In this study we present a computational method of CT examination classification into visual assessed
emphysema severity. The visual severity categories ranged from 0 to 5 and were rated by an experienced
radiologist. The six categories were none, trace, mild, moderate, severe and very severe. Lung segmentation
was performed for every input image and all image features are extracted from the segmented lung only. We
adopted a two-level feature representation method for the classification. Five gray level distribution statistics,
six gray level co-occurrence matrix (GLCM), and eleven gray level run-length (GLRL) features were
computed for each CT image depicted segment lung. Then we used wavelets decomposition to obtain the
low- and high-frequency components of the input image, and again extract from the lung region six GLCM
features and eleven GLRL features. Therefore our feature vector length is 56. The CT examinations were
classified using the support vector machine (SVM) and k-nearest neighbors (KNN) and the traditional
threshold (density mask) approach. The SVM classifier had the highest classification performance of all the
methods with an overall sensitivity of 54.4% and a 69.6% sensitivity to discriminate "no" and "trace visually
assessed emphysema. We believe this work may lead to an automated, objective method to categorically
classify emphysema severity on CT exam.
Onboard CBCT for radiation linear accelerators suffers from limited longitudinal coverage and various image quality
problems, especially at wider cone angles. Such problems prevent CBCT being applied in full potentials for many
clinical cancer sites, including head-neck, and for many quantitative applications, including tumor response evaluation
and daily radiation dose computation. We propose to use CBCT with flexible X-ray source trajectories to overcome
these limitations. The core idea is to combine gantry rotation with simultaneous couch motion. Longitudinal coverage
can therefore be extended without limitation. Image quality can be enhanced by applying advanced exact CBCT
reconstruction algorithm. However, unlike diagnostic CT where helical CBCT is widely used, LINAC onboard CBCT
because gantry can only rotate within 360 degrees and couch table cannot move during gantry rotation. To solve the
hardware problem, we program the new Varian TrueBeam LINAC machine in developer mode to realize simultaneous
gantry and couch motion so to simulate any flexible scan trajectories. We also implemented CBCT simulation
algorithms with digital phantoms to support any flexible source trajectories. We implemented and improved Katsevich
exact reconstruction algorithm for image reconstruction from projection data obtained in phantom simulations. We have
studied a few different source trajectory models including double circle, helical and saddle. The initial digital phantom
results were encouraging. The longitudinal coverage was extended. Image quality has been improved using Katsevich
reconstruction algorithm. Physics phantom studies on TrueBeam LINAC machine is our next step.
Quantitative computed tomography (CT) has been widely used to detect and evaluate the presence (or absence) of
emphysema applying the density masks at specific thresholds, e.g., -910 or -950 Hounsfield Unit (HU). However, it has
also been observed that subjects with similar density-mask based emphysema scores could have varying lung function,
possibly indicating differences of disease severity. To assess this possible discrepancy, we investigated whether density
distribution of "viable" lung parenchyma regions with pixel values > -910 HU correlates with lung function. A dataset of
38 subjects, who underwent both pulmonary function testing and CT examinations in a COPD SCCOR study, was
assembled. After the lung regions depicted on CT images were automatically segmented by a computerized scheme, we
systematically divided the lung parenchyma into different density groups (bins) and computed a number of statistical
features (i.e., mean, standard deviation (STD), skewness of the pixel value distributions) in these density bins. We then
analyzed the correlations between each feature and lung function. The correlation between diffusion lung capacity
(DLCO) and STD of pixel values in the bin of -910HU ≤ PV < -750HU was -0.43, as compared with a correlation
of -0.49 obtained between the post-bronchodilator ratio (FEV1/FVC) measured by the forced expiratory volume in 1
second (FEV1) dividing the forced vital capacity (FVC) and the STD of pixel values in the bin of
-1024HU ≤ PV < -910HU. The results showed an association between the distribution of pixel values in "viable"
lung parenchyma and lung function, which indicates that similar to the conventional density mask method, the pixel
value distribution features in "viable" lung parenchyma areas may also provide clinically useful information to improve
assessments of lung disease severity as measured by lung functional tests.
This paper describes a non-linear medical image registration algorithm that aligns lung CT images scanned at
different respiratory phases. The method uses landmarks obtained from the airway tree to find the airway
branch extension lines and where the lines intersect the lung surface. The branch extension and lung
intersection voxels on the surface were the crucial landmarks that initialize the non-rigid registration process.
