Biomedical Image Analysis: Rapid prototyping with Mathematica

Digital acquisition techniques have caused an explosion in the production of medical images, especially with the advent of multi-slice CT and volume MRI. One third of the financial investments in a modern hospital's equipment are dedicated to imaging. Emerging screening programs add to this flood of data. The capabilities of many recent computer-aided diagnosis (CAD) programs are compelling, and have recently lead to many new CAD companies. This calls for many new algorithms for image analysis and dedicated scientists for the job.Image analysis software libraries abound, but unfortunately are often limited in functionality, are too specific, or need a rather dedicated environment and have a long learning curve. Today's computer vision algorithms are based on solid mathematics, requiring a highly versatile, high level mathematical prototyping environment. We have chosen Mathematica by Wolfram Research Inc., and describe the successful results of the first 2.5 years of its use in the training of biomedical engineers in image analysis

Combined Classifier versus Combined Feature Space in Scale Space Texture Classification

Using combined classifiers alleviates the problem of generating a large feature space, as the features generated from each scale/derivative are directly fed to a base classifier. In this approach, instead of concatenating features generated from each scale/derivative, the decision made by the base classifiers are combined in a two-stage combined classifier.In this paper, the performance of the proposed classification system is first compared against the combined feature space for only the zeroth order Gaussian derivative at multiple scales. The results clearly show that the proposed system using combined classifiers outperforms the classical approach of the combined feature space. The significance of the parameters, especially the fraction of variance maintained after applying PCA (principal component analysis) is also discussed

Automatic IVUS segmentation of atherosclerotic plaque with Stop & Go snake

Since the upturn of intravascular ultrasound (IVUS)as an imaging technique for the coronary artery system, much research has been done to simplify the complicated analysis of the resulting images. In this study, an attempt to develop an automatic tissue characterization algorithm for IVUS images was done. We concentrated on the segmentation of calcium and soft plaque, because these structures predict the extension and the vulnerability of the atherosclerotic disease, respectively. The first step in the procedure was the extraction of texture features like local binary patterns, co-occurrence matrices and Gabor filter banks. After dimensionality reduction, the resulting feature space was used for classification, constructing a likelihood map to represent different coronary plaques. The information in this map was organized using a recently developed geodesic snake formulation,the so-called Stop & Go snake. The novelty of our study lies in this last step, as it was the first time to apply the Stop & Go snake to segment IVUS images

Extrapolating fiber crossings from DTI data : can we gain the same information as HARDI?

High angular resolution diffusion imaging (HARDI) has proven to better characterize complex intra-voxel structures compared to its predecessor diffusion tensor imaging (DTI). However, the benefits from the modest acquisitions and significantly higher signal-to-noise ratios (SNRs) of DTI make it more attractive for use in clinical research. In this work we use contextual information derived from DTI data, to obtain similar crossing information as from HARDI data. We conduct synthetic phantom validation under different angles of crossing and different SNRs. We corroborate our findings from the phantom study to real human data. We show that with extrapolation of the contextual information the obtained crossings are the same as the ones from the HARDI data, and the robustness to noise is significantly better

Cardiac motion estimation using multi-scale feature points

Heart illnesses influence the functioning of the cardiac muscle and are the major causes of death inthe world. Optic flow methods are essential tools to assess and quantify the contraction of the cardiacwalls, but are hampered by the aperture problem. Harmonic phase (HARP) techniques measure thephase in magnetic resonance (MR) tagged images. Due to the regular geometry, patterns generated bya combination of HARPs and sine HARPs represent a suitable framework to extract landmark features.In this paper we introduce a new aperture-problem free method to study the cardiac motion by trackingmulti-scale features such as maxima, minima, saddles and corners, on HARP and sine HARP taggedimages

