Guowei  Wei

Recent Advances in Mathematical Methods for the Analysis of Biomedical Image

In brain mapping studies of sensory, cognitive, and motor operations, specific waveforms of dynamic neural activity are predicted based on theoretical models of human information processing. For example in event-related functional MRI (fMRI), the general linear model (GLM) is employed in mass-univariate analyses to identify the regions whose dynamic activity closely matches the expected waveforms. By comparison multivariate analyses based on PCA or ICA provide greater flexibility in detecting spatiotemporal properties of experimental data that may strongly support alternative neuroscientific explanations. We investigated conjoint multivariate and mass-univariate analyses that combine the capabilities to (1) verify activation of neural machinery we already understand and (2) discover reliable signatures of new neural machinery. We examined combinations of GLM and PCA that recover latent neural signals (waveforms and footprints) with greater accuracy than either method alone. Comparative results are illustrated with analyses of real fMRI data, adding to Monte Carlo simulation support.

James R. Moeller

Christian G. Habeck

Reciprocal Benefits of Mass-Univariate and Multivariate Modeling in Brain Mapping: Applications to Event-Related Functional MRI, H215O-, and FDG-PET

Important attributes of 3D brain cortex segmentation algorithms include robustness, accuracy, computational efficiency, and facilitation of user interaction, yet few algorithms incorporate all of these traits. Manual segmentation is highly accurate but tedious and laborious. Most automatic techniques, while less demanding on the user, are much less accurate. It would be useful to employ a fast automatic segmentation procedure to do most of the work but still allow an expert user to interactively guide the segmentation to ensure an accurate final result. We propose a novel 3D brain cortex segmentation procedure utilizing dual-front active contours which minimize image-based energies in a manner that yields flexibly global minimizers based on active regions. Region-based information and boundary-based information may be combined flexibly in the evolution potentials for accurate segmentation results. The resulting scheme is not only more robust but much faster and allows the user to guide the final segmentation through simple mouse clicks which add extra seed points. Due to the flexibly global nature of the dual-front evolution model, single mouse clicks yield corrections to the segmentation that extend far beyond their initial locations, thus minimizing the user effort. Results on 15 simulated and 20 real 3D brain images demonstrate the robustness, accuracy, and speed of our scheme compared with other methods.

Hua Li

Anthony Yezzi

Laurent D. Cohen

3D Brain Segmentation Using Dual-Front Active Contours with Optional User Interaction

Recent advances in neuroimaging techniques have provided precise spatial localization
of brain activation applied in several neuroscience subareas. The development of functional
magnetic resonance imaging (fMRI), based on the BOLD signal, is one of the most popular
techniques related to the detection of neuronal activation. However, understanding the
interactions between several neuronal modules is also an important task, providing a better
comprehension about brain dynamics. Nevertheless, most connectivity studies in fMRI are based
on a simple correlation analysis, which is only an association measure and does not provide the
direction of information flow between brain areas. Other proposed methods like structural
equation modeling (SEM) seem to be attractive alternatives. However, this approach assumes
prior information about the causality direction and stationarity conditions, which may not be
satisfied in fMRI experiments. Generally, the fMRI experiments are related to an activation task;
hence, the stimulus conditions should also be included in the model. In this paper, we suggest an intervention analysis, which includes stimulus condition, allowing a nonstationary modeling. Furthermore, an illustrative application to real fMRI
dataset from a simple motor task is presented.

João Ricardo Sato

Daniel Yasumasa Takahashi

Ellison Fernando Cardoso

Maria da Graça Morais Martin

Edson Amaro Júnior

Pedro Alberto Morettin

Intervention Models in Functional Connectivity Identification Applied to fMRI

We present a tool for tracking coronary vessels in MRI
scans of the human heart to aid in the screening of heart
diseases. The vessels are identified through a single click inside
each vessel present in a standard orthogonal view. The vessel
identification results from a series of computational steps
including eigenvalue analysis of the Hessian of the MRI image
followed by a level set-based extraction of the vessel centerline.
All identified vessels are highlighted using a virtual contrast
agent and displayed simultaneously in a spherical curved
reformation view. In cases of over segmentation, the vessel traces
can be shortened by a click on each vessel end point. Intermediate
analysis results of the vessel computation steps can be displayed
as well. We successfully validated the tool on 40 MRI scans
demonstrating accuracy and significant time savings over manual
vessel tracing.

