Toward automated detection and segmentation of aortic calcifications from radiographs (original) (raw)
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Toward automated detection and segmentation of aortic calcifications from radiographs
SPIE Proceedings, 2007
This paper aims at automatically measuring the extent of calcified plaques in the lumbar aorta from standard radiographs. Calcifications in the abdominal aorta are an important predictor for future cardiovascular morbidity and mortality. Accurate and reproducible measurement of the amount of calcified deposit in the aorta is therefore of great value in disease diagnosis and prognosis, treatment planning, and the study of drug effects. We propose a two-step approach in which first the calcifications are detected by an iterative statistical pixel classification scheme combined with aorta shape model optimization. Subsequently, the detected calcified pixels are used as the initialization for an inpainting based segmentation. We present results on synthetic images from the inpainting based segmentation as well as results on several X-ray images based on the two-steps approach.
Automatic detection of calcifications in the aorta from CT scans of the abdomen1
Academic Radiology, 2004
An automatic method to detect calcifications in the aorta from CT scans of the abdomen is presented. Candidate objects are extracted by gray level thresholding. For each candidate object, a number of shape, spatial and gray level features are calculated. Based on those features, classification of candidate objects into calcifications and non-calcifications is performed in two stages. In the first stage, objects are discarded for which one of the features is not within a predefined range. In the second stage, classification of the remaining objects is performed using a k nearest-neighbor classifier. The method is evaluated on 20 scans containing different amounts of calcifications and gives high accuracy, sensitivity and specificity. In total, 119 calcifications out of 153 were detected at the expense of 33 false-positives. D
Quantifying Calcification in the Lumbar Aorta on X-Ray Images
Lecture Notes in Computer Science, 2007
In this paper we propose to use inpainting to estimate the severity of atherosclerotic plaques from X-ray projections. Inpainting allows to "remove" the plaque and estimate what the background image for an uncalcified aorta would have looked like. A measure of plaque severity can then be derived by subtracting the inpainting from the original image. In contrast to the current standard of categorical calcification scoring from X-rays, our method estimates both the size and the density of calcified areas and provides a continuous severity score, thus allowing for measurement of more subtle differences.
Automatic segmentation of calcified plaques and vessel borders in IVUS images
International Journal of Computer Assisted Radiology and Surgery, 2008
Objective Intravascular ultrasound (IVUS) is a diagnostic imaging technique for tomographic visualization of coronary arteries. Automatic analysis of IVUS images is difficult due to speckle noise, artifacts of the catheter, and shadows generated by calcifications. We designed and implemented a system for automated segmentation of coronary artery IVUS images. Methods Two methods for automatic detection of the intima and the media-adventitia borders in IVUS coronary artery images were developed and compared. The first method uses the parametric deformable models, while the second method is based on the geometric deformable models. The initial locations of the borders are approximated using two different edge detection methods. The final borders are then defined using the two deformable models. Finally, the calcified regions between the extracted borders are identified using a Bayesian classifier. The performance of the proposed methods was evaluated using 60 different IVUS images obtained from 7 patients. Results Segmented images were compared with manually outlined contours. We compared the performance of calcified region characterization methods using ROC analysis and by computing the sensitivity and specificity of the Bayesian classifier, thresholding, adaptive thresholding, and textural features. The Bayesian method performed best. Conclusion The results shows that the geometric deformable model outperforms the parametric deformable model for automated segmentation of IVUS coronary artery images.
A Static SMC Sampler on Shapes for the Automated Segmentation of Aortic Calcifications
Lecture Notes in Computer Science, 2010
In this paper, we propose a sampling-based shape segmentation method that builds upon a global shape and a local appearance model. It is suited for challenging problems where there is high uncertainty about the correct solution due to a low signal-to-noise ratio, clutter, occlusions or an erroneous model. Our method suits for segmentation tasks where the number of objects is not known a priori, or where the object of interest is invisible and can only be inferred from other objects in the image. The method was inspired by shape particle filtering from de Bruijne and Nielsen, but shows substantial improvements to it. The principal contributions of this paper are as follows: (i) We introduce statistically motivated importance weights that lead to better performance and facilitate the application to new problems. (ii) We adapt the static sequential Monte Carlo (SMC) algorithm to the problem of image segmentation, where the algorithm proves to sample efficiently from high-dimensional static spaces. (iii) We evaluate the static SMC sampler on shapes on a medical problem of high relevance: the automated quantification of aortic calcifications on X-ray radiographs for the prognosis and diagnosis of cardiovascular disease and mortality. Our results suggest that the static SMC sampler on shapes is more generic, robust, and accurate than shape particle filtering, while being computationally equally costly.
Automated coronary calcification detection and scoring
ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005., 2005
An automated method for coronary calcification detection from ECG-triggered multi-slice CT data is presented. The method first segments the heart region. In the obtained volume candidate objects are extracted by thresholding. They include coronary calcification, calcium located elsewhere in the heart, for example, in the valves or the myocardium, and other high density structures mostly representing noise and bone. A set of 57 features is calculated for each candidate object. In the feature space objects are classified with a k-NN classifier and feature selection in three consecutive stages. The method is tested on 51 scans of the heart. They contain 320 calcification in the coronary arteries, 291 in the aorta and 62 calcifications in the heart. The system correctly detected 177 calcifications in the coronaries at the expense of 56 false positive objects. On average the method makes 3.8 errors per scan.