Cardiac left atrium CT image segmentation for ablation guidance

Catheter ablation is an increasingly important curative procedure for atrial fibrillation. Knowledge of the local wall thickness is essential to determine the proper ablation energy. This paper presents the first semi-automatic atrial wall thickness measurement method for ablation guidance. It includes both endocardial and epicardial atrial wall segmentation on CT image data. Segmentation is based on active contours, Otsu's multiple threshold method and hysteresis thresholding. Segmentation results were compared to contours manually drawn by two experts, using repeated measures analysis of variance. The root mean square differences between the semi-automatic and the manually drawn contours were comparable to intra-observer variation (endocardium: p = 0.23, epicardium: p = 0.18). Mean wall thickness difference is significant between one of the experts on one side, and the presented method and the other expert on the other side (

Retinal Artery/Vein Classification via Graph Cut Optimization

In many diseases with a cardiovascular component, the geometry of microvascular blood vessels changes. These changes are specific to arteries and veins, and can be studied in the microvasculature of the retina using retinal photography. To facilitate large-scale studies of artery/vein-specific changes in the retinal vasculature, automated classification of the vessels is required. Here we present a novel method for artery/vein classification based on local and contextual feature analysis of retinal vessels. For each vessel, local information in the form of a transverse intensity profile is extracted. Crossings and bifurcations of vessels provide contextual information. The local and contextual features are integrated into a non-submodular energy function, which is optimized exactly using graph cuts. The method was validated on a ground truth data set of 150 retinal fundus images, achieving an accuracy of 88.0% for all vessels and 94.0% for the six arteries and six veins with highest caliber in the image

Infrastructure for Retinal Image Analysis

This paper introduces a retinal image analysis infrastructure for the automatic assessment of biomarkers related to early signs of diabetes, hypertension and other systemic diseases. The developed application provides several tools, namely normalization, vessel enhancement and segmentation, optic disc and fovea detection, junction detection, bifurcation/crossing discrimination, artery/vein classification and red lesion detection. The pipeline of these methods allows the assessment of important biomarkers characterizing dynamic properties of retinal vessels, such as tortuosity, width, fractal dimension and bifurcation geometry features

Linear scale-space: II. early visual operations

This seminal book is a primer on geometry-driven, nonlinear diffusion as a promising new paradigm for vision, with an emphasis on the tutorial. It gives a thorough overview of current linear and nonlinear scale-space theory, presenting many viewpoints such as the variational approach, curve evolution and nonlinear diffusion equations. The book is meant for computer vision scientists and students, with a computer science, mathematics or physics background. Appendices explain the terminology. Many illustrated applications are given, e.g. in medical imaging, vector valued (or coupled) diffusion, general image enhancement (e.g. edge preserving noise suppression) and modeling of the human front-end visual system. Some examples are given to implement the methods in modern computer-algebra systems. From the Preface by Jan J. Koenderink: ` I have read through the manuscript of this book in fascination. Most of the approaches that have been explored to tweak scale-space into practical tools are represented here. It is easy to appreciate how both the purist and the engineer find problems of great interest in this area. The book is certainly unique in its scope and has appeared at a time where this field is booming and newcomers can still potentially leave their imprint on the core corpus of scale related methods that still slowly emerge. As such the book is a very timely one. It is quite evident that it would be out of the question to compile anything like a textbook at this stage: this book is a snapshot of the field that manages to capture its current state very well and in a most lively fashion. I can heartily recommend its reading to anyone interested in the issues of image structure, scale and resolution.

Numerical analysis of geometry-driven diffusion equations

This seminal book is a primer on geometry-driven, nonlinear diffusion as a promising new paradigm for vision, with an emphasis on the tutorial. It gives a thorough overview of current linear and nonlinear scale-space theory, presenting many viewpoints such as the variational approach, curve evolution and nonlinear diffusion equations. The book is meant for computer vision scientists and students, with a computer science, mathematics or physics background. Appendices explain the terminology. Many illustrated applications are given, e.g. in medical imaging, vector valued (or coupled) diffusion, general image enhancement (e.g. edge preserving noise suppression) and modeling of the human front-end visual system. Some examples are given to implement the methods in modern computer-algebra systems