Proc. SPIE. 7260, Medical Imaging 2009: Computer-Aided Diagnosis
KEYWORDS: Visual analytics, Visualization, Magnetic resonance imaging, Image segmentation, Magnetism, 3D modeling, 3D metrology, Motion models, 3D image processing, Cardiovascular magnetic resonance imaging
The aim of our work is to present a robust 3D automated method for measuring regional myocardial thickening using cardiac magnetic resonance imaging (MRI) based on Laplace's equation. Multiple slices of the myocardium in short-axis orientation at end-diastolic and end-systolic phases were considered for this analysis. Automatically assigned 3D epicardial and endocardial boundaries were fitted
to short-axis and long axis slices corrected for breathold related misregistration, and final boundaries were edited by a cardiologist if required. Myocardial thickness was quantified at the two cardiac phases by computing the distances between the myocardial boundaries over the entire volume using Laplace's equation. The distance between the surfaces was found by computing normalized gradients that form a
vector field. The vector fields represent tangent vectors along field lines connecting both boundaries. 3D thickening measurements were transformed into polar map representation and 17-segment model
(American Heart Association) regional thickening values were derived. The thickening results were then compared with standard 17-segment 6-point visual scoring of wall motion/wall thickening (0=normal;
5=greatest abnormality) performed by a consensus of two experienced imaging cardiologists. Preliminary results on eight subjects indicated a strong negative correlation (r=-0.8, p<0.0001) between the average thickening obtained using Laplace and the summed segmental visual scores. Additionally, quantitative
ejection fraction measurements also correlated well with average thickening scores (r=0.72, p<0.0001). For
segmental analysis, we obtained an overall correlation of -0.55 (p<0.0001) with higher agreement along the
mid and apical regions (r=-0.6). In conclusion 3D Laplace transform can be used to quantify myocardial thickening in 3D.
One of the key measures of response to treatment for patients in multicenter clinical trials is the lung density measured in Hounsfield Units (HU) from Computer Tomography (CT) scans. The purpose of this work is to determine the dependence of CT attenuation values on scanner type by using in vivo measurements made from homogeneous anatomic areas. In vivo measurements were made in areas within the trachea, aorta, fat and muscle regions of CT scans obtained from subjects scanned as part of a multicenter treatment trial. Scans were selected so that exams from all four major manufacturers were included in the study. For each anatomic region of interest, the mean and standard deviation values were computed to investigate attenuation dependence on scanners. For example, trachea mean (standard deviation) measurements for exams from GE, Siemens, Philips and Toshiba scanners were -986 HU(±15), - 993 HU(±9), -988HU(±8), -1046(±10) respectively. Inter-scanner variability was observed for each scanner showing significant differences (all p-values <0.005). Previous work in examining attenuation dependence on scanners has been performed using anthropomorphic phantoms. The novelty of this work is the use of in vivo measurements from homogeneous regions in order to examine scanner effects on CT attenuation values. Our results show that CT attenuation values for the anatomic regions vary between scanners and hence, dependence of CT attenuation values on scanners is observed.
Segmentation of lungs in the setting of scleroderma is a major challenge in medical image analysis.
Threshold based techniques tend to leave out lung regions that have increased attenuation, for example in
the presence of interstitial lung disease or in noisy low dose CT scans. The purpose of this work is to
perform segmentation of the lungs using a technique that selects an optimal threshold for a given
scleroderma patient by comparing the curvature of the lung boundary to that of the ribs. Our approach is
based on adaptive thresholding and it tries to exploit the fact that the curvature of the ribs and the curvature
of the lung boundary are closely matched. At first, the ribs are segmented and a polynomial is used to
represent the ribs' curvature. A threshold value to segment the lungs is selected iteratively such that the
deviation of the lung boundary from the polynomial is minimized. A Naive Bayes classifier is used to build
the model for selection of the best fitting lung boundary. The performance of the new technique was
compared against a standard approach using a simple fixed threshold of -400HU followed by regiongrowing.
The two techniques were evaluated against manual reference segmentations using a volumetric
overlap fraction (VOF) and the adaptive threshold technique was found to be significantly better than the
fixed threshold technique.
Emphysema is a common chronic respiratory disorder characterized by the destruction of lung tissue. Labelling
lung images containing Emphysema is a tedious and time consuming process and detection using fewer labelled
examples would be very useful. We have recently developed an automated texture-based system capable of
achieving varying levels of Emphysema detection in High Resolution Computed Tomography (HRCT) images
using co-training . Co-training is a semi-supervised technique used to improve classifiers trained with very
few labelled examples using a large pool of unseen examples. In this paper, we show how we can use examples
labelled by experts within the same system but in an incremental manner. We show that through the use of two
views in our system, one can detect the most informative examples and through the use of labelled examples,
one can provide class labels to those examples. When the two views disagree about the class label of an
example, we feed the example together with the correct class label provided by the expert’s marking to the
system in order to improve its performance. Results show that when images labelled by experts are incorporated
into the system at early iterations, the performance of the system compared to the earlier system improves. The
results were also compared against "density mask", a standard approach used for Emphysema detection in
medical image analysis. In addition, radiologists have verified the results and concluded that the classifiers built
at different iterations can be used for different levels of Emphysema diagnosis.