The advantage of these landmarks is that they have high correspondence between the matching patterns in the
template images and deformed images. This method was developed and tested on CT examinations from
participants in an asthma study. The registration accuracy was evaluated by the average distance between the
corresponding airway tree branch points in the pair of images. The mean value of the distance between
landmarks in template images and deformed matching images for subjects 1 and 2 were 8.44 mm (±4.46 mm)
and 4.33 mm (± 3.78 mm), respectively. The results show that the lung image registration technique
developed in this study may prove useful in quantifying longitudinal changes, performing regional analysis,
tracking lung tumors, and compensating for subject motion across CT images.
In this study we present a texture-based method of emphysema segmentation depicted on CT examination consisting of
two steps. Step 1, a fractal dimension based texture feature extraction is used to initially detect base regions of
emphysema. A threshold is applied to the texture result image to obtain initial base regions. Step 2, the base regions are
evaluated pixel-by-pixel using a method that considers the variance change incurred by adding a pixel to the base in an
effort to refine the boundary of the base regions. Visual inspection revealed a reasonable segmentation of the emphysema
regions. There was a strong correlation between lung function (FEV1%, FEV1/FVC, and DLCO%) and fraction of
emphysema computed using the texture based method, which were -0.433, -.629, and -0.527, respectively. The texture-based
method produced more homogeneous emphysematous regions compared to simple thresholding, especially for
large bulla, which can appear as speckled regions in the threshold approach. In the texture-based method, single isolated
pixels may be considered as emphysema only if neighboring pixels meet certain criteria, which support the idea that
single isolated pixels may not be sufficient evidence that emphysema is present. One of the strength of our complex
texture-based approach to emphysema segmentation is that it goes beyond existing approaches that typically extract a
single or groups texture features and individually analyze the features. We focus on first identifying potential regions of
emphysema and then refining the boundary of the detected regions based on texture patterns.
In this study, an efficient computational geometry approach is introduced to segment pulmonary nodules. The
basic idea is to estimate the three-dimensional surface of a nodule in question by analyzing the shape characteristics of
its surrounding tissues in geometric space. Given a seed point or a specific location where a suspicious nodule may be,
three steps are involved in this approach. First, a sub-volume centered at this seed point is extracted and the contained
anatomy structures are modeled in the form of a triangle mesh surface. Second, a "visibility" test combined with a shape
classification algorithm based on principal curvature analysis removes surfaces determined not to belong to nodule
boundaries by specific rules. This step results in a partial surface of a nodule boundary. Third, an interpolation /
extrapolation based shape reconstruction procedure is used to estimate a complete nodule surface by representing the
partial surface as an implicit function. The preliminary experiments on 158 annotated CT examinations demonstrated
that this scheme could achieve a reasonable performance in nodule segmentation.
We have developed and preliminarily tested a new breast cancer risk prediction model based on computerized
bilateral mammographic tissue asymmetry. In this study, we investigated and compared the performance difference of
our risk prediction model when the bilateral mammographic tissue asymmetrical features were extracted in two different
methods namely (1) the entire breast area and (2) the mirror-matched local strips between the left and right breast. A
testing dataset including bilateral craniocaudal (CC) view images of 100 negative and 100 positive cases for developing
breast abnormalities or cancer was selected from a large and diverse full-field digital mammography (FFDM) image
database. To detect bilateral mammographic tissue asymmetry, a set of 20 initial "global" features were extracted from
the entire breast areas of two bilateral mammograms in CC view and their differences were computed. Meanwhile, a
pool of 16 local histogram-based statistic features was computed from eight mirror-matched strips between the left and
right breast. Using a genetic algorithm (GA) to select optimal features, two artificial neural networks (ANN) were built
to predict the risk of a test case developing cancer. Using the leave-one-case-out training and testing method, two GAoptimized
ANNs yielded the areas under receiver operating characteristic (ROC) curves of 0.754±0.024 (using feature
differences extracted from the entire breast area) and 0.726±0.026 (using the feature differences extracted from 8 pairs of
local strips), respectively. The risk prediction model using either ANN is able to detect 58.3% (35/60) of cancer cases 6
to 18 months earlier at 80% specificity level. This study compared two methods to compute bilateral mammographic
tissue asymmetry and demonstrated that bilateral mammographic tissue asymmetry was a useful breast cancer risk
indicator with high discriminatory power.
