Regional Appearance Modeling based on the Clustering of Intensity Profiles

International audienceModel-based image segmentation is a popular approach for the segmentation of anatomical structures from medical images because it includes prior knowledge about the shape and appearance of structures of interest. This paper focuses on the formulation of a novel appearance prior that can cope with large variability between subjects, for instance due to the presence of pathologies. Instead of relying on Principal Component Analysis such as in Statistical Appearance Models, our approach relies on a multimodal intensity profi le atlas from which a point may be assigned to several profi le modes consisting of a mean pro le and its covariance matrix. These profi le modes are first estimated without any intra-subject registration through a boosted EM classi cation based on spectral clustering. Then, they are projected on a reference mesh whose role is to store the appearance information in a common geometric representation. We show that this prior leads to better performance than the classical monomodal Principal Component Analysis approach while relying on fewer pro file modes

A Novel Framework for Highlight Reflectance Transformation Imaging

We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa

A Practical Reflectance Transformation Imaging Pipeline for Surface Characterization in Cultural Heritage

We present a practical acquisition and processing pipeline to characterize the surface structure of cultural heritage objects. Using a free-form Reflectance Transformation Imaging (RTI) approach, we acquire multiple digital photographs of the studied object shot from a stationary camera. In each photograph, a light is freely positioned around the object in order to cover a wide variety of illumination directions. Multiple reflective spheres and white Lambertian surfaces are added to the scene to automatically recover light positions and to compensate for non-uniform illumination. An estimation of geometry and reflectance parameters (e.g., albedo, normals, polynomial texture maps coefficients) is then performed to locally characterize surface properties. The resulting object description is stable and representative enough of surface features to reliably provide a characterization of measured surfaces. We validate our approach by comparing RTI-acquired data with data acquired with a high-resolution microprofilometer.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 66509

Clustering and Shifting of Regional Appearance for Deformable Model Segmentation

Automated medical image segmentation is a challenging task that benefits from the use of effective image appearance models. An appearance model describes the grey-level intensity information relative to the object being segmented. Previous models that compare the target against a single template image or that assume a very small-scale correspondence fail to capture the variability seen in the target cases. In this dissertation I present novel appearance models to address these deficiencies, and I show their efficacy in segmentation via deformable models. The models developed here use clustering and shifting of the object-relative appearance to capture the true variability in appearance. They all learn their parameters from training sets of previously-segmented images. The first model uses clustering on cross-boundary intensity profiles in the training set to determine profile types, and then it builds a template of optimal types that reflects the various edge characteristics seen around the boundary. The second model uses clustering on local regional image descriptors to determine large-scale regions relative to the boundary. The method then partitions the object boundary according to region type and captures the intensity variability per region type. The third and fourth models allow shifting of the image model on the boundary to reflect knowledge of the variable regional conformations seen in training. I evaluate the appearance models by considering their efficacy in segmentation of the kidney, bladder, and prostate in abdominal and male pelvis CT. I compare the automatically generated segmentations using these models against expert manual segmentations of the target cases and against automatically generated segmentations using previous models

Doctor of Philosophy

dissertationMany retinal pathologies, including age-related macular degeneration (AMD), display characteristic spatial patterns. AMD predominately a ects the macula, the central conedominated region of the human retina responsible for high-acuity daytime vision. An understanding of why the macula is speci cally susceptible to age-related changes would likely prove invaluable to understanding the pathology of AMD and the development of preventative therapies. Unfortunately, such an understanding has thus far proven elusive. A large number of physiological and anatomical parameters vary signi cantly between the central and peripheral human retina, and many of these parameters are altered during retinal degenerative disorders. This produces a large number of spatial associations between various parameters, obscuring causal relationships and making identi cation of timing and initiating factors very challenging. To address this challenge we developed RetSpace, an analytic software package designed to generate and analyze quantitative maps of anatomic and pathologic parameters within individual human retinas. The RetSpace system was speci cally designed to analyze image sets generated with computational molecular phenotyping (CMP), a technique pioneered by our laboratory to characterize the immense cellular diversity of the neural and sensory retina. By combining the sensitivity and diversity of CMP with the analytic power of RetSpace, we have produced a novel mechanism to study the spatial distributions and regional variability of various measures of retinal anatomy and pathology, as well as the extent to which di erent pathologies show regional correlations potentially indicative of shared pathological origins. To demonstrate the utility of our approach we have analyzed the severity and spatial distributions of an extensive set of anatomic and pathologic parameters in a series of aging human donor retinas. In doing so we have identi ed novel metabolic changes in the RPE and photoreceptors that are spatially and quantitatively correlated with known pathological characteristics of AMD and may serve as sensitive markers of early stress in AMD and other retinal diseases. Mathematical models of the heterocellular diversity of these metabolic changes provide further insight into the mechanisms behind these changes and hint at the origins and spatial specificity of the disease