3D nonrigid medical image registration using a new information theoretic measure.

International audienceThis work presents a novel method for the nonrigid registration of medical images based on the Arimoto entropy, a generalization of the Shannon entropy. The proposed method employed the Jensen-Arimoto divergence measure as a similarity metric to measure the statistical dependence between medical images. Free-form deformations were adopted as the transformation model and the Parzen window estimation was applied to compute the probability distributions. A penalty term is incorporated into the objective function to smooth the nonrigid transformation. The goal of registration is to optimize an objective function consisting of a dissimilarity term and a penalty term, which would be minimal when two deformed images are perfectly aligned using the limited memory BFGS optimization method, and thus to get the optimal geometric transformation. To validate the performance of the proposed method, experiments on both simulated 3D brain MR images and real 3D thoracic CT data sets were designed and performed on the open source elastix package. For the simulated experiments, the registration errors of 3D brain MR images with various magnitudes of known deformations and different levels of noise were measured. For the real data tests, four data sets of 4D thoracic CT from four patients were selected to assess the registration performance of the method, including ten 3D CT images for each 4D CT data covering an entire respiration cycle. These results were compared with the normalized cross correlation and the mutual information methods and show a slight but true improvement in registration accuracy

Robust non-rigid feature matching for image registration using geometry preserving

In this paper, a robust non-rigid feature matching approach for image registration with geometry constraints is proposed. The non-rigid feature matching approach is formulated as a maximum likelihood (ML) estimation problem. The feature points of one image are represented by Gaussian mixture model (GMM) centroids, and are fitted to the feature points of the other image by moving coherently to encode the global structure. To preserve the local geometry of these feature points, two local structure descriptors of the connectivity matrix and Laplacian coordinate are constructed. The expectation maximization (EM) algorithm is applied to solve this ML problem. Experimental results demonstrate that the proposed approach has better performance than current state-of-the-art methods

A precise bare simulation approach to the minimization of some distances. Foundations

In information theory -- as well as in the adjacent fields of statistics, machine learning, artificial intelligence, signal processing and pattern recognition -- many flexibilizations of the omnipresent Kullback-Leibler information distance (relative entropy) and of the closely related Shannon entropy have become frequently used tools. To tackle corresponding constrained minimization (respectively maximization) problems by a newly developed dimension-free bare (pure) simulation method, is the main goal of this paper. Almost no assumptions (like convexity) on the set of constraints are needed, within our discrete setup of arbitrary dimension, and our method is precise (i.e., converges in the limit). As a side effect, we also derive an innovative way of constructing new useful distances/divergences. To illustrate the core of our approach, we present numerous examples. The potential for widespread applicability is indicated, too; in particular, we deliver many recent references for uses of the involved distances/divergences and entropies in various different research fields (which may also serve as an interdisciplinary interface)

Medical image registration and soft tissue deformation for image guided surgery system

In parallel with the developments in imaging modalities, image-guided surgery (IGS) can now provide the surgeon with high quality three-dimensional images depicting human anatomy. Although IGS is now in widely use in neurosurgery, there remain some limitations that must be overcome before it can be employed in more general minimally invasive procedures. In this thesis, we have developed several contributions to the field of medical image registration and brain tissue deformation modeling. From the methodology point of view, medical image registration algorithms can be classified into feature-based and intensity-based methods. One of the challenges faced by feature-based registration would be to determine which specific type of feature is desired for a given task and imaging type. For this reason, a point set registration using points and curves feature is proposed, which has the accuracy of registration based on points and the robustness of registration based on lines or curves. We have also tackled the problem on rigid registration of multimodal images using intensity-based similarity measures. Mutual information (MI) has emerged in recent years as a popular similarity metric and widely being recognized in the field of medical image registration. Unfortunately, it ignores the spatial information contained in the images such as edges and corners that might be useful in the image registration. We introduce a new similarity metric, called Adaptive Mutual Information (AMI) measure which incorporates the gradient spatial information. Salient pixels in the regions with high gradient value will contribute more in the estimation of mutual information of image pairs being registered. Experimental results showed that our proposed method improves registration accuracy and it is more robust to noise images which have large deviation from the reference image. Along with this direction, we further improve the technique to simultaneously use all information obtained from multiple features. Using multiple spatial features, the proposed algorithm is less sensitive to the effect of noise and some inherent variations, giving more accurate registration. Brain shift is a complex phenomenon and there are many different reasons causing brain deformation. We have investigated the pattern of brain deformation with respect to location and magnitude and to consider the implications of this pattern for correcting brain deformation in IGS systems. A computational finite element analysis was carried out to analyze the deformation and stress tensor experienced by the brain tissue during surgical operations. Finally, we have developed a prototype visualization display and navigation platform for interpretation of IGS. The system is based upon Qt (cross-platform GUI toolkit) and it integrates VTK (an object-oriented visualization library) as the rendering kernel. Based on the construction of a visualization software platform, we have laid a foundation on the future research to be extended to implement brain tissue deformation into the system

