We present a method for the automatic delineation of two neuromelanin rich brainstem structures –<i>substantia nigra pars </i><i>compacta</i> (SN) and <i>locus coeruleus </i>(LC)- in neuromelanin sensitive magnetic resonance images of the brain. The segmentation method uses a dynamic multi-image reference atlas and a pre-registration atlas selection strategy. To create the atlas, a pool of 35 images of healthy subjects was pair-wise pre-registered and clustered in groups using an affinity propagation approach. Each group of the atlas is represented by a single exemplar image. Each new target image to be segmented is registered to the exemplars of each cluster. Then all the images of the highest performing clusters are enrolled into the final atlas, and the results of the registration with the target image are propagated using a majority voting approach. All registration processes used combined one two-stage affine and one elastic B-spline algorithm, to account for global positioning, region selection and local anatomic differences. In this paper, we present the algorithm, with emphasis in the atlas selection method and the registration scheme. We evaluate the performance of the atlas selection strategy using 35 healthy subjects and 5 Parkinson’s disease patients. Then, we quantified the volume and contrast ratio of neuromelanin signal of these structures in 47 normal subjects and 40 Parkinson’s disease patients to confirm that this method can detect neuromelanin-containing neurons loss in Parkinson’s disease patients and could eventually be used for the early detection of SN and LC damage.
Accurate quantification of vessel diameter in low-dose Computer Tomography (CT) images is important to study pulmonary diseases, in particular for the diagnosis of vascular diseases and the characterization of morphological vascular remodeling in Chronic Obstructive Pulmonary Disease (COPD). In this study, we objectively compare several vessel diameter estimation methods using a physical phantom. Five solid tubes of differing diameters (from 0.898 to 3.980 mm) were embedded in foam, simulating vessels in the lungs. To measure the diameters, we first extracted the vessels using either of two approaches: vessel enhancement using multi-scale Hessian matrix computation, or explicitly segmenting them using intensity threshold. We implemented six methods to quantify the diameter: three estimating diameter as a function of scale used to calculate the Hessian matrix; two calculating equivalent diameter from the crosssection area obtained by thresholding the intensity and vesselness response, respectively; and finally, estimating the diameter of the object using the Full Width Half Maximum (FWHM). We find that the accuracy of frequently used methods estimating vessel diameter from the multi-scale vesselness filter depends on the range and the number of scales used. Moreover, these methods still yield a significant error margin on the challenging estimation of the smallest diameter (on the order or below the size of the CT point spread function). Obviously, the performance of the thresholding-based methods depends on the value of the threshold. Finally, we observe that a simple adaptive thresholding approach can achieve a robust and accurate estimation of the smallest vessels diameter.
Emphysema is associated with the destruction of lung parenchyma, resulting in abnormal enlargement of airspaces. Accurate quantification of emphysema is required for a better understanding of the disease as well as for the assessment of drugs and treatments. In the present study, a novel method for emphysema characterization from histological lung images is proposed. Elastase-induced mice were used to simulate the effect of emphysema on the lungs. A database composed of 50 normal and 50 emphysematous lung patches of size 512 x 512 pixels was used in our experiments. The purpose is to automatically identify those patches containing emphysematous tissue. The proposed approach is based on the use of granulometry analysis, which provides the pattern spectrum describing the distribution of airspaces in the lung region under evaluation. The profile of the spectrum was summarized by a set of statistical features. A logistic regression model was then used to estimate the probability for a patch to be emphysematous from this feature set. An accuracy of 87% was achieved by our method in the classification between normal and emphysematous samples. This result shows the utility of our granulometry-based method to quantify the lesions due to emphysema.
In this study, we quantitatively characterize lung airway remodeling caused by smoking-related emphysema and Chronic
Obstructive Pulmonary Disease (COPD), in low-dose CT scans. To that end, we established three groups of individuals:
subjects with COPD (n=35), subjects with emphysema (n=38) and healthy smokers (n=28). All individuals underwent a
low-dose CT scan, and the images were analyzed as described next. First the lung airways were segmented using a fast
marching method and labeled according to its generation. Along each airway segment, cross-section images were
resampled orthogonal to the airway axis. Next 128 rays were cast from the center of the airway lumen in each crosssection
slice. Finally, we used an integral-based method, to measure lumen radius, wall thickness, mean wall percentage
and mean peak wall attenuation on every cast ray. Our analysis shows that both the mean global wall thickness and the
lumen radius of the airways of both COPD and emphysema groups were significantly different from those of the healthy
group. In addition, the wall thickness change starts at the 3<sup>rd</sup> airway generation in the COPD patients compared with
emphysema patients, who display the first significant changes starting in the 2<sup>nd</sup> generation. In conclusion, it is shown
that airway remodeling happens in individuals suffering from either COPD or emphysema, with some local difference
between both groups, and that we are able to detect and accurately quantify this process using images of low-dose CT
In this paper, we explore the use of anatomical information as a guide in the image formation
process of fluorescence molecular tomography (FMT). Namely, anatomical knowledge obtained
from high resolution computed tomography (micro-CT) is used to construct a model for the
diffusion of light and to constrain the reconstruction to areas candidate to contain fluorescent
volumes. Moreover, a sparse regularization term is added to the state-of-the-art least square
solution to contribute to the sparsity of the localization. We present results showing the increase
in accuracy of the combined system over conventional FMT, for a simulated experiment of lung
cancer detection in mice.
