A central problem in the development of a mass-screening tool for atherosclerotic plaque is automatic calcification detection.
The mass-screening aspect implies that the detection process should be fast and reliable. In this paper we present a
first step in this direction by introducing a semi-automatic calcification classification tool based on non-linear stretching, an
image enhancement method that focusses on local image statistics. The calcified areas are approximated by a coarse brush,
which in our case is mimicked by taking the ground truth, provided by radiologists, and dilating it with circular structuring
elements of varying sizes. Thresholds are then examined for the different structuring elements, that yield optimal results
on the enhanced image. The results in this preliminary study which contains 19 images of varying calcification degree,
fully annotated by medical experts, show a significant increase in accuracy when the methodology is validated on a region
of interest containing the areas of a simulated coarse brush.
In this paper we seek to improve the standard method of assessing the degree of calcification in the lumbar aorta visualized on lateral 2-D X-rays. The semiquantitative method does not take density of calcification within the individual plaques into account and is unable to measure subtle changes in the severity of calcification over time. Both of these parameters would be desirable to assess, since they are the keys to assessing important information on the impact of risk factors and candidate drugs aiming at the prevention of atherosclerosis. As a further step for solving this task, we propose a pixelwise inpainting-based refinement scheme that seeks to optimize the individual plaque shape by maximizing the signal-to-noise ratio. Contrary to previous work the algorithm developped for this study uses a sorted candidate list, which omits possible bias introduced by the choice of starting pixel. The signal-to-noise optimization scheme will be discussed in different settings using TV as well as Harmonic inpainting and comparing these with a simple averaging process.
In this paper we seek to improve upon the standard method of assessing the degree of calcification in the lumbar aorta, which is commonly used on lateral 2-D x-rays. The necessity for improvement arises from the fact that the existing method can not measure subtle progressions in the plaque development; neither is it possible to express the density of individual plaques. Both of these qualities would be desireable to assess, since they are the key for making progression studies as well as for testing the effect of drugs in longitudinal studies. Our approach is based on inpainting, a technique used in image restoration as well as postprocessing of film. In this study we discuss the potential implications of total variation inpainting for characterizing aortic calcification.
This paper is one of the first steps towards the development of a mass-screening tool, well-suited for quantizing the extend of calcific deposits in the lumbar aorta, which should deliver reliable and easily reproducible data. The major problem is that non-calcified parts of the aorta are not visible on conventional x-ray images. We investigate whether or not it is possible to predict the location of the lumbar aorta, using the first four lumbar vertebrae as prior.
We build a conditional probabilistic model from 90 manually annotated datasets. Using this model we made inferences on the position of the aortic walls given the position and shape of the four vertebrae.
Of particular interest is the performance of the probabilistic model in comparison to the mean aortic shape. Due to the fact that our data set for this particular study only contained 90 hand-annotated images, we evaluated the model using the "leave-one-out" method. The resulting distance from the predicted to the actual aorta was then compared to the distance from the mean aorta to the actual aorta.
The obtained results are encouraging; our conditional model provides results that are up to 38% better than the prediction using only the mean shape, and yields an overlap index of 0.89, whereas the mean shape only produces 0.83.