Fibrin networks are a major component of blood clots that provides structural support to the formation of
growing clots. Abnormal fibrin networks that are too rigid or too unstable can promote cardiovascular problems
and/or bleeding. However, current biological studies of fibrin networks rarely perform quantitative analysis of
their structural properties (e.g., the density of branch points) due to the massive branching structures of the
networks. In this paper, we present a new approach for segmenting and analyzing fibrin networks in 3D confocal
microscopy images. We first identify the target fibrin network by applying the 3D region growing method with
global thresholding. We then produce a one-voxel wide centerline for each fiber segment along which the branch
points and other structural information of the network can be obtained. Branch points are identified by a novel
approach based on the outer medial axis. Cells within the fibrin network are segmented by a new algorithm that
combines cluster detection and surface reconstruction based on the α-shape approach. Our algorithm has been
evaluated on computer phantom images of fibrin networks for identifying branch points. Experiments on z-stack
images of different types of fibrin networks yielded results that are consistent with biological observations.
Accurate detection and classification of stained cells in microscopy images enable quantitative measurements of
cell distributions and spatial structures, and are crucial for developing new analysis tools for medical studies and
applications such as cancer diagnosis and treatment. In this paper, we present a learning based approach for
identifying different types of cells in multi-spectral microscopy images of tumor-draining lymph nodes (TDLNs)
and locating their centroid positions. With our approach, a set of features based on the eigenvalues of the
Hessian matrix is constructed for each image pixel to determine whether the local shape is elliptic. The elliptic
features are then used together with the intensity-based ring scores as the feature set for the supervised learning
method. Using this new feature set, a random forest based classifier is trained from a set of training samples
of different cell types. In order to overcome the difficulties of classifying cells with varying stain qualities, sizes,
and shapes, we build a large set of prior training data from a variety of tissue sections. To deal with the issue
of multiple overlapping cell nuclei in images, we propose to utilize the spikes of the outer medial axis of the cells
to detect and detach the touching cells. As a result, the centroid position of each identified cell is pinpointed.
The experimental data show that our proposed method achieves higher recognition rates than previous methods,
reducing significantly the human interaction effort involved in previous cell classification work.
Plain radiography (i.e., X-ray imaging) provides an effective and economical imaging modality for diagnosing
knee illnesses and injuries. Automatically segmenting and analyzing knee radiographs is a challenging problem.
In this paper, we present a new approach for accurately segmenting the knee joint in X-ray images. We first
use the Gaussian high-pass filter to remove homogeneous regions which are unlikely to appear on bone contours.
We then presegment the bones and develop a novel decomposition-based sweeping algorithm for extracting bone
contour topology from the filtered skeletonized images. Our sweeping algorithm decomposes the bone structures
into several relatively simple components and deals with each component separately based on its geometric
characteristics using a sweeping strategy. Utilizing the presegmentation, we construct a graph to model the bone
topology and apply an optimal graph search algorithm to optimize the segmentation results (with respect to
our cost function defined on the bone boundaries). Our segmented results match well with the manual tracing
results by radiologists. Our segmentation approach can be a valuable tool for assisting radiologists and X-ray
technologists in clinical practice and training.
Thrombus development in mouse mesenteric vessels following laser-induced injury was monitored by high-resolution, near-real-time, two-photon, intravital microscopy. In addition to the use of fluorescently tagged fibrin(ogen) and platelets, plasma was labeled with fluorescently tagged dextran. Because blood cells exclude the dextran in the single plane, blood cells appear as black silhouettes. Thus, in addition to monitoring the accumulation of platelets and fibrin in the thrombus, the protocol detects the movement and incorporation of unlabeled cells in and around it. The developing thrombus perturbs the blood flow near the thrombus surface, which affects the incorporation of platelets and blood cells into the structure. The hemodynamic effects and incorporation of blood cells lead to the development of thrombi with heterogeneous domain structures. Additionally, image processing algorithms and simulations were used to quantify structural features of developing thrombi. This analysis suggests a novel mechanism to stop the growth of developing thrombus.
Segmenting objects with complicated topologies in 3D images is a challenging problem in medical image processing, especially for objects with multiple interrelated surfaces. In this paper, we extend a graph search based technique to simultaneously identifying multiple interrelated surfaces for objects that have complex topologies (e.g., with tree-like structures) in 3D. We first perform a pre-segmentation on the input image to obtain basic information of the objects' topologies. Based on the initial pre-segmentation, the original image is resampled along judiciously determined directions to produce a set of vectors of voxels (called voxel columns). The resampling process utilizes medial axes to ensure that voxel columns of appropriate lengths are used to capture the sought object surfaces. Then a geometric graph is constructed whose edges connect voxels in the resampled voxel columns and enforce the smoothness constraint and separation constraint on the sought surfaces. Validation of our algorithm was performed on the segmentation of airway trees and lung vascular trees in human in-vivo CT scans. Cost functions with directional information are applied to distinguish the airway inner wall and outer wall. We succeed in extracting the outer airway wall and optimizing the location of the inner wall in all cases, while the vascular trees are optimized as well. Comparing with the pre-segmentation results, our approach captures the wall surfaces more accurately, especially across bifurcations. The statistical evaluation on a double wall phantom derived from in-vivo CT images yields highly accurate results of the wall thickness measurement on the whole tree (with mean unsigned error 0.16 ± 0.16mm).