Feature detection from echocardiography images using local phase information

Ultrasound images are characterized by their special speckle appearance, low contrast, and low signal-to-noise ratio. It is always challenging to extract important clinical information from these images. An important step before formal analysis is to transform the image to significant features of interest. Intensity based methods do not perform particularly well on ultrasound images. However, it has been previously shown that these images respond well to local phase-based methods which are theoretically intensity-invariant and thus suitable for ultrasound images. We extend the previous local phase-based method to detect features using the local phase computed from monogenic signal which is an isotropic extension of the analytic signal. We apply our method of multiscale feature-asymmetry measurement and local phase-gradient computation to cardiac ultrasound (echocardiography) images for the detection of endocardial, epicardial and myocardial centerline

The Hilbert transform on the two-sphere: A spectral characterization

The analytic signal is an important representation in one dimensional signal processing. Its generalization to two dimensions is the monogenic signal. The properties of the analytic and the monogenic signal in the Fourier domain are well known. A generalization to the sphere is given by the Hilbert transform on the sphere known from Clifford analysis. Nonetheless no spectral characterization exists and therefore prohibits an interpretation. We derive the spherical harmonic coefficients of the Hilbert transform on the sphere and give a series expansion. It will turn out that it acts as a differential operator on the spherical harmonic basis functions of the Laplace equation solution, analogously to the Riesz transform in two dimensions. This allows an interpretation of the Hilbert transform suitable for signal processing of signals naturally arising on the two-sphere. We show that the scale space naturally arising is a Poisson scale space in the unit ball. In addition the obtained interpretation of the Hilbert transform is used for orientation analysis of plane waves. This representation is justified as a novel signal model on the sphere which can be used to construct intensity and rotation invariant feature detectors in a scale-space concept

Phase-based registration of multi-view real-time three-dimensional echocardiographic sequences.

Real time three-dimensional echocardiography opens the possibility of interactive, fast three-dimensional analysis of cardiac anatomy and function. However, at the present time these capabilities cannot be fully exploited due to the low image quality associated to this modality. We propose to increase image quality and information content by combining images acquired from different echocardiographic windows. In this paper, we present an algorithm to register these datasets. Phase-based measures have been proposed as a suitable alternative to intensity-based ones for ultrasound image analysis. The proposed algorithm uses a new cost function, based on local orientation and phase differences, to align the datasets. Visual observation of results, and preliminary numerical analysis, show the robustness and accuracy of this method

Utilisation de signaux hypercomplexes en estimation du mouvement et recalage multimodal

L'imagerie médicale est d'une nécessité certaine pour aider les médecins à comprendre et interpréter les comportements mécaniques et fonctionnels du corps humain. Les différentes modalités existantes fournissent des informations complémentaires qui peuvent améliorer cette compréhension. En particulier, la déformation d'organes ou de tissus peut fournir une indication sur la présence ou non d'une pathologie. Cette appréciation qualitative est facile à effectuer à l'œil nu, mais une estimation automatisée et précise de cette déformation peut être nécessaire. Le choix le plus naturel pour traiter les images est de se baser sur l'intensité des pixels. Cependant, certaines approches d'estimation du mouvement décomposent d'abord l'image en différents descripteurs, tels que la phase spatiale, qui porte l'information structurelle de l'image. L'objectif de cette thèse est d'évaluer l'apport de ce type de descripteurs dans le cadre de séquences ultrasonores (US) et de recalage multimodal entre images par résonance magnétique (IRM) et US. Pour cela, nous avons d'abord montré que pour des images US, une approche basée sur la phase issue du signal monogène constituait un bon compromis vis-à-vis de techniques de mise en correspondance de blocs ou de flux optique basé sur la phase extraite du signal analytique complexe 2D. Nous avons ensuite poursuivi cette étude en considérant les différentes informations issues du signal monogène, avec son extension au cas 3D. Cela nous a permis de proposer un estimateur de translations basé sur un autre descripteur : l'orientation principale locale. Nous avons ensuite évalué l'apport de la phase dans le cadre du recalage IRM-US basé sur l'information mutuelle. Nous avons remarqué que dans ce cas, la phase donnait de meilleurs résultats que l'intensité dans la direction latérale mais pas axiale. Finalement, nous présentons les enjeux cliniques du prolapsus génito-urinaire chez la femme. Nous avons ainsi introduit un estimateur de mise en correspondance de blocs déformables basé sur la phase, que nous avons appliqué à des séquences échographiques in vivo. Bien que cet estimateur ait tendance à minimiser le stade du prolapsus, il permet un meilleur suivi des tissus au fil de la séquence que l'estimateur de blocs déformables initial basé sur l'intensitéNowadays, medical imaging is necessary to help doctors to understand and interpret the mechanical and functional behavior of the human body. The different existing modalities provide complementary information, which can improve this comprehension. In particular, the tissue deformation provide an indication on the presence of a pathology. This qualitative appreciation is easy to perform for the human eye, but it would be useful to get an automatic and accurate estimation of this deformation. The most natural choice to process images is to use the intensities of the pixels. However, some approaches estimate the motion decomposing the image in several descriptors, such as spatial phase, which is a strucural information of the image. The aim of this thesis is to evaluate the contribution of this kind of descriptors, when they are used for motion estimation on ultrasound (US) sequences and multimodal registration, between a magnetic resonance images (MRI) and US images. For this, we first showed that for ultrasound images, an approach based on the monogenic spatial phase was a good compromise, facing block matching technics or optical flow estimation based on 2D analytic complex signal. Then, we continued this study, considering all the features extracted from the 3D monogenic signal. It allowed us to propose a translation estimator based on another descriptor : the main local orientation. Afterward, we evaluated the contribution of the phase for MR-US registration based on the mutual information. We noted that, in this case, the spatial phase gave more accurate results than the intensity-based approach in the lateral direction, but not in the axial direction. Finally, we present the clinical issues of the pelvic organ prolaps. Thus, we introduced a phase-based block deformable block matching estimator. We applied this estimator on in vivo US sequences. Although this estimator tends to minimize the degree of the pelvic floor disorders, it allows a better tissues monitoring than the intensity-based block deformable estimator all along the sequenc

