2,929 research outputs found
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
FUZZY KERNEL REGRESSION FOR REGISTRATION AND OTHER IMAGE WARPING APPLICATIONS
In this dissertation a new approach for non-rigid medical im-
age registration is presented. It relies onto a probabilistic framework
based on the novel concept of Fuzzy Kernel Regression. The theoric
framework, after a formal introduction is applied to develop several
complete registration systems, two of them are interactive and one
is fully automatic. They all use the composition of local deforma-
tions to achieve the final alignment. Automatic one is based onto the
maximization of mutual information to produce local affine aligments
which are merged into the global transformation. Mutual Information
maximization procedure uses gradient descent method. Due to the
huge amount of data associated to medical images, a multi-resolution
topology is embodied, reducing processing time. The distance based
interpolation scheme injected facilitates the similairity measure op-
timization by attenuating the presence of local maxima in the func-
tional. System blocks are implemented on GPGPUs allowing efficient
parallel computation of large 3d datasets using SIMT execution. Due
to the flexibility of Mutual Information, it can be applied to multi-
modality image scans (MRI, CT, PET, etc.).
Both quantitative and qualitative experiments show promising results
and great potential for future extension.
Finally the framework flexibility is shown by means of its succesful
application to the image retargeting issue, methods and results are
presented
Image similarity in medical images
Recent experiments have indicated a strong influence of the substrate grain orientation on the self-ordering in anodic porous alumina. Anodic porous alumina with straight pore channels grown in a stable, self-ordered manner is formed on (001) oriented Al grain, while disordered porous pattern is formed on (101) oriented Al grain with tilted pore channels growing in an unstable manner. In this work, numerical simulation of the pore growth process is carried out to understand this phenomenon. The rate-determining step of the oxide growth is assumed to be the Cabrera-Mott barrier at the oxide/electrolyte (o/e) interface, while the substrate is assumed to determine the ratio β between the ionization and oxidation reactions at the metal/oxide (m/o) interface. By numerically solving the electric field inside a growing porous alumina during anodization, the migration rates of the ions and hence the evolution of the o/e and m/o interfaces are computed. The simulated results show that pore growth is more stable when β is higher. A higher β corresponds to more Al ionized and migrating away from the m/o interface rather than being oxidized, and hence a higher retained O:Al ratio in the oxide. Experimentally measured oxygen content in the self-ordered porous alumina on (001) Al is indeed found to be about 3% higher than that in the disordered alumina on (101) Al, in agreement with the theoretical prediction. The results, therefore, suggest that ionization on (001) Al substrate is relatively easier than on (101) Al, and this leads to the more stable growth of the pore channels on (001) Al
Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review
Radiotherapy aims to deliver a prescribed dose to the tumor while sparing
neighboring organs at risk (OARs). Increasingly complex treatment techniques
such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery
(SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been
developed to deliver doses more precisely to the target. While such
technologies have improved dose delivery, the implementation of intra-fraction
motion management to verify tumor position at the time of treatment has become
increasingly relevant. Recently, artificial intelligence (AI) has demonstrated
great potential for real-time tracking of tumors during treatment. However,
AI-based motion management faces several challenges including bias in training
data, poor transparency, difficult data collection, complex workflows and
quality assurance, and limited sample sizes. This review serves to present the
AI algorithms used for chest, abdomen, and pelvic tumor motion
management/tracking for radiotherapy and provide a literature summary on the
topic. We will also discuss the limitations of these algorithms and propose
potential improvements.Comment: 36 pages, 5 Figures, 4 Table
Advancements and Breakthroughs in Ultrasound Imaging
Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world
New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
Multidimensional imaging techniques provide powerful ways to examine various
kinds of scientific questions. The routinely produced datasets in the
terabyte-range, however, can hardly be analyzed manually and require an
extensive use of automated image analysis. The present thesis introduces a new
concept for the estimation and propagation of uncertainty involved in image
analysis operators and new segmentation algorithms that are suitable for
terabyte-scale analyses of 3D+t microscopy images.Comment: 218 pages, 58 figures, PhD thesis, Department of Mechanical
Engineering, Karlsruhe Institute of Technology, published online with KITopen
(License: CC BY-SA 3.0, http://dx.doi.org/10.5445/IR/1000057821
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