Medical image registration and soft tissue deformation for image guided surgery system

In parallel with the developments in imaging modalities, image-guided surgery (IGS) can now provide the surgeon with high quality three-dimensional images depicting human anatomy. Although IGS is now in widely use in neurosurgery, there remain some limitations that must be overcome before it can be employed in more general minimally invasive procedures. In this thesis, we have developed several contributions to the field of medical image registration and brain tissue deformation modeling. From the methodology point of view, medical image registration algorithms can be classified into feature-based and intensity-based methods. One of the challenges faced by feature-based registration would be to determine which specific type of feature is desired for a given task and imaging type. For this reason, a point set registration using points and curves feature is proposed, which has the accuracy of registration based on points and the robustness of registration based on lines or curves. We have also tackled the problem on rigid registration of multimodal images using intensity-based similarity measures. Mutual information (MI) has emerged in recent years as a popular similarity metric and widely being recognized in the field of medical image registration. Unfortunately, it ignores the spatial information contained in the images such as edges and corners that might be useful in the image registration. We introduce a new similarity metric, called Adaptive Mutual Information (AMI) measure which incorporates the gradient spatial information. Salient pixels in the regions with high gradient value will contribute more in the estimation of mutual information of image pairs being registered. Experimental results showed that our proposed method improves registration accuracy and it is more robust to noise images which have large deviation from the reference image. Along with this direction, we further improve the technique to simultaneously use all information obtained from multiple features. Using multiple spatial features, the proposed algorithm is less sensitive to the effect of noise and some inherent variations, giving more accurate registration. Brain shift is a complex phenomenon and there are many different reasons causing brain deformation. We have investigated the pattern of brain deformation with respect to location and magnitude and to consider the implications of this pattern for correcting brain deformation in IGS systems. A computational finite element analysis was carried out to analyze the deformation and stress tensor experienced by the brain tissue during surgical operations. Finally, we have developed a prototype visualization display and navigation platform for interpretation of IGS. The system is based upon Qt (cross-platform GUI toolkit) and it integrates VTK (an object-oriented visualization library) as the rendering kernel. Based on the construction of a visualization software platform, we have laid a foundation on the future research to be extended to implement brain tissue deformation into the system

Microcalcifications Detection Using Image And Signal Processing Techniques For Early Detection Of Breast Cancer

Breast cancer has transformed into a severe health problem around the world. Early diagnosis is an important factor to survive this disease. The earliest detection signs of potential breast cancer that is distinguishable by current screening techniques are the presence of microcalcifications (MCs). MCs are small crystals of calcium apatite and their normal size ranges from 0.1mm to 0.5mm single crystals to groups up to a few centimeters in diameter. They are the first indication of breast cancer in more than 40% of all breast cancer cases, making their diagnosis critical. This dissertation proposes several segmentation techniques for detecting and isolating point microcalcifications: Otsu’s Method, Balanced Histogram Thresholding, Iterative Method, Maximum Entropy, Moment Preserving, and Genetic Algorithm. These methods were applied to medical images to detect microcalcifications. In this dissertation, results from the application of these techniques are presented and their efficiency for early detection of breast cancer is explained. This dissertation also explains theories and algorithms related to these techniques that can be used for breast cancer detection

Estimating the 4D respiratory lung motion by spatiotemporal registration and super-resolution image reconstruction

One of the main challenges in lung cancer radiation therapy is how to reduce the treatment margin but accommodate the geometric uncertainty of moving tumor. 4D-CT is able to provide the full range of motion information for the lung and tumor. However, accurate estimation of lung motion with respect to the respiratory phase is difficult due to various challenges in image registration, e.g., motion artifacts and large interslice thickness in 4D-CT. Meanwhile, the temporal coherence across respiration phases is usually not guaranteed in the conventional registration methods which consider each phase image in 4D-CT independently. To address these challenges, the authors present a unified approach to estimate the respiratory lung motion with two iterative steps