Automated Analysis of 3D Stress Echocardiography

__Abstract__ The human circulatory system consists of the heart, blood, arteries, veins and capillaries. The heart is the muscular organ which pumps the blood through the human body (Fig. 1.1,1.2). Deoxygenated blood flows through the right atrium into the right ventricle, which pumps the blood into the pulmonary arteries. The blood is carried to the lungs, where it passes through a capillary network that enables the release of carbon dioxide and the uptake of oxygen. Oxygenated blood then returns to the heart via the pulmonary veins and flows from the left atrium into the left ventricle. The left ventricle then pumps the blood through the aorta, the major artery which supplies blood to the rest of the body [Drake et a!., 2005; Guyton and Halt 1996]. Therefore, it is vital that the cardiovascular system remains healthy. Disease of the cardiovascular system, if untreated, ultimately leads to the failure of other organs and death

Post formation processing of cardiac ultrasound data for enhancing image quality and diagnostic value

Cardiovascular diseases (CVDs) constitute a leading cause of death, including premature death, in the developed world. The early diagnosis and treatment of CVDs is therefore of great importance. Modern imaging modalities enable the quantification and analysis of the cardiovascular system and provide researchers and clinicians with valuable tools for the diagnosis and treatment of CVDs. In particular, echocardiography offers a number of advantages, compared to other imaging modalities, making it a prevalent tool for assessing cardiac morphology and function. However, cardiac ultrasound images can suffer from a range of artifacts reducing their image quality and diagnostic value. As a result, there is great interest in the development of processing techniques that address such limitations. This thesis introduces and quantitatively evaluates four methods that enhance clinical cardiac ultrasound data by utilising information which until now has been predominantly disregarded. All methods introduced in this thesis utilise multiple partially uncorrelated instances of a cardiac cycle in order to acquire the information required to suppress or enhance certain image features. No filtering out of information is performed at any stage throughout the processing. This constitutes the main differentiation to previous data enhancement approaches which tend to filter out information based on some static or adaptive selection criteria. The first two image enhancement methods utilise spatial averaging of partially uncorrelated data acquired through a single acoustic window. More precisely, Temporal Compounding enhances cardiac ultrasound data by averaging partially uncorrelated instances of the imaged structure acquired over a number of consecutive cardiac cycles. An extension to the notion of spatial compounding of cardiac ultrasound data is 3D-to-2D Compounding, which presents a novel image enhancement method by acquiring and compounding spatially adjacent (along the elevation plane), partially uncorrelated, 2D slices of the heart extracted as a thin angular sub-sector of a volumetric pyramid scan. Data enhancement introduced by both approaches includes the substantial suppression of tissue speckle and cavity noise. Furthermore, by averaging decorrelated instances of the same cardiac structure, both compounding methods can enhance tissue structures, which are masked out by high levels of noise and shadowing, increasing their corresponding tissue/cavity detectability. The third novel data enhancement approach, referred as Dynamic Histogram Based Intensity Mapping (DHBIM), investigates the temporal variations within image histograms of consecutive frames in order to (i) identify any unutilised/underutilised intensity levels and (ii) derive the tissue/cavity intensity threshold within the processed frame sequence. Piecewise intensity mapping is then used to enhance cardiac ultrasound data. DHBIM introduces cavity noise suppression, enhancement of tissue speckle information as well as considerable increase in tissue/cavity contrast and detectability. A data acquisition and analysis protocol for integrating the dynamic intensity mapping along with spatial compounding methods is also investigated. The linear integration of DHBIM and Temporal Compounding forms the fourth and final implemented method, which is also quantitatively assessed. By taking advantage of the benefits and compensating for the limitations of each individual method, the integrated method suppresses cavity noise and tissue speckle while enhancing tissue/cavity contrast as well as the delineation of cardiac tissue boundaries even when heavily corrupted by cardiac ultrasound artifacts. Finally, a novel protocol for the quantitative assessment of the effect of each data enhancement method on image quality and diagnostic value is employed. This enables the quantitative evaluation of each method as well as the comparison between individual methods using clinical data from 32 patients. Image quality is assessed using a range of quantitative measures such as signal-to-noise ratio, tissue/cavity contrast and detectability index. Diagnostic value is assessed through variations in the repeatability level of routine clinical measurements performed on patient cardiac ultrasound scans by two experienced echocardiographers. Commonly used clinical measures such as the wall thickness of the Interventricular Septum (IVS) and the Left Ventricle Posterior Wall (LVPW) as well as the cavity diameter of the Left Ventricle (LVID) and Left Atrium (LAD) are employed for assessing diagnostic value