A Hybrid Similarity Measure Framework for Multimodal Medical Image Registration

Medical imaging is widely used today to facilitate both disease diagnosis and treatment planning practice, with a key prerequisite being the systematic process of medical image registration (MIR) to align either mono or multimodal images of different anatomical parts of the human body. MIR utilises a similarity measure (SM) to quantify the level of spatial alignment and is particularly demanding due to the presence of inherent modality characteristics like intensity non-uniformities (INU) in magnetic resonance images and large homogeneous non-vascular regions in retinal images. While various intensity and feature-based SMs exist for MIR, mutual information (MI) has become established because of its computational efficiency and ability to register multimodal images. It is however, very sensitive to interpolation artefacts in the presence of INU with noise and can be compromised when overlapping areas are small. Recently MI-based hybrid variants which combine regional features with intensity have emerged, though these incur high dimensionality and large computational overheads. To address these challenges and secure accurate, efficient and robust registration of images containing high INU, noise and large homogeneous regions, this thesis presents a new hybrid SM framework for 2D multimodal rigid MIR. The framework consistently provides superior quantitative and qualitative performance, while offering a uniquely flexible design trade-off between registration accuracy and computational time. It makes three significant technical contributions to the field: i) An expectation maximisation-based principal component analysis with mutual information (EMPCA-MI) framework incorporating neighbourhood feature information; ii) Two innovative enhancements to reduce information redundancy and improve MI computational efficiency; and iii) an adaptive algorithm to select the most significant principal components for feature selection. The thesis findings conclusively confirm the hybrid SM framework offers an accurate and robust 2D registration solution for challenging multimodal medical imaging datasets, while its inherent flexibility means it can also be extended to the 3D registration domain

A New Mutual Information based Similarity Measure for Medical Image Registration

Medical image registration (IR) is the systematic process of aligning spate images, often involving different modalities with common reference framework, so complementary information can be combined and compared. This paper presents a new similarity measure which uses Expectation Maximization for Principal Component Analysis allied with mutual information (EMPCA-MI) for medical IR. The new measure has been analysed on multimodal, three band magnetic resonance images (MRI) T1, T2 and PD weighted, in the presence of both intensity non-uniformities (INU) and noise. Both quantitative and qualitative experimental results clearly demonstrate both improved robustness and lower computational complexity of the new EMPCA-MI paradigm compared with existing MI-based similarity measures, for various MRI test datasets

Efficient Image Registration using Fast Principal Component Analysis

Incorporating spatial features with mutual information (MI) has demonstrated superior image registration performance compared with traditional MI-based methods, particularly in the presence of noise and intensity non-uniformities (INU). This paper presents a new efficient MI-based similarity measure which applies Expectation Maximisation for Principal Component Analysis (EMPCA-MI), to afford significantly lower computational complexity, while providing analogous image registration performance with other feature-based MI solutions. Experimental analysis corroborates both the improved robustness and faster runtimes of EMPCA-MI, for different test datasets containing both INU and noise artefacts

Multimodal retinal image registration using a fast principal component analysis hybrid-based similarity measure

Multimodal retinal images (RI) are extensively used for analysing various eye diseases and conditions such as myopia and diabetic retinopathy. The incorporation of either two or more RI modalities provides complementary structure information in the presence of non-uniform illumination and low-contrast homogeneous regions. It also presents significant challenges for retinal image registration (RIR). This paper investigates how the Expectation Maximization for Principal Component Analysis with Mutual Information (EMPCA-MI) algorithm can effectively achieve multimodal RIR. This iterative hybrid-based similarity measure combines spatial features with mutual information to provide enhanced registration without recourse to either segmentation or feature extraction. Experimental results for clinical multimodal RI datasets comprising colour fundus and scanning laser ophthalmoscope images confirm EMPCA-MI is able to consistently afford superior numerical and qualitative registration performance compared with existing RIR techniques, such as the bifurcation structures method

