oalib

Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99

Submit

Any time

2020 ( 3 )

2019 ( 49 )

2018 ( 58 )

2017 ( 38 )

Custom range...

Search Results: 1 - 10 of 21992 matches for " U. Meyer-Baese "
All listed articles are free for downloading (OA Articles)
Page 1 /21992
Display every page Item
Selection of Spatiotemporal Features in Breast MRI to Differentiate between Malignant and Benign Small Lesions Using Computer-Aided Diagnosis
F. Steinbruecker,A. Meyer-Baese,C. Plant,T. Schlossbauer,U. Meyer-Baese
Advances in Artificial Neural Systems , 2012, DOI: 10.1155/2012/919281
Abstract: Automated detection and diagnosis of small lesions in breast MRI represents a challenge for the traditional computer-aided diagnosis (CAD) systems. The goal of the present research was to compare and determine the optimal feature sets describing the morphology and the enhancement kinetic features for a set of small lesions and to determine their diagnostic performance. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal feature number and tested different classification techniques. The results suggest that the computerized analysis system based on spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography. 1. Introduction Breastcancer is one of the most common cancers among women. Contrast-enhanced MR imaging of the breast was reported to be a highly sensitive method for the detection of invasive breast cancer [1]. Different investigators described that certain dynamic signal intensity (SI) characteristics (rapid and intense contrast enhancement followed by a wash out phase) obtained in dynamic studies are a strong indicator for malignancy [2]. Morphologic criteria have also been identified as valuable diagnostic tools [3]. Recently, combinations of different dynamic and morphologic characteristics have been reported [4] that can reach diagnostic sensitivities up to 97 and specificities up to 76.5 . As an important aspect remains the fact that many of these techniques were applied on a database of predominantly tumors of a size larger than 2?cm. In these cases, MRI reaches a very high sensitivity in the detection of invasive breast cancer due to both morphological criteria as well as characteristic time-signal intensity curves. However, the value of dynamic MRI and of automatic identification and classification of characteristic kinetic curves is not well established in small lesions when clinical findings, mammography, and ultrasound are unclear. Recent clinical research has shown that DCIS with small invasive carcinoma can be adequately visualized in MRI [5] and that MRI provides an accurate estimation of invasive breast cancer tumor size, especially in tumors of 2?cm or smaller [6]. Visual assessment of
Evaluation of a Nonrigid Motion Compensation Technique Based on Spatiotemporal Features for Small Lesion Detection in Breast MRI
F. Steinbruecker,A. Meyer-Baese,T. Schlossbauer,D. Cremers
Advances in Artificial Neural Systems , 2012, DOI: 10.1155/2012/808602
Abstract: Motion-induced artifacts represent a major problem in detection and diagnosis of breast cancer in dynamic contrast-enhanced magnetic resonance imaging. The goal of this paper is to evaluate the performance of a new nonrigid motion correction algorithm based on the optical flow method. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set under consideration of several 2D or 3D motion compensation parameters for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal motion compensation parameters. Our results have shown that motion compensation can improve the classification results. The results suggest that the computerized analysis system based on the non-rigid motion compensation technique and spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography. 1. Introduction Breast cancer is one of the leading death cases among women in the US. MR technology has advanced tremendously and became a highly sensitive method for the detection of invasive breast cancer [1]. While only dynamic signal intensity characteristics are integrated in today's CAD systems leaving morphological features to the interpretation of the radiologist, an automated diagnosis based on a combination of both should be strived for. Clinical studies have shown that combinations of different dynamic and morphologic characteristics [2] achieve diagnostic sensitivities up to 97 % and specificities up to 76.5 % . However, most of these techniques have not been applied to small enhancing foci having a size of less than 1?cm. These diagnostically challenging cases found unclear ultrasound or mammography indications can be adequately visualized in magnetic resonance imaging (MRI) [3] with MRI providing an accurate estimation of invasive breast cancer tumor size [4]. Automatic motion correction represents an important prerequisite to a correct automated small lesion evaluation [5]. Motion artifacts are caused either by the relaxation of the pectoral muscle or involuntary patient motion and invalidate the assumption of same spatial location within the breast of the corresponding voxels in the acquired volumes for assessing lesion enhancement. Especially for small lesions, the assumption of correct spatial alignment often
Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRI
A. Meyer-Baese,T. Schlossbauer,O. Lange,A. Wismueller
International Journal of Biomedical Imaging , 2009, DOI: 10.1155/2009/326924
Abstract: An application of an unsupervised neural network-based computer-aided diagnosis (CAD) system is reported for the detection and characterization of small indeterminate breast lesions, average size 1.1 mm, in dynamic contrast-enhanced MRI. This system enables the extraction of spatial and temporal features of dynamic MRI data and additionally provides a segmentation with regard to identification and regional subclassification of pathological breast tissue lesions. Lesions with an initial contrast enhancement ≥50% were selected with semiautomatic segmentation. This conventional segmentation analysis is based on the mean initial signal increase and postinitial course of all voxels included in the lesion. In this paper, we compare the conventional segmentation analysis with unsupervised classification for the evaluation of signal intensity time courses for the differential diagnosis of enhancing lesions in breast MRI. The results suggest that the computerized analysis system based on unsupervised clustering has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography.
A new supervised non-linear mapping
Sylvain Lespinats,Anke Meyer-Baese,Michael Aupetit
Computer Science , 2012,
Abstract: Supervised mapping methods project multi-dimensional labeled data onto a 2-dimensional space attempting to preserve both data similarities and topology of classes. Supervised mappings are expected to help the user to understand the underlying original class structure and to classify new data visually. Several methods have been designed to achieve supervised mapping, but many of them modify original distances prior to the mapping so that original data similarities are corrupted and even overlapping classes tend to be separated onto the map ignoring their original topology. We propose ClassiMap, an alternative method for supervised mapping. Mappings come with distortions which can be split between tears (close points mapped far apart) and false neighborhoods (points far apart mapped as neighbors). Some mapping methods favor the former while others favor the latter. ClassiMap switches between such mapping methods so that tears tend to appear between classes and false neighborhood within classes, better preserving classes' topology. We also propose two new objective criteria instead of the usual subjective visual inspection to perform fair comparisons of supervised mapping methods. ClassiMap appears to be the best supervised mapping method according to these criteria in our experiments on synthetic and real datasets.
FPGA-Based Multimodal Embedded Sensor System Integrating Low- and Mid-Level Vision
Guillermo Botella,José Antonio Martín H.,Matilde Santos,Uwe Meyer-Baese
Sensors , 2011, DOI: 10.3390/s110808164
Abstract: Motion estimation is a low-level vision task that is especially relevant due to its wide range of applications in the real world. Many of the best motion estimation algorithms include some of the features that are found in mammalians, which would demand huge computational resources and therefore are not usually available in real-time. In this paper we present a novel bioinspired sensor based on the synergy between optical flow and orthogonal variant moments. The bioinspired sensor has been designed for Very Large Scale Integration (VLSI) using properties of the mammalian cortical motion pathway. This sensor combines low-level primitives (optical flow and image moments) in order to produce a mid-level vision abstraction layer. The results are described trough experiments showing the validity of the proposed system and an analysis of the computational resources and performance of the applied algorithms.
Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
Anke Meyer-Baese,Sylvain Lespinats,Juan Manuel Gorriz Saez,Olivier Bastien
Advances in Artificial Neural Systems , 2012, DOI: 10.1155/2012/219860
Abstract:
Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
Anke Meyer-Baese,Sylvain Lespinats,Juan Manuel Gorriz Saez,Olivier Bastien
Advances in Artificial Neural Systems , 2012, DOI: 10.1155/2012/219860
Abstract:
Space-Efficient Simulation of Quantum Computers
Michael P. Frank,Uwe H. Meyer-Baese,Irinel Chiorescu,Liviu Oniciuc,Robert A. van Engelen
Physics , 2008, DOI: 10.1145/1566445.1566554
