widely used. Usually, the passage of ultrasound contrast agents thought the organ of interest is analyzed for the assessment of tissue perfusion...
November 16, 2016: IEEE Transactions on Medical Imaging
Sihong Chen, Jing Qin, Xing Ji, Baiying Lei, Tianfu Wang, Dong Ni, Jie-Zhi Cheng
The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images...
November 16, 2016: IEEE Transactions on Medical Imaging
Assaf Hoogi, Arjun Subramaniam, Rishi Veerapaneni, Daniel Rubin
In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process...
November 11, 2016: IEEE Transactions on Medical Imaging
Lin Fu, Tzu-Cheng Lee, Soo Mee Kim, Adam Alessio, Paul Kinahan, Zhiqian Chang, Ken Sauer, Mannudeep Kalra, Bruno De Man
X-ray detectors in clinical computed tomography (CT) usually operate in current-integrating mode. Their complicated signal statistics often lead to intractable likelihood functions for practical use in model-based image reconstruction (MBIR). It is therefore desirable to design simplified statistical models without losing the essential factors. Depending on whether the CT transmission data are logarithmically transformed, pre-log and post-log models are two major categories of choices in CT MBIR. Both being approximations, it remains an open question whether one model can notably improve image quality over the other on real scanners...
November 9, 2016: IEEE Transactions on Medical Imaging
Martin Rajchl, Matthew Lee, Ozan Oktay, Konstantinos Kamnitsas, Jonathan Passerat-Palmbach, Wenjia Bai, Mary Rutherford, Joseph Hajnal, Bernhard Kainz, Daniel Rueckert
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. It extends the approach of the well-known GrabCut [1] method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations...
November 9, 2016: IEEE Transactions on Medical Imaging
Matthieu Le, Herve Delingette, Jayashree Kalpathy-Cramer, Elizabeth R Gerstner, Tracy Batchelor, Jan Unkelbach, Nicholas Ayache
In this article, we propose a proof of concept for the automatic planning of personalized radiotherapy for brain tumors. A computational model of glioblastoma growth is combined with an exponential cell survival model to describe the effect of radiotherapy. The model is personalized to the magnetic resonance images (MRIs) of a given patient. It takes into account the uncertainty in the model parameters, together with the uncertainty in the MRI segmentations. The computed probability distribution over tumor cell densities, together with the cell survival model, is used to define the prescription dose distribution, which is the basis for subsequent Intensity Modulated Radiation Therapy (IMRT) planning...
November 8, 2016: IEEE Transactions on Medical Imaging
Pierre Ambrosini, Ihor Smal, Daniel Ruijters, Wiro Niessen, Adriaan Moelker, Theo van Walsum
In minimal invasive image guided catheterization procedures, physicians require information of the catheter position with respect to the patient's vasculature. However, in fluoroscopic images, visualization of the vasculature requires toxic contrast agent. Static vasculature roadmapping, which can reduce the usage of iodine contrast, is hampered by the breathing motion in abdominal catheterization. In this paper, we propose a method to track the catheter tip inside the patient's 3D vessel tree using intra-operative single-plane 2D X-ray image sequences and a peri-operative 3D rotational angiography (3DRA)...
November 7, 2016: IEEE Transactions on Medical Imaging
Ashkan Javaherian, Sean Holman
Inspired by the recent advances on minimizing nonsmooth or bound-constrained convex functions on models using varying degrees of fidelity, we propose a line search multigrid (MG) method for full-wave iterative image reconstruction in photoacoustic tomography (PAT) in heterogeneous media. To compute the search direction at each iteration, we decide between the gradient at the target level, or alternatively an approximate error correction at a coarser level, relying on some predefined criteria. To incorporate absorption and dispersion, we derive the analytical adjoint directly from the first-order acoustic wave system...
November 4, 2016: IEEE Transactions on Medical Imaging
Johannes Vorwerk, Christian Engwer, Sampsa Pursiainen, Carsten H Wolters
Finite element methods have been shown to achieve high accuracies in numerically solving the EEG forward problem and they enable the realistic modeling of complex geometries and important conductive features such as anisotropic conductivities. To date, most of the presented approaches rely on the same underlying formulation, the continuous Galerkin (CG)-FEM. In this article, a novel approach to solve the EEG forward problem based on a mixed finite element method (Mixed-FEM) is introduced. To obtain the Mixed-FEM formulation, the electric current is introduced as an additional unknown besides the electric potential...