Proc. SPIE. 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging
KEYWORDS: Image processing algorithms and systems, Signal to noise ratio, Image segmentation, Wavefronts, Image analysis, Lung, Wave propagation, Computed tomography, Reconstruction algorithms, In vivo imaging
Mouse models are becoming instrumental for the study of lung disease. Due to its resolution and low cost, high resolution Computed Tomography (micro-CT) is a very adequate technology to visualize the mouse lungs in-vivo. Automatic segmentation and measurement of airways in micro-CT images of the lungs can be useful as a preliminary step prior other image analysis quantification tasks, as well as for the study of pathologies that alter the airways structure. In this paper, we present an efficient segmentation and reconstruction algorithm which simultaneously segments and reconstructs the bronchial tree, while providing the length and mean radius of each airway segment. A locally adaptive intensity threshold is used to account for the low signal to noise ratio and strong artifacts present in micro-CT images. We validate our method by comparing it with manual segmentations of 10 different scans, obtaining an average true positive volume fraction of 85.52% with a false positive volume fraction of 5.04%.
Analytically predicting photon paths in real-high
scattering-anisotropic tissues is extremely complex, due to
the significant random scattering events that photons suffer as they traverse the tissue, especially at
boundaries between areas with different optical properties. An statistically correct optical and anatomical
model of photon trajectories inside laboratory animals will therefore improve considerably our understanding
about how light diffuses within the animals, and therefore help us designing efficient experimental setups and
reconstruction algorithms for fluorescence mediated tomography (FMT). Here, we present new simulations of
photon propagation and fluorescence emission in anisotropic media using realistic models of laboratory
animals and a Monte Carlo (MC) based approach. We compare the MC simulation results with an
approximation of the solution of the diffusion equation using finite differences and discuss the different
behaviour of the two methods.
Atlas-based segmentation has proven effective in multiple applications. Usually, several reference images are combined
to create a representative average atlas image. Alternatively, a number of independent atlas images can be used, from which multiple segmentations of the image of interest are derived and later combined. One of the major drawbacks of this approach is its large computational burden caused by the high number of required registrations. To address this problem, we introduce One Registration, Multiple Segmentations (ORMS), a procedure to obtain multiple segmentations with a single online registration. This can be achieved by pre-computing intermediate transformations from the initial atlas images to an average image. We show that, compared to the usual approach, our method reduces time considerably
with little or no loss in accuracy. On the other hand, optimum combination of these segmentations remains an unresolved problem. Different approaches have been adopted, but they are all far from the upper bound of any combination strategy. This is given by the
Combination Oracle, which classifies a voxel correctly if any individual segmentation coincides with the ground truth.
We present here a novel combination approach, based on weighting the different segmentations according to the mutual information between the test image and the atlas image after registration. We compare this method with other existing combination strategies using microscopic MR images of mouse brains, achieving statistically significant improvement in segmentation accuracy.
The Continuous Wavelet Transform (CWT) is an effective way to analyze nonstationary signals and to localize and characterize singularities. Fast algorithms have already been developed to compute the CWT at integer time points and dyadic or integer scales. We propose here a new method that is based on a B-spline expansion of both the signal and the analysis wavelet and that allows the CWT computation at arbitrary scales. Its complexity is O(N), where N represents the size of the input signal; in other words, the cost is independent of the scale factor. Moreover, the algorithm lends itself well to a parallel implementation.
We propose to design the reduction operator of an image pyramid so as to minimize the approximation error in the l<SUB>p</SUB> sense where p can take non-integer values. The underlying image model is specified using arbitrary shift- invariant basis functions such as splines. The solution is determined by an iterative optimization algorithm, based on digital filtering. Its convergence is accelerated by the use of first and second derivatives. For p equals 1, our modified pyramid is robust to outliers; edges are preserved better than in the standard case where p equals 2. For 1 < p < 2, the pyramid decomposition combines the qualities of l<SUB>1</SUB> and l<SUB>2</SUB> approximations. The method is applied to edge detection and its improved performance over the standard formulation is determined.