Abstract: Traditional algorithms for simulating quantum computers on classical ones require an exponentially large amount of memory, and so typically cannot simulate general quantum circuits with more than about 30 or so qubits on a typical PC-scale platform with only a few gigabytes of main memory. However, more memory-efficient simulations are possible, requiring only polynomial or even linear space in the size of the quantum circuit being simulated. In this paper, we describe one such technique, which was recently implemented at FSU in the form of a C++ program called SEQCSim, which we releasing publicly. We also discuss the potential benefits of this simulation in quantum computing research and education, and outline some possible directions for further progress.
A space-efficient quantum computer simulator suitable for high-speed FPGA implementation
Michael P. Frank,Liviu Oniciuc,Uwe Meyer-Baese,Irinel Chiorescu
Physics , 2009, DOI: 10.1117/12.817924
Abstract: Conventional vector-based simulators for quantum computers are quite limited in the size of the quantum circuits they can handle, due to the worst-case exponential growth of even sparse representations of the full quantum state vector as a function of the number of quantum operations applied. However, this exponential-space requirement can be avoided by using general space-time tradeoffs long known to complexity theorists, which can be appropriately optimized for this particular problem in a way that also illustrates some interesting reformulations of quantum mechanics. In this paper, we describe the design and empirical space-time complexity measurements of a working software prototype of a quantum computer simulator that avoids excessive space requirements. Due to its space-efficiency, this design is well-suited to embedding in single-chip environments, permitting especially fast execution that avoids access latencies to main memory. We plan to prototype our design on a standard FPGA development board.
Measuring Non-Gaussianity by Phi-Transformed and Fuzzy Histograms
Claudia Plant,Son Mai Thai,Junming Shao,Fabian J. Theis,Anke Meyer-Baese,Christian B?hm
Advances in Artificial Neural Systems , 2012, DOI: 10.1155/2012/962105
Abstract: Independent component analysis (ICA) is an essential building block for data analysis in many applications. Selecting the truly meaningful components from the result of an ICA algorithm, or comparing the results of different algorithms, however, is nontrivial problems. We introduce a very general technique for evaluating ICA results rooted in information-theoretic model selection. The basic idea is to exploit the natural link between non-Gaussianity and data compression: the better the data transformation represented by one or several ICs improves the effectiveness of data compression, the higher is the relevance of the ICs. We propose two different methods which allow an efficient data compression of non-Gaussian signals: Phi-transformed histograms and fuzzy histograms. In an extensive experimental evaluation, we demonstrate that our novel information-theoretic measures robustly select non-Gaussian components from data in a fully automatic way, that is, without requiring any restrictive assumptions or thresholds. 1. Introduction Independent component analysis (ICA) is a powerful technique for signal demixing and data analysis in numerous applications. For example, in neuroscience, ICA is essential for the analysis of functional magnetic resonance imaging (fMRI) data and electroencephalograms (EEGs). The function of the human brain is very complex and can be only imaged at a very coarse spatial resolution. Millions of nerve cells are contained in a single voxel of fMRI data. The neural activity is indirectly measured by the so-called BOLD-effect, that is, by the increased supply of active regions with oxygenated blood. In EEG, the brain function can be directly measured by the voltage fluctuations resulting from ionic current flows within the neurons. The spacial resolution of EEG, however, is even much lower than that of fMRI. Usually, an EEG is recorded using an array of 64 electrodes distributed over the scalp. Often, the purpose of acquiring fMRI or EEG data is obtaing a better understanding of brain function while the subject is performing some task. An example for such an experiment is to show subjects images while they are in the scanner to study the processing of visual stimuli, see Section 4.1.4. Recent results in neuroscience, for example [1], confirm the organization of the human brain into distinct functional modules. During task processing, some functional modules are actively contributing to the task. However, many other modules are also active but not involved into task-specific activities. Due to the low resolution of fMRI and EEG data,
Page 1 /21992
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.