Why finance professors should be teaching Nietzsche

<p><strong>Abstract:</strong> Retinal images (RI) are widely used to diagnose a variety of eye conditions and diseases such as myopia and diabetic retinopathy. They are inherently characterised by having nonuniform illumination and low-contrast homogeneous regions which represent a unique set of challenges for retinal image registration (RIR). This paper investigates using the expectation maximization for principal component analysis based mutual information (EMPCA-MI) algorithm in RIR. It combines spatial features with mutual information to efficiently achieve improved registration performance. Experimental results for mono-modal RI datasets verify that EMPCA-MI<br>together with Powell-Brent optimization affords superior robustness in comparison with existing RIR methods, including the geometrical features method.</p> <p><br><strong>Index Terms</strong>— Image registration, principal component analysis, mutual information, expectation-maximization algorithms, retinopathy.</p> <p> </p> <p><strong>Poster presented at</strong>: 38th International Conference on Acoustics, Speech, and Signal Processing<br>(ICASSP), 26th to 31st May 2013, Vancouver, Canada.<br>doi: 10.1109/ICASSP.2013.6637824</p

Effects of threshold of hard cut based technique for advertisement detection in TV video streams

Advertisement detection in a TV video recording/archiving system is a challenging task. In this paper, shot change (hard cut) detection that is based on 'inter-frame difference', 'normalized difference energy' and the 'normalized sum of absolute differences' and its comparison with a threshold are discussed. The effects of changing video threshold on hard cut detection have been summarized. A threshold value is evaluated for the efficient extraction of complete advertisement. Effects of various thresholds are extensively tested on more than 20 hrs of TV video streams. The frequency of shot breaks along with blank frames is considered as a key component in advertisement detection. Results of these MATLAB and LabVIEW based computer simulations have been reported

Analysis of contemporary robotics simulators

Designing a robot physically and validating it at every step is an arduous task. Contemporary robotics simulators can help to achieve laborious physics simulations, 3D visualizations, virtual robot modeling and novel research work that save time and money. Simulators are written in numerous programming languages like C, C++, Java, C# and other OOP languages that decide their platform compatibility. Some robot simulators use physics engines like ODE, Karma Engine for better simulation that include responses like collision detection, scene representation and rigid body simulations. Some use sophisticated 3D graphic aids like OpenGL and Graphic cards (NVidia, ATI) rather than traditional Direct3D, considering various facets like cost, portability and extensions. Codes can be transferred to the real robots from some simulators after proper verification for their operation. Unlimited features like multiplayer, controllers transference over networks, `special effects' and so forth, keep on pouring. This paper proffers some of the widespread commercial/ open-source simulators for robots and UAVs used in market. Further, I conclude that any kind of robot including legged, wheeled or UAVs can be simulated using simulators available in market with continuous endeavors to upgrade these simulators

Real time low level parallel image processing for active vision systems

This paper presents an overview of low level parallel image processing algorithms and their implementation for active vision systems. Authors have demonstrated novel low level image processing algorithms for point operators, local operators, dithering, smoothing, edge detection, morphological operators, image segmentation and image compression. The algorithms have been prepared & described as pseudo codes. These algorithms have been simulated using Parallel Computing Toolboxtrade (PCT) of MATLAB. The PCT provides parallel constructs in the MATLAB language, such as parallel for loops, distributed arrays and message passing & enables rapid prototyping of parallel code through an interactive parallel MATLAB session

Robust Image Registration using Adaptive Expectation Maximisation based PCA

<p>Images having either the same or different modalities can be aligned using the systematic process of image registration. Inherent image characteristics including intensity non-uniformities in magnetic resonance images and large homogeneous<br>non-vascular regions in retinal and other generic image types however, pose a significant challenge to their registration. This paper presents an adaptive expectation maximisation for principal component analysis with mutual information (aEMPCA-MI)<br>similarity measure for image registration. It introduces a novel iterative process to adaptively select the most significant principal components using Kaiser rule and applies 4-pixel connectivity<br>for feature extraction together with Wichard’s bin size selection in calculating the MI. Both quantitative and qualitative results on a diverse range of image datasets, conclusively demonstrate<br>the superior image registration performance of aEMPCA-MI compared with existing MI-based similarity measures.</p