Visually searching for analyzable metaphase chromosome cells under microscopes is a routine and timeconsuming
task in genetic laboratories to diagnose cancer and genetic disorders. To improve detection efficiency,
consistency, and accuracy, we developed an automated microscopic image scanning system using a 100X oil immersion
objective lens to acquire images that has sufficient spatial resolution allowing clinicians to do diagnosis. Due to the highresolution,
the field of image depth is very limited and multiple scans up to seven layers are required. Thus, a metaphase
cell can spread over multiple images at different focal levels. Among them only one or two are adequate for the
diagnosis and the others are typically fuzzy images. In this study, we developed and tested a computer-aided detection
(CAD) scheme to automatically select one image with the sharpest image quality and discard all of the other fuzzy
images based on the computed sharpness index. From three scanned bone marrow specimen slides, the on-line and offline
metaphase finding modules automatically selected 100 chromosome cells with 534 images. These images were
selected to build a testing dataset. For each cell, the CAD scheme selects one image with the maximum sharpness index.
Three observers also independently visually selected one best image for diagnosis from each cell. The agreement rate
between CAD and visually selected images ranges from 89% to 96%, which is also very comparable to the agreement
rate between the two observers. This experiment demonstrated the feasibility of applying a CAD scheme to select the
images with sharpest high-resolution metaphase chromosome cell and potentially improve diagnostic efficiency and
accuracy in the future clinical practice.
Although 3-D airway tree segmentation permits analysis of airway tree paths of practical lengths and facilitates
visual inspection, our group developed and tested an automated computer scheme that was operated on individual 2-D
CT images to detect airway sections and measure their morphometry and/or dimensions. The algorithm computes a set
of airway features including airway lumen area (Ai), airway cross-sectional area (Aw), the ratio (Ra) of Ai to Aw, and the
airway wall thickness (Tw) for each detected airway section depicted on the CT image slice. Thus, this 2-D based
algorithm does not depend on the accuracy of 3-D airway tree segmentation and does not require that CT examination
encompasses the entire lung or reconstructs contiguous images. However, one disadvantage of the 2-D image based
schemes is the lack of the ability to identify the airway generation (Gb) of the detected airway section. In this study, we
developed and tested a new approach that uses 2-D airway features to assign a generation number to an airway. We
developed and tested two probabilistic neural networks (PNN) based on different sets of airway features computed by
our 2-D based scheme. The PNNs were trained and tested on 12 lung CT examinations (8 training and 4 testing). The
accuracy for the PNN that utilized Ai and Ra for identifying the generation of airway sections varies from 55.4% - 100%.
The overall accuracy of the PNN for all detected airway sections that are spread over all generations is 76.7%.
Interestingly, adding wall thickness feature (Tw) to PNN did not improve identification accuracy. This preliminary study
demonstrates that a set of 2-D airway features may be used to identify the generation number of an airway with
reasonable accuracy.
An interactive computer-aided detection or diagnosis (ICAD) scheme allows observers to query suspicious
abnormalities (lesions) depicted on medical images. Once a suspicious region is queried, ICAD segments the abnormal
region, computes a set of image features, searches for and identifies the reference regions depicted on the verified lesions
that are similar to the queried one. Based on the distribution of the selected similar regions, ICAD generates a detection
(or classification) score of the queried region depicting true-positive disease. In this study, we assessed the performance
and reliability of an ICAD scheme when using a database including total 1500 positive images depicted verified breast
masses and 1500 negative images depicted ICAD-cued false-positive regions as well as the leave-one-out testing method.
We conducted two experiments. In the first experiment, we tested the relationship between ICAD performance and the
size of reference database by systematically increasing the size of reference database from 200 to 3000 images. In the
second experiment, we tested the relationship between ICAD performance and the similarity level between the queried
image and the retrieved similar references by applying a set of thresholds to systematically remove the queried images
whose similarity level to their most "similar" reference images are lower than threshold. The performance was compared
based on the areas under ROC curves (AUC). The results showed that (1) as the increase of reference database, AUC
value monotonically increased from 0.636±0.041 to 0.854±0.004 and (2) as the increase of similarity threshold values,
AUC value also monotonically increased from 0.854±0.004 to 0.932±0.016. The increase of AUC values and the
decrease of their standard deviations indicate the improvement of both CAD performance and reliability. The study
suggested that (1) assembling the large and diverse reference databases and (2) assessing and reporting the reliability of
ICAD-generated results based on the similarity measurement are important in development and application of the ICAD
schemes.
