Diseases like pulmonary embolism and pulmonary hypertension are associated with vascular dystrophy. Identifying
such pulmonary artery/vein (A/V) tree dystrophy in terms of quantitative measures via CT imaging significantly
facilitates early detection of disease or a treatment monitoring process. A tree structure, consisting of nodes and
connected arcs, linked to the volumetric representation allows multi-level geometric and volumetric analysis of A/V
trees. Here, a new theory and method is presented to generate multi-level A/V tree representation of volumetric data
and to compute quantitative measures of A/V tree geometry and topology at various tree hierarchies. The new method
is primarily designed on arc skeleton computation followed by a tree construction based topologic and geometric
analysis of the skeleton. The method starts with a volumetric A/V representation as input and generates its topologic
and multi-level volumetric tree representations long with different multi-level morphometric measures. A new
recursive merging and pruning algorithms are introduced to detect bad junctions and noisy branches often associated
with digital geometric and topologic analysis. Also, a new notion of shortest axial path is introduced to improve the
skeletal arc joining two junctions. The accuracy of the multi-level tree analysis algorithm has been evaluated using
computer generated phantoms and pulmonary CT images of a pig vessel cast phantom while the reproducibility of
method is evaluated using multi-user A/V separation of in vivo contrast-enhanced CT images of a pig lung at different
respiratory volumes.
Distinguishing pulmonary arterial and venous (A/V) trees via in vivo imaging is a critical first step in the
quantification of vascular geometry for purposes of determining, for instance, pulmonary hypertension, detection of
pulmonary emboli and more. A multi-scale topo-morphologic opening algorithm has recently been introduced by us
separating A/V trees in pulmonary multiple-detector X-ray computed tomography (MDCT) images without contrast.
The method starts with two sets of seeds - one for each of A/V trees and combines fuzzy distance transform, fuzzy
connectivity, and morphologic reconstruction leading to multi-scale opening of two mutually fused structures while
preserving their continuity. The method locally determines the optimum morphological scale separating the two
structures. Here, a validation study is reported examining accuracy of the method using mathematically generated
phantoms with different levels of fuzziness, overlap, scale, resolution, noise, and geometric coupling and MDCT
images of pulmonary vessel casting of pigs. After exsanguinating the animal, a vessel cast was generated using
rapid-hardening methyl methacrylate compound with additional contrast by 10cc of Ethiodol in the arterial side
which was scanned in a MDCT scanner at 0.5mm slice thickness and 0.47mm in plane resolution. True
segmentations of A/V trees were computed from these images by thresholding. Subsequently, effects of
distinguishing A/V contrasts were eliminated and resulting images were used for A/V separation by our method.
Experimental results show that 92% - 98% accuracy is achieved using only one seed for each object in phantoms
while 94.4% accuracy is achieved in MDCT cast images using ten seeds for each of A/V trees.
Distinguishing arterial and venous trees in pulmonary multiple-detector X-ray computed tomography (MDCT) images
(contrast-enhanced or unenhanced) is a critical first step in the quantification of vascular geometry for purposes of
determining, for instance, pulmonary hypertension, using vascular dimensions as a comparator for assessment of airway
size, detection of pulmonary emboli and more. Here, a novel method is reported for separating arteries and veins in
MDCT pulmonary images. Arteries and veins are modeled as two iso-intensity objects closely entwined with each other
at different locations at various scales. The method starts with two sets of seeds -- one for arteries and another for veins.
Initialized with seeds, arteries and veins grow iteratively while maintaining their spatial separation and eventually
forming two disjoint objects at convergence. The method combines fuzzy distance transform, a morphologic feature,
with a topologic connectivity property to iteratively separate finer and finer details starting at a large scale and
progressing towards smaller scales. The method has been validated in mathematically generated tubular objects with
different levels of fuzziness, scale and noise. Also, it has been successfully applied to clinical CT pulmonary data. The
accuracy of the method has been quantitatively evaluated by comparing its results with manual outlining. For arteries,
the method has yielded correctness of 81.7% at the cost of 6.7% false positives and 11.6% false negatives. Our method is
very promising for automated separation of arteries and veins in MDCT pulmonary images even when there is no mark
of intensity variation at conjoining locations.
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