The International Journal of Cardiovascular Imaging, 2010
Measurements related to coronary artery calcification (CAC) offer significant predictive value for coronary artery disease (CAD). In current medical practice CAC scoring is a labor-intensive task. The objective of this paper is the development and evaluation of a family of coronary artery region (CAR) models applied to the detection of CACs in coronary artery zones and sections. Thirty patients underwent non-contrast electron-beam computed tomography scanning. Coronary artery trajectory points as presented in the University of Houston heart-centered coordinate system were utilized to construct the CAR models which automatically detect coronary artery zones and sections. On a perpatient and per-zone basis the proposed CAR models detected CACs with a sensitivity, specificity and accuracy of 85.56 (±15.80)%, 93.54 (±1.98)%, and 85.27 (±14.67)%, respectively while the corresponding values in the zones and segments based case were 77.94 (±7.78)%, 96.57 (±4.90)%, and 73.58 (±8.96)%, respectively. The results of this study suggest that the family of CAR models provide an effective method to detect different regions of the coronaries. Further, the CAR classifiers are able to detect CACs with a mean sensitivity and specificity of 86.33 and 93.78%, respectively.
Automated aortic calcification detection in low-dose chest CT images
SPIE Proceedings, 2014
The extent of aortic calcification has been shown to be a risk indicator for vascular events including cardiac events. We have developed a fully automated computer algorithm to segment and measure aortic calcification in low-dose noncontrast, non-ECG gated, chest CT scans. The algorithm first segments the aorta using a pre-computed Anatomy Label Map (ALM). Then based on the segmented aorta, aortic calcification is detected and measured in terms of the Agatston score, mass score, and volume score. The automated scores are compared with reference scores obtained from manual markings. For aorta segmentation, the aorta is modeled as a series of discrete overlapping cylinders and the aortic centerline is determined using a cylinder-tracking algorithm. Then the aortic surface location is detected using the centerline and a triangular mesh model. The segmented aorta is used as a mask for the detection of aortic calcification. For calcification detection, the image is first filtered, then an elevated threshold of 160 Hounsfield units (HU) is used within the aorta mask region to reduce the effect of noise in low-dose scans, and finally non-aortic calcification voxels (bony structures, calcification in other organs) are eliminated. The remaining candidates are considered as true aortic calcification. The computer algorithm was evaluated on 45 low-dose non-contrast CT scans. Using linear regression, the automated Agatston score is 98.42% correlated with the reference Agatston score. The automated mass and volume score is respectively 98.46% and 98.28% correlated with the reference mass and volume score.
Procedia Computer Science, 2016
Cardiovascular disease (CVD) is a major cause of mortality and the main cause of morbidity worldwide. CVD may lead to heart attacks and strokes and most of these are caused by atherosclerosis; this is a medical condition in which the arteries become narrowed and hardened due to an excessive build-up of plaque on the inner artery wall. Arterial calcification and, in particular, abdominal aortic calcification (AAC) is a manifestation of atherosclerosis and a prognostic indicator of CVD. In this paper, a two-stage automatic method to detect and quantify the severity of AAC is described; it is based on the analysis of lateral vertebral fracture assessment (VFA) images. These images were obtained on a dual energy x-ray absorptiometry (DXA) scanner used in single energy mode. First, an active appearance model was used to segment the lumbar vertebrae L1-L4 and the aorta on VFA images; the segmentation of the aorta was based on its position with respect to the vertebrae. In the second stage, feature vectors representing calcified regions in the aorta were extracted to quantify the severity of AAC. The presence and severity of AAC was also determined using an established visual scoring system (AC24). The abdominal aorta was divided into four parts immediately anterior to each vertebra, and the severity of calcification in the anterior and posterior walls was graded separately for each part on a 0-3 scale. The results were summed to give a composite severity score ranging from 0 to 24. This severity score was classified as follows: mild AAC (score 0-4), moderate AAC (score 5-12) and severe AAC (score 12-24). Two classification algorithms (k-nearest neighbour and support vector machine) were trained and tested to assign the automatically extracted feature vectors into the three classes. There was good agreement between the automatic and visual AC24 methods and the accuracy of the automated technique relative to visual classification indicated that it is capable of identifying and quantifying AAC over a range of severity.
MORPHOLOGICAL QUANTIFICATION OF AORTIC CALCIFICATION FROM LOW MAGNIFICATION IMAGES
Image Analysis & Stereology, 2003
Atherosclerotic and medial vascular calcifications are frequent in chronic renal failure patiens and predict their increased cardiovascular mortality. Experimental models for mice have been recently developed in order to study these disorders. The aim of this paper is to present the morphological image processing algorithms developed for the semi-automated measurement of calcification from sections of aorta stained using von Kossa's silver nitrate procedure and acquired at low magnification power ( ¢ 2£ 5) on colour images. The approach is separated into two sequential phases. First, the segmentation is aimed to extract the calcification structures and on the other hand to demarcate the region of the atherosclerotic lesion within the tissue. The segmentation yields the image data which is the input to the second phase, the quantification. Calcified structures are measured inside and outside the lesion using a granulometric curve which allows the calculation of statistical parameters of size. The same operator computes the shape of the lesion. The relative proportion of the area of calcification is also calculated respectively for the atherosclerotic lesion area and the area outside such lesions. In conclusion, the here developed method allows quantification of vascular calcified deposits in mouse aorta. This method will be useful for the quantitative assessment of pathological vascular changes in animals and man.