November 2, 2016: IEEE Transactions on Medical Imaging
Jeroen van Gemert, Wyger Brink, Andrew Webb, Rob Remis
Interference effects in the transmit B+ 1 field can severely degrade the image quality in high-field Magnetic Resonance Imaging (MRI). High-permittivity pads are increasingly used to counteract these effects, but designing such pads is not trivial. In this paper, we present an efficient solution methodology for this dielectric RF shimming problem. By exploiting the fact that dielectric pads form a low rank perturbation of a largescale background model, we are able to efficiently compute B+ 1 fields that correspond to a wide range of different pad realizations...
November 2, 2016: IEEE Transactions on Medical Imaging
Ryan Cunningham, Peter Harding, Ian Loram
Despite widespread availability of ultrasound and a need for personalised muscle diagnosis (neck/back pain-injury, work related disorder, myopathies, neuropathies), robust, online segmentation of muscles within complex groups remains unsolved by existing methods. For example, Cervical Dystonia (CD) is a prevalent neurological condition causing painful spasticity in one or multiple muscles in the cervical muscle system. Clinicians currently have no method for targeting/monitoring treatment of deep muscles. Automated methods of muscle segmentation would enable clinicians to study, target, and monitor the deep cervical muscles via ultrasound...
November 1, 2016: IEEE Transactions on Medical Imaging
You Zhang, Joubin Nasehi Tehrani, Jing Wang
Two-dimensional-to-three-dimensional (2D-3D) deformation has emerged as a new technique to estimate cone-beam computed tomography (CBCT) images. The technique is based on deforming a prior high-quality 3D CT/CBCT image to form a new CBCT image, guided by limited-view 2D projections. The accuracy of this intensity-based technique, however, is often limited in low-contrast image regions with subtle intensity differences. The solved deformation vector fields (DVFs) can also be biomechanically unrealistic. To address these problems, we have developed a biomechanical modeling guided CBCT estimation technique (Bio-CBCT-est) by combining 2D-3D deformation with finite element analysis (FEA)-based biomechanical modeling of anatomical structures...
November 1, 2016: IEEE Transactions on Medical Imaging
Amir Kiperwas, Daniel Rosenfeld, Yonina C Eldar
We present an algorithm for resampling a function from its values on a non-Cartesian grid onto a Cartesian grid. This problem arises in many applications such as MRI, CT, radio astronomy and geophysics. Our algorithm, termed SParse Uniform ReSampling (SPURS), employs methods from modern sampling theory to achieve a small approximation error while maintaining low computational cost. The given non-Cartesian samples are projected onto a selected intermediate subspace, spanned by integer translations of a compactly supported kernel function...
November 1, 2016: IEEE Transactions on Medical Imaging
Clement Papadacci, Ethan A Bunting, Elaine Y Wan, Pierre Nauleau, Elisa E Konofagou
Strain evaluation is of major interest in clinical cardiology as it can quantify the cardiac function. Myocardial elastography, a radio-frequency (RF)-based cross-correlation method, has been developed to evaluate the local strain distribution in the heart in vivo. However, inhomogeneities such as RF ablation lesions or infarction require a three-dimensional approach to be measured accurately. In addition, acquisitions at high volume rate are essential to evaluate the cardiac strain in three dimensions. Conventional focused transmit schemes using 2D matrix arrays, trade off sufficient volume rate for beam density or sector size to image rapid moving structure such as the heart, which lowers accuracy and precision in the strain estimation...
November 1, 2016: IEEE Transactions on Medical Imaging
Loic Le Folgoc, Herve Delingette, Antonio Criminisi, Nicholas Ayache
We investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the tuning of registration hyperparameters, such as the trade-off between the data and regularization functionals. Sparsity-inducing priors have recently been used to render the parametrization itself adaptive and data-driven. The sparse prior on transformation parameters effectively favors the use of coarse basis functions to capture the global trends in the visible motion while finer, highly localized bases are introduced only in the presence of coherent image information and motion...
November 1, 2016: IEEE Transactions on Medical Imaging
Lin Gu, Xiaowei Zhang, He Zhao, Huiqi Li, Li Cheng
We focus on the challenging problem of filamentary structure segmentation in both 2D and 3D images, including retinal vessels and neurons, among others. Despite the increasing amount of efforts in learning based methods to tackle this problem, there still lack proper data-driven feature construction mechanisms to sufficiently encode contextual labelling information, which might hinder the segmentation performance. This observation prompts us to propose a data-driven approach to learn structured and contextual features in this paper...
October 31, 2016: IEEE Transactions on Medical Imaging
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