Airways tree segmentation is an important step in quantitatively assessing the severity of and changes in several
lung diseases such as chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis. It can also be used in
guiding bronchoscopy. The purpose of this study is to develop an automated scheme for segmenting the airways tree
structure depicted on chest CT examinations. After lung volume segmentation, the scheme defines the first cylinder-like
volume of interest (VOI) using a series of images depicting the trachea. The scheme then iteratively defines and adds
subsequent VOIs using a region growing algorithm combined with adaptively determined thresholds in order to trace
possible sections of airways located inside the combined VOI in question. The airway tree segmentation process is
automatically terminated after the scheme assesses all defined VOIs in the iteratively assembled VOI list. In this
preliminary study, ten CT examinations with 1.25mm section thickness and two different CT image reconstruction
kernels ("bone" and "standard") were selected and used to test the proposed airways tree segmentation scheme. The
experiment results showed that (1) adopting this approach affectively prevented the scheme from infiltrating into the
parenchyma, (2) the proposed method reasonably accurately segmented the airways trees with lower false positive
identification rate as compared with other previously reported schemes that are based on 2-D image segmentation and
data analyses, and (3) the proposed adaptive, iterative threshold selection method for the region growing step in each
identified VOI enables the scheme to segment the airways trees reliably to the 4th generation in this limited dataset with
successful segmentation up to the 5th generation in a fraction of the airways tree branches.
KEYWORDS: Lung, Chronic obstructive pulmonary disease, Computed tomography, Image segmentation, Gold, Signal attenuation, Tissues, Medium wave, Medicine, In vivo imaging
Computed tomography (CT) examination is often used to quantify the relation between lung function and airway
remodeling in chronic obstructive pulmonary disease (COPD). In this preliminary study, we examined the
association between lung function and airway wall computed attenuation ("density") in 200 COPD screening
subjects. Percent predicted FVC (FVC%), percent predicted FEV1 (FEV1%), and the ratio of FEV1 to FVC as a
percentage (FEV1/FVC%) were measured post-bronchodilator. The apical bronchus of the right upper lobe was
manually selected from CT examinations for evaluation. Total airway area, lumen area, wall area, lumen perimeter
and wall area as fraction of the total airway area were computed. Mean HU (meanHU) and maximum HU (maxHU)
values were computed across pixels assigned membership in the wall and with a HU value greater than -550. The
Pearson correlation coefficients (PCC) between FVC%, FEV1%, and FEV1/FVC% and meanHU were -0.221 (p =
0.002), -0.175 (p = 0.014), and -0.110 (p = 0.123), respectively. The PCCs for maxHU were only significant for
FVC%. The correlations between lung function and the airway morphometry parameters were slightly stronger
compared to airway wall density. MeanHU was significantly correlated with wall area (PCC = 0.720), airway area
(0.498) and wall area percent (0.611). This preliminary work demonstrates that airway wall density is associated
with lung function. Although the correlations in our study were weaker than a recent study, airway wall density
initially appears to be an important parameter in quantitative CT analysis of COPD.
We present a way to fabricate microgrippers that can meet the industry's needs well, i.e., low cost and large tip deflection, etc. The microgripper is fabricated by bonding two identical micro NiTi-Si cantilever beams together with a silicon spacer in between. It can be actuated by electrical current directly. We have tested the behavior of micro NiTi-Si cantilever beams of three different sizes, and compared that with our simulation results. According to our simulation, the maximum strain and the maximum stress in NiTi should enable the grippers to survive after 106 cycles. Due to the simple fabrication process, this design is very suitable for batch production at low cost, which is a significant advantage in both medical and manufacturing industries
In this paper, we present the results of theoretical study of thin film technique based micro grippers. Three most popular actuation mechanisms, namely, piezoelectric, bimetal and shape memory alloy are investigated. First, we present the simulation results against the measured behavior of NiTi shape memory alloy thin film based micro grippers. Then we compare the performances of these three kinds of micro grippers. It shows that shape memory alloy based micro gripper is much better than the others.
In this paper, we present a way to fabricate microgripper that could well meet the industry needs, i.e. low cost, high performance etc. A medium sized microgripper of 1.6 mm in length has been fabricated, tested and simulated. This novel design, with its fabrication process, makes it possible for batch production, which results in lower production cost. Its low cost has unique advantages in both medical and manufacturing industries. For example, in the medical field, the microgripper could be disposed after every use, much like a syringe without imposing excessive costs. Our finite element simulation agrees reasonably well with the measured behavior.
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