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Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach
Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence
Nature Inspired Evolutionary Swarm Optimizers for Biomedical Image and Signal Processing -- A Systematic Review
The challenge of finding a global optimum in a solution search space with
limited resources and higher accuracy has given rise to several optimization
algorithms. Generally, the gradient-based optimizers converge to the global
solution very accurately, but they often require a large number of iterations
to find the solution. Researchers took inspiration from different natural
phenomena and behaviours of many living organisms to develop algorithms that
can solve optimization problems much quicker with high accuracy. These
algorithms are called nature-inspired meta-heuristic optimization algorithms.
These can be used for denoising signals, updating weights in a deep neural
network, and many other cases. In the state-of-the-art, there are no systematic
reviews available that have discussed the applications of nature-inspired
algorithms on biomedical signal processing. The paper solves that gap by
discussing the applications of such algorithms in biomedical signal processing
and also provides an updated survey of the application of these algorithms in
biomedical image processing. The paper reviews 28 latest peer-reviewed relevant
articles and 26 nature-inspired algorithms and segregates them into thoroughly
explored, lesser explored and unexplored categories intending to help readers
understand the reliability and exploration stage of each of these algorithms
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research
Local Binary Fitting Segmentation by Cooperative Quantum Particle Optimization
Recently, sophisticated segmentation techniques, such as level set method, which using valid numerical calculation methods to process the evolution of the curve by solving linear or nonlinear elliptic equations to divide the image availably, has become being more popular and effective. In Local Binary Fitting (LBF) algorithm, a simple contour is initialized in an image and then the steepest-descent algorithm is employed to constrain it to minimize the fitting energy functional. Hence, the initial position of the contour is difficult or impossible to be well chosen for the final performance. To overcoming this drawback, this work treats the energy fitting problem as a meta-heuristic optimization algorithm and imports a varietal particle swarm optimization (PSO) method into the inner optimization process. The experimental results of segmentations on medical images show that the proposed method is not only effective to both simple and complex medical images with adequate stochastic effects, but also shows the accuracy and high efficiency
Development of registration methods for cardiovascular anatomy and function using advanced 3T MRI, 320-slice CT and PET imaging
Different medical imaging modalities provide complementary anatomical and
functional information. One increasingly important use of such information is in
the clinical management of cardiovascular disease. Multi-modality data is helping
improve diagnosis accuracy, and individualize treatment. The Clinical Research
Imaging Centre at the University of Edinburgh, has been involved in a number
of cardiovascular clinical trials using longitudinal computed tomography (CT) and
multi-parametric magnetic resonance (MR) imaging. The critical image processing
technique that combines the information from all these different datasets is known
as image registration, which is the topic of this thesis. Image registration, especially
multi-modality and multi-parametric registration, remains a challenging field in
medical image analysis. The new registration methods described in this work were
all developed in response to genuine challenges in on-going clinical studies. These
methods have been evaluated using data from these studies.
In order to gain an insight into the building blocks of image registration methods,
the thesis begins with a comprehensive literature review of state-of-the-art algorithms.
This is followed by a description of the first registration method I developed to help
track inflammation in aortic abdominal aneurysms. It registers multi-modality and
multi-parametric images, with new contrast agents. The registration framework uses a
semi-automatically generated region of interest around the aorta. The aorta is aligned
based on a combination of the centres of the regions of interest and intensity matching.
The method achieved sub-voxel accuracy.
The second clinical study involved cardiac data. The first framework failed to
register many of these datasets, because the cardiac data suffers from a common
artefact of magnetic resonance images, namely intensity inhomogeneity. Thus I
developed a new preprocessing technique that is able to correct the artefacts in the
functional data using data from the anatomical scans. The registration framework,
with this preprocessing step and new particle swarm optimizer, achieved significantly
improved registration results on the cardiac data, and was validated quantitatively
using neuro images from a clinical study of neonates. Although on average
the new framework achieved accurate results, when processing data corrupted
by severe artefacts and noise, premature convergence of the optimizer is still a
common problem. To overcome this, I invented a new optimization method, that
achieves more robust convergence by encoding prior knowledge of registration. The
registration results from this new registration-oriented optimizer are more accurate
than other general-purpose particle swarm optimization methods commonly applied
to registration problems.
In summary, this thesis describes a series of novel developments to an image
registration framework, aimed to improve accuracy, robustness and speed. The
resulting registration framework was applied to, and validated by, different types of
images taken from several ongoing clinical trials. In the future, this framework could
be extended to include more diverse transformation models, aided by new machine
learning techniques. It may also be applied to the registration of other types and
modalities of imaging data
Development of Efficient Intensity Based Registration Techniques for Multi-modal Brain Images
Recent advances in medical imaging have resulted in the development of many imaging techniques that capture various aspects of the patients anatomy and metabolism. These are accomplished with image registration: the task of transforming images on a common anatomical coordinate space. Image registration is one of the important task for multi-modal brain images, which has paramount importance in clinical diagnosis, leads to treatment of brain diseases. In many other applications, image registration characterizes anatomical variability, to detect changes in disease state over time, and by mapping functional information into anatomical space. This thesis is focused to explore intensity-based registration techniques to accomplish precise information with accurate transformation for multi-modal brain images. In this view, we addressed mainly three important issues of image registration both in the rigid and non-rigid framework, i.e. i) information theoretic based similarity measure for alignment measurement, ii) free form deformation (FFD) based transformation, and iii) evolutionary technique based optimization of the cost function. Mutual information (MI) is a widely used information theoretic similarity measure criterion for multi-modal brain image registration. MI only dense the quantitative aspects of information based on the probability of events. For rustication of the information of events, qualitative aspect i.e. utility or saliency is a necessitate factor for consideration. In this work, a novel similarity measure is proposed, which incorporates the utility information into mutual Information, known as Enhanced Mutual Information(EMI).It is found that the maximum information gain using EMI is higher as compared to that of other state of arts. The utility or saliency employed in EMI is a scale invariant parameter, and hence it may fail to register in case of projective and perspective transformations. To overcome this bottleneck, salient region (SR) based Enhance Mutual Information (SR-EMI)is proposed, a new similarity measure for robust and accurate registration. The proposed SR-EMI based registration technique is robust to register the multi-modal brain images at a faster rate with better alignment
Deep Learning and Quantum-computing Based Optimization in Medical Imaging and Power Dispatcing
兵庫県立大学大学院202
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