Part-to-whole Registration of Histology and MRI using Shape Elements

Image registration between histology and magnetic resonance imaging (MRI) is a challenging task due to differences in structural content and contrast. Too thick and wide specimens cannot be processed all at once and must be cut into smaller pieces. This dramatically increases the complexity of the problem, since each piece should be individually and manually pre-aligned. To the best of our knowledge, no automatic method can reliably locate such piece of tissue within its respective whole in the MRI slice, and align it without any prior information. We propose here a novel automatic approach to the joint problem of multimodal registration between histology and MRI, when only a fraction of tissue is available from histology. The approach relies on the representation of images using their level lines so as to reach contrast invariance. Shape elements obtained via the extraction of bitangents are encoded in a projective-invariant manner, which permits the identification of common pieces of curves between two images. We evaluated the approach on human brain histology and compared resulting alignments against manually annotated ground truths. Considering the complexity of the brain folding patterns, preliminary results are promising and suggest the use of characteristic and meaningful shape elements for improved robustness and efficiency.Comment: Paper accepted at ICCV Workshop (Bio-Image Computing

Large-Scale, Metric Structure from Motion for Unordered Light Fields

This paper presents a large scale, metric Structure from Motion (SfM) pipeline for generalised cameras with overlapping fields-of-view, and demonstrates it using Light Field (LF) images. We build on recent developments in algorithms for absolute and relative pose recovery for generalised cameras and couple them with multi-view triangulation in a robust framework that advances the state-of-the-art on 3D reconstruction from LFs in several ways. First, our framework can recover the scale of a scene. Second, it is concerned with unordered sets of LF images, meticulously determining the order in which images should be considered. Third, it can scale to datasets with hundreds of LF images. Finally, it recovers 3D scene structure while abstaining from triangulating using very small baselines. Our approach outperforms the state-of-the-art, as demonstrated by real-world experiments with variable size datasets

A Linear Approach to Absolute Pose Estimation for Light Fields

This paper presents the first absolute pose estimation approach tailored to Light Field cameras. It builds on the observation that the ratio between the disparity arising in different sub-aperture images and their corresponding baseline is constant. Hence, we augment the 2D pixel coordinates with the corresponding normalised disparity to obtain the Light Field feature. This new representation reduces the effect of noise by aggregating multiple projections and allows for linear estimation of the absolute pose of a Light Field camera using the well-known Direct Linear Transformation algorithm. We evaluate the resulting absolute pose estimates with extensive simulations and experiments involving real Light Field datasets, demonstrating the competitive performance of our linear approach. Furthermore, we integrate our approach in a state-of-the-art Light Field Structure from Motion pipeline and demonstrate accurate multi-view 3D reconstruction

Uno studio sul rilascio di aromi in sistemi modello attraverso misure PTR-MS di nose-space

Vitreoretinal surgery is moving towards 3D visualization of the surgical field. This require acquisition system capable of recording such 3D information. We propose a proof of concept imaging system based on a light-field camera where an array of micro-lenses is placed in front of a conventional sensor. With a single snapshot, a stack of images focused at different depth are produced on the fly, which provides enhanced depth perception for the surgeon. Difficulty in depth localization of features and frequent focus-change during surgery are making current vitreoretinal heads-up surgical imaging systems cumbersome to use. To improve the depth perception and eliminate the need to manually refocus on the instruments during the surgery, we designed and implemented a proof-of-concept ophthalmoscope equipped with a commercial light-field camera. The sensor of our camera is composed of an array of micro-lenses which are projecting an array of overlapped micro-images. We show that with a single light-field snapshot we can digitally refocus between the retina and a tool located in front of the retina or display an extended depth-of-field image where everything is in focus. The design and system performances of the plenoptic fundus camera are detailed. We will conclude by showing in vivo data recorded with our device