Deformable Medical Image Registration: A Survey

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this technical report, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this technical report is to provide an extensive account of registration techniques in a systematic manner.Le recalage déformable d'images est une des tâches les plus fondamentales dans l'imagerie médicale. Parmi ses applications les plus importantes, on compte: i) la fusion d' information provenant des différents types de modalités a n de faciliter le diagnostic et la planification du traitement; ii) les études longitudinales, oú des changements structurels ou anatomiques sont étudiées en fonction du temps; et iii) la modélisation de la variabilité anatomique normale d'une population et les atlas statistiques. Dans ce rapport de recherche, nous essayons de donner un aperçu des différentes méthodes du recalage déformables, en mettant l'accent sur les avancées les plus récentes du domaine. Nous avons particulièrement insisté sur les techniques appliquées aux images médicales. A n d'étudier les méthodes du recalage d'images, leurs composants principales sont d'abord identifiés puis étudiées de manière indépendante, les techniques les plus récentes étant classifiées en suivant un schéma logique déterminé. La contribution de ce rapport de recherche est de fournir un compte rendu détaillé des techniques de recalage d'une manière systématique

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

3D-3D Deformable Registration and Deep Learning Segmentation based Neck Diseases Analysis in MRI

Whiplash, cervical dystonia (CD), neck pain and work-related upper limb disorder (WRULD) are the most common diseases in the cervical region. Headaches, stiffness, sensory disturbance to the legs and arms, optical problems, aching in the back and shoulder, and auditory and visual problems are common symptoms seen in patients with these diseases. CD patients may also suffer tormenting spasticity in some neck muscles, with the symptoms possibly being acute and persisting for a long time, sometimes a lifetime. Whiplash-associated disorders (WADs) may occur due to sudden forward and backward movements of the head and neck occurring during a sporting activity or vehicle or domestic accident. These diseases affect private industries, insurance companies and governments, with the socio-economic costs significantly related to work absences, long-term sick leave, early disability and disability support pensions, health care expenses, reduced productivity and insurance claims. Therefore, diagnosing and treating neck-related diseases are important issues in clinical practice. The reason for these afflictions resulting from accident is the impairment of the cervical muscles which undergo atrophy or pseudo-hypertrophy due to fat infiltrating into them. These morphological changes have to be determined by identifying and quantifying their bio-markers before applying any medical intervention. Volumetric studies of neck muscles are reliable indicators of the proper treatments to apply. Radiation therapy, chemotherapy, injection of a toxin or surgery could be possible ways of treating these diseases. However, the dosages required should be precise because the neck region contains some sensitive organs, such as nerves, blood vessels and the trachea and spinal cord. Image registration and deep learning-based segmentation can help to determine appropriate treatments by analyzing the neck muscles. However, this is a challenging task for medical images due to complexities such as many muscles crossing multiple joints and attaching to many bones. Also, their shapes and sizes vary greatly across populations whereas their cross-sectional areas (CSAs) do not change in proportion to the heights and weights of individuals, with their sizes varying more significantly between males and females than ages. Therefore, the neck's anatomical variabilities are much greater than those of other parts of the human body. Some other challenges which make analyzing neck muscles very difficult are their compactness, similar gray-level appearances, intra-muscular fat, sliding due to cardiac and respiratory motions, false boundaries created by intramuscular fat, low resolution and contrast in medical images, noise, inhomogeneity and background clutter with the same composition and intensity. Furthermore, a patient's mode, position and neck movements during the capture of an image create variability. However, very little significant research work has been conducted on analyzing neck muscles. Although previous image registration efforts form a strong basis for many medical applications, none can satisfy the requirements of all of them because of the challenges associated with their implementation and low accuracy which could be due to anatomical complexities and variabilities or the artefacts of imaging devices. In existing methods, multi-resolution- and heuristic-based methods are popular. However, the above issues cause conventional multi-resolution-based registration methods to be trapped in local minima due to their low degrees of freedom in their geometrical transforms. Although heuristic-based methods are good at handling large mismatches, they require pre-segmentation and are computationally expensive. Also, current deformable methods often face statistical instability problems and many local optima when dealing with small mismatches. On the other hand, deep learning-based methods have achieved significant success over the last few years. Although a deeper network can learn more complex features and yields