Optic fundus assessment is widely used for diagnosing vascular and non-vascular pathology. Inspection of the
retinal vasculature may reveal hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. Due to
various imaging conditions retinal images may be degraded. Consequently, the enhancement of such images and
vessels in them is an important task with direct clinical applications. We propose a novel technique for vessel
enhancement in retinal images that is capable of enhancing vessel junctions in addition to linear vessel segments.
This is an extension of vessel filters we have previously developed for vessel enhancement in thoracic CT scans.
The proposed approach is based on probabilistic models which can discern vessels and junctions. Evaluation
shows the proposed filter is better than several known techniques and is comparable to the state of the art when
evaluated on a standard dataset. A ridge-based vessel tracking process is applied on the enhanced image to
demonstrate the effectiveness of the enhancement filter.
Automated detection of lung nodules in thoracic CT scans is an important clinical challenge. Blood vessels form a major source of false positives in automated nodule detection systems. Hence, the performance of such systems may be improved by enhancing nodules while suppressing blood vessels. Ideally, nodule enhancement filters
should enhance nodules while suppressing vessels and lung tissue. A distinction between vessels and nodules is normally obtained through eigenvalue analysis of the Hessian matrix. The Hessian matrix is a second order differential quantity and so is sensitive to noise. Furthermore, by relying on principal curvatures alone, existing
filters are incapable of distinguishing between nodules and vessel junctions, and are incapable of handling cases in which nodules touch vessels. In this paper we develop novel nodule enhancement filters that are capable of suppressing junctions and are capable of handling cases in which nodules appear to touch or even overlap with vessels. The proposed filters are based on optimized probabilistic models derived from eigenvalue analysis of the gradient correlation matrix which is a first order differential quantity and so are less sensitive to noise compared with known vessel enhancement filters. The proposed filters are evaluated and compared to known techniques both qualitatively, quantitatively. The evaluation includes both synthetic and actual clinical data.
Volume registration is fundamental to multiple medical imaging
algorithms. Specifically, non-rigid registration of thoracic CT scans
taken at different time instances can be used to detect new nodules more
reliably and assess the growth rate of existing nodules.
Voxel-based registration techniques are generally sensitive to intensity
variation and structural differences, which are common in CT scans due
to partial volume effects and naturally occurring motion and deformations.
The approach we propose in this paper is based on vessel tree extraction
which is then used to infer the complete volume registration. Vessels
form unique features with good localization.
Using extracted vessel trees, a minimization process is used to estimate
the motion vectors at vessels. Accurate motion vectors are obtained
at vessel junctions whereas vessel segments support only normal
component estimation. The obtained motion vectors are then interpolated
to produce a dense motion field using thin plate splines.
The proposed approach is evaluated on both real and synthetically
deformed volumes. The obtained results are compared to several
standard registration techniques. It is shown that by using vessel
structure, the proposed approach results in improved performance.
Vessel enhancement in volumetric data is a necessary prerequisite in
various medical imaging applications. In the context of automated lung nodule detection in thoracic CT scans, segmented blood vessels can be used to resolve local ambiguities based on global considerations and so improve the performance of lung nodule detection algorithms. Segmenting the data correctly is a difficult problem with direct consequences for subsequent processing steps. Voxels belonging to vessels and nodules in thoracic CT scans are both characterized by high contrast with respect to a local neighborhood. Thus in order to enhance vessels while suppressing nodules, additional characteristics should be used. In this paper we propose a novel vessel enhancement filter that is capable of enhancing vessels and junctions in thoracic CT scans while suppressing nodules. The proposed filters are based on a Gaussian mixture model which is optimized through expectation maximization. The proposed filters are based on first order differential quantities and so are less sensitive to noise compared with known Hessian-based vessel enhancement filters. Moreover, the proposed filters utilize an adaptive window and so avoid the common need for multiple scale analysis. The proposed filters are evaluated and compared to known techniques qualitatively and quantitatively on both synthetic and actual clinical data and it is shown that the proposed filters perform better.
Blood vessel segmentation in volumetric data is a necessary prerequisite in various medical imaging applications. Specifically, when considering the application of automatic lung nodule detection in thoracic CT scans, segmented blood vessels can be used in order to resolve local ambiguities based on global considerations and so improve the performance of lung nodule detection algorithms. In this paper, a novel regulated morphology approach to fuzzy shape analysis is described in the context of blood vessel extraction in thoracic CT scans. The fuzzy shape representation is obtained by using regulated morphological operations. Such a representation is necessary due to noise present in the data and due to the discrete nature of the volumetric data produced by CT scans, and particularly the interslice spacing. Regulated morphological operations are a generalization of ordinary morphological operations which relax the extreme strictness inherent to ordinary morphological operations. Based on constraints of collinearity, size, and global direction, a tracking algorithm produces a set of connected trees representing blood vessels and nodules in the volume. The produced tree structures are composed of fuzzy spheres in which the degree of object membership is proportional to the ratio between the occupied volume and the volume of the discrete sphere encompassing it. The performance of the blood vessel extraction algorithm described in the paper is evaluated based on a distance measure between a known blood vessel structure and a recovered one. As the generation of synthetic data for which the true vessel network is known may not be sufficiently realistic, our evaluation is based on different versions of real data corrupted by multiplicative Gaussian noise.