Quantitative imaging of retinal arteries and veins offers unique insights into cardiovascular and microvascular diseases but is laborious. We developed and tested a method to automatically identify arterial/venular (A/V) vessels in digital retinal images in conjunction with a semi-automatic segmentation technique. Methods of segmentation of blood vessels and the optic disc (OD) was performed as previously described, using a dataset of 10 colour fundus images. Using the OD as a reference a graph representation was constructed using the vessel skeletons. Vessel bifurcations and crossings were identified based on direction and local geometry, and A/V classification was carried out by fuzzy logic classification using colour information. Results were compared with expert classification. Preliminary results showed an average true positive rate for arteries of TPRA=0.83 and TPRV=0.74 for veins. With an overall average of TPRall=0.79 for both vessel type jointly. Computer-based systems can assess local and global aspects of the retinal microvascular architecture, geometry and topology. Automated A/V classification will facilitate efficient cost-effective assessment of clinical images at scale.
In recent years it has been more common to see 3D visualization of objects applied in many different areas. In
neuroscience research, 3D visualization of neurons acquired at different depth views (i.e. image stacks) by means
of confocal microscopy are of increase use. However in the best case, these visualizations only help to have a
qualitative description of the neuron shape. Since it is well know that neuronal function is intimately related to
its morphology. Having a precise characterization of neuronal structures such as axons and dendrites is critical
to perform a quantitative analysis and thus it allows to design neuronal functional models based on neuron
morphology. Currently there exists different commercial software to reconstruct neuronal arbors, however these
processes are labor intensive since in most of the cases they are manually made. In this paper we propose a new
software capable to reconstruct 3D neurons from confocal microscopy views in a more efficient way, with minimal
user intervention. The propose algorithm is based on finding the tubular structures present in the stack of images
using a modify version of the minimal graph cut algorithm. The model is generated from the segmented stack
with a modified version of the Marching Cubes algorithm to generate de 3D isosurface. Herein we describe the
principles of our 3D segmentation technique and the preliminary results.
3D reconstruction of blood vessels is a powerful visualization tool for physicians, since it allows them to refer
to qualitative representation of their subject of study. In this paper we propose a 3D reconstruction method of
retinal vessels from fundus images. The reconstruction method propose herein uses images of the same retinal
structure in epipolar geometry. Images are preprocessed by RISA system for segmenting blood vessels and
obtaining feature points for correspondences. The correspondence points process is solved using correlation.
The LMedS analysis and Graph Transformation Matching algorithm are used for outliers suppression. Camera
projection matrices are computed with the normalized eight point algorithm. Finally, we retrieve 3D position of
the retinal tree points by linear triangulation. In order to increase the power of visualization, 3D tree skeletons
are represented by surfaces via generalized cylinders whose radius correspond to morphological measurements
obtained by RISA. In this paper the complete calibration process including the fundus camera and the optical
properties of the eye, the so called camera-eye system is proposed. On one hand, the internal parameters
of the fundus camera are obtained by classical algorithms using a reference pattern. On the other hand, we
minimize the undesirable efects of the aberrations induced by the eyeball optical system assuming that contact
enlarging lens corrects astigmatism, spherical and coma aberrations are reduced changing the aperture size
and eye refractive errors are suppressed adjusting camera focus during image acquisition. Evaluation of two
self-calibration proposals and results of 3D blood vessel surface reconstruction are presented.
KEYWORDS: Cameras, Blood vessels, Image segmentation, 3D modeling, Calibration, 3D image processing, Matrices, Visualization, 3D image reconstruction, Field emission displays
This article presents improvements on a methodology for the estimation of the 3D structure of retinal blood vessels from a sequence of fundus images taken from the same subject. The following
problems are addressed: the use of a self-calibration method in order to find the intrinsic and extrinsic camera parameters based on correspondences between images, the extraction of blood vessels skeletons from fundus images, the matching of corresponding points of two labelled skeleton trees, the triangulation of matched points in and generation of surface model for visualisation. An image mapping was defined in order to correct optic distortions of the fundus camera, and a new set of fundus images was used.
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