better performances, its depth cannot be increased as this would cause the gradient to vanish during training and result in training difficulties. Recently, researchers have focused on attention mechanisms for deep learning but current attention models face a challenge in the case of an application with compact and similar small multiple classes, large variability, low contrast and noise. The focus of this dissertation is on the design of 3D-3D image registration approaches as well as deep learning-based semantic segmentation methods for analyzing neck muscles. In the first part of this thesis, a novel object-constrained hierarchical registration framework for aligning inter-subject neck muscles is proposed. Firstly, to handle large-scale local minima, it uses a coarse registration technique which optimizes a new edge position difference (EPD) similarity measure to align large mismatches. Also, a new transformation based on the discrete periodic spline wavelet (DPSW), affine and free-form-deformation (FFD) transformations are exploited. Secondly, to avoid the monotonous nature of using transformations in multiple stages, affine registration technique, which uses a double-pushing system by changing the edges in the EPD and switching the transformation's resolutions, is designed to align small mismatches. The EPD helps in both the coarse and fine techniques to implement object-constrained registration via controlling edges which is not possible using traditional similarity measures. Experiments are performed on clinical 3D magnetic resonance imaging (MRI) scans of the neck, with the results showing that the EPD is more effective than the mutual information (MI) and the sum of squared difference (SSD) measures in terms of the volumetric dice similarity coefficient (DSC). Also, the proposed method is compared with two state-of-the-art approaches with ablation studies of inter-subject deformable registration and achieves better accuracy, robustness and consistency. However, as this method is computationally complex and has a problem handling large-scale anatomical variabilities, another 3D-3D registration framework with two novel contributions is proposed in the second part of this thesis. Firstly, a two-stage heuristic search optimization technique for handling large mismatches,which uses a minimal user hypothesis regarding these mismatches and is computationally fast, is introduced. It brings a moving image hierarchically closer to a fixed one using MI and EPD similarity measures in the coarse and fine stages, respectively, while the images do not require pre-segmentation as is necessary in traditional heuristic optimization-based techniques. Secondly, a region of interest (ROI) EPD-based registration framework for handling small mismatches using salient anatomical information (AI), in which a convex objective function is formed through a unique shape created from the desired objects in the ROI, is proposed. It is compared with two state-of-the-art methods on a neck dataset, with the results showing that it is superior in terms of accuracy and is computationally fast. In the last part of this thesis, an evaluation study of recent U-Net-based convolutional neural networks (CNNs) is performed on a neck dataset. It comprises 6 recent models, the U-Net, U-Net with a conditional random field (CRF-Unet), attention U-Net (A-Unet), nested U-Net or U-Net++, multi-feature pyramid (MFP)-Unet and recurrent residual U-Net (R2Unet) and 4 with more comprehensive modifications, the multi-scale U-Net (MS-Unet), parallel multi-scale U-Net (PMSUnet), recurrent residual attention U-Net (R2A-Unet) and R2A-Unet++ in neck muscles segmentation, with analyses of the numerical results indicating that the R2Unet architecture achieves the best accuracy. Also, two deep learning-based semantic segmentation approaches are proposed. In the first, a new two-stage U-Net++ (TS-UNet++) uses two different types of deep CNNs (DCNNs) rather than one similar to the traditional multi-stage method, with the U-Net++ in the first stage and the U-Net in the second. More convolutional blocks are added after the input and before the output layers of the multi-stage approach to better extract the low- and high-level features. A new concatenation-based fusion structure, which is incorporated in the architecture to allow deep supervision, helps to increase the depth of the network without accelerating the gradient-vanishing problem. Then, more convolutional layers are added after each concatenation of the fusion structure to extract more representative features. The proposed network is compared with the U-Net, U-Net++ and two-stage U-Net (TS-UNet) on the neck dataset, with the results indicating that it outperforms the others. In the second approach, an explicit attention method, in which the attention is performed through a ROI evolved from ground truth via dilation, is proposed. It does not require any additional CNN, as does a cascaded approach, to localize the ROI. Attention in a CNN is sensitive with respect to the area of the ROI. This dilated ROI is more capable of capturing relevant regions and suppressing irrelevant ones than a bounding box and region-level coarse annotation, and is used during training of any CNN. Coarse annotation, which does not require any detailed pixel wise delineation that can be performed by any novice person, is used during testing. This proposed ROI-based attention method, which can handle compact and similar small multiple classes with objects with large variabilities, is compared with the automatic A-Unet and U-Net, and performs best