Ingmar Bitter

Robert Van Uitert

Ivo Wolf

Efstathia Tzatha

Ahmed M Gharib

Ronald Summers

Hans-Peter Meinzer

Roderic Pettigrew

Virtual Contrast for Coronary Vessels Based on Level Set Generated Subvoxel Accurate Centerlines

Intensity inhomogeneity (IIH) is often encountered in MR imaging,and a number of techniques have been devised to correct thisartifact. This paper attempts to review some of the recentdevelopments in the mathematical modeling of IIH field.Low-frequency models are widely used, but they tend to corrupt thelow-frequency components of the tissue. Hypersurface models andstatistical models can be adaptive to the image and generally morestable, but they are also generally more complex and consume morecomputer memory and CPU time. They are often formulated togetherwith image segmentation within one framework and the overallperformance is highly dependent on the segmentation process.Beside these three popular models, this paper also summarizesother techniques based on different principles. In addition, theissue of quantitative evaluation and comparative study arediscussed.

Zujun  Hou

A Review on MR Image Intensity Inhomogeneity Correction

We propose a denoising algorithm for medical images based on a
combination of the total variation minimization scheme and the
wavelet scheme. We show that our scheme offers effective noise
removal in real noisy medical images while maintaining sharpness
of objects. More importantly, this scheme allows us to implement
an effective automatic stopping time criterion.

Yang Wang

Haomin Zhou

Total Variation Wavelet-Based Medical Image Denoising

This paper discusses the assumption of Gaussian noise in the
blood-oxygenation-dependent (BOLD) contrast for functional MRI
(fMRI). In principle, magnitudes in MRI images follow a Rice
distribution. We start by reviewing differences between Rician and
Gaussian noise. An analytic expression is derived for the null
(resting-state) distribution of the difference between two Rician
distributed images. This distribution is shown to be symmetric,
and an exact expression for its standard deviation is derived.
This distribution can be well approximated by a Gaussian, with
very high precision for high SNR, and high precision for lower
SNR. Tests on simulated and real MR images show that subtracting
the time-series mean in fMRI yields asymmetrically distributed
temporal noise. Subtracting a resting-state time series from the
first results in symmetric and nearly Gaussian noise. This has
important consequences for fMRI analyses using standard
statistical tests.

Alle Meije Wink

Jos B. T. M. Roerdink

BOLD Noise Assumptions in fMRI

Breast cancer is a major cause of death and morbidity among women
all over the world, and it is a fact that early detection is a key
in improving outcomes. Therefore development of algorithms that
aids radiologists in identifying changes in breast tissue early on
is essential. In this work an algorithm that investigates the use
of principal components analysis (PCA) is developed to identify
suspicious regions on mammograms. The algorithm employs linear
structure and curvelinear modeling prior to PCA implementations.
Evaluation of the algorithm is based on the percentage of correct
classification, false positive (FP) and false negative (FN) in all
experimental work using real data. Over 90% accuracy in block
classification is achieved using mammograms from MIAS database.

Ikhlas Abdel-Qader

Lixin Shen

Christina Jacobs

Fadi Abu Amara

Sarah Pashaie-Rad

Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA

The study of cell behavior is of crucial importance in drug and
disease research. The fields of bioinformatics and biotechnology
rely on the collection, processing, and analysis of huge numbers
of biocellular images, including cell features such as cell size,
shape, and motility. However manual methods of inferring these
values are so onerous that automated methods of cell tracking and
segmentation are in high demand. In this paper, a novel
model-based cell tracker is designed to locate and track
individual cells. The proposed cell tracker has been successfully
applied to track hematopoietic stem cells (HSCs) based on
identified cell locations and probabilistic data association.

Nezamoddin N. Kachouie

Paul Fieguth

John Ramunas

Eric Jervis

Probabilistic Model-Based Cell Tracking

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International Journal of Biomedical Imaging aims to promote research and development of biomedical imaging by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field.

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International Journal of Biomedical Imaging

Recent Advances in Mathematical Methods for the Analysis of Biomedical Images

Recent Advances in Mathematical Methods for the Analysis of Biomedical Images

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