Pulmonary fissures are important landmarks for automated recognition of lung anatomy and need to be detected
as a pre-processing step. We propose a derivative of stick (DoS) filter for pulmonary fissures detection in
thoracic CT scans by considering their thin curvilinear shape across multiple transverse planes. Based on a stick
decomposition of a local rectangular neighborhood, a nonlinear derivative operator perpendicular to each stick
is defined. Then, combining with a standard deviation of the intensity along the stick, the composed likelihood
function will take a strong response to fissure-like bright lines, and tends to suppress undesired structures
including large vessels, step edges and blobs. Applying the 2D filter sequentially to the sagittal, coronal and
axial slices, an approximate 3D co-planar constraint is implicitly exerted through the cascaded pipeline, which
helps to further eliminate non-fissure tissues. To generate a clear fissure segmentation, we adopt a connected
component based post-processing scheme, combined with a branch-point finding algorithm to disconnect the
residual adjacent clutters from the fissures. The performance of our filter has been verified in experiments with
a 23 patients dataset, where pathologies to different extents are included. The DoS filter compared favorably
with prior algorithms.
We present an automatic lung lobe segmentation algorithm for COPD patients. The method enhances fissures,
removes unlikely fissure candidates, after which a B-spline is fitted iteratively through the remaining candidate
objects. The iterative fitting approach circumvents the need to classify each object as being part of the fissure
or being noise, and allows the fissure to be detected in multiple disconnected parts. This property is beneficial
for good performance in patient data, containing incomplete and disease-affected fissures.
The proposed algorithm is tested on 22 COPD patients, resulting in accurate lobe-based densitometry, and a
median overlap of the fissure (defined 3 voxels wide) with an expert ground truth of 0.65, 0.54 and 0.44 for the
three main fissures. This compares to complete lobe overlaps of 0.99, 0.98, 0.98, 0.97 and 0.87 for the five main
lobes, showing promise for lobe segmentation on data of patients with moderate to severe COPD.
In this paper, a structured light system based on synchronous scanning technology is developed for meeting the need of body surface acquisition. The proposed system is composed of a
fixed CCD camera, a fixed structured light projector and a mirror scanner. While the mirror is reflecting light stripes and scene images, the camera acquires a series of body section images from the scanner. After extracting the trace of laser stripes and calculating the relative 3-D coordinate of the illuminated pixels on the series of CCD images, the system can acquire the spatial profile of the
inspected body surface. Moreover, a prototype is developed according to the results from geometrical analysis mentioned above. The experiment data obtained from the scanning system are shown. This synchronized scanning system can be widely applied in the custom design, surgery navigation and the other optical measurement field in the future.
This paper presents a novel method for speckle reduction in ultrasonic images. Firstly, a particular filtering kernel is defined by decomposing the local rectangular neighborhood into asymmetric sticks pointing outside with variable orientation from the investigated pixel. Then the local mean and variance along each stick are calculated using a template based convolution algorithm. Finally, a pseudo-diffusion model is derived to diffuse the intensity averages of sticks into the central pixel, and a variance sensitive conductance functions is designed to adaptively control the diffusion strength in varying directions. The proposed method is in essence an integration of the linear boundary detection operator, i.e. stick technique, and the nonlinear diffusion model. In homogeneous regions, our method will act as a Gaussian like low pass filter, since the sticks are partially overlapped near the center, which implicitly assigns distance dependent weights to neighboring pixels. In heterogeneous regions, the information is expressed as many structures, which often occur as line boundaries or tube shapes in ultrasonic images, then our approach can encourage smoothing along the sticks falling inside the structures, and penalize blurring along the sticks across edges. The performance of our method is verified in experiments of both synthetic and clinical ultrasonic images. The results show that our method outperforms the existed filtering techniques in term of smoothing homogeneous regions, preserving resolvable features, enhancing weak edges and linear structures.