Vascular segmentation plays an important role in the assessment of peripheral arterial disease. The segmentation is very challenging especially for arteries with severe stenosis or complete occlusion. We present a cascading algorithm for vascular centerline tree detection specializing in detecting centerlines in diseased peripheral arteries. It takes a three-dimensional computed tomography angiography (CTA) volume and returns a vascular centerline tree, which can be used for accelerating and facilitating the vascular segmentation. The algorithm consists of four levels, two of which detect healthy arteries of varying sizes and two that specialize in different types of vascular pathology: severe calcification and occlusion. We perform four main steps at each level: appropriate parameters for each level are selected automatically, a set of centrally located voxels is detected, these voxels are connected together based on the connection criteria, and the resulting centerline tree is corrected from spurious branches. The proposed method was tested on 25 CTA scans of the lower limbs, achieving an average overlap rate of 89% and an average detection rate of 82%. The average execution time using four CPU cores was 70 s, and the technique was successful also in detecting very distal artery branches, e.g., in the foot.
Proc. SPIE. 7962, Medical Imaging 2011: Image Processing
KEYWORDS: Image processing algorithms and systems, Image visualization, Detection and tracking algorithms, Visualization, Image segmentation, Medical imaging, Partial differential equations, 3D metrology, Information technology, Radiology
To accelerate level-set based abdominal aorta segmentation on CTA data, we propose a periodic monotonic speed
function, which allows segments of the contour to expand within one period and to shrink in the next period, i.e.,
coherent propagation. This strategy avoids the contour's local wiggling behavior which often occurs during the
propagating when certain points move faster than the neighbors, as the curvature force will move them backwards even
though the whole neighborhood will eventually move forwards. Using coherent propagation, these faster points will,
instead, stay in their places waiting for their neighbors to catch up. A period ends when all the expanding/shrinking
segments can no longer expand/shrink, which means that they have reached the border of the vessel or stopped by the
curvature force. Coherent propagation also allows us to implement a modified narrow band level set algorithm that
prevents the endless computation in points that have reached the vessel border. As these points' expanding/shrinking
trend changes just after several iterations, the computation in the remaining iterations of one period can focus on the
actually growing parts. Finally, a new convergence detection method is used to permanently stop updating the local level
set function when the 0-level set is stationary in a voxel for several periods. The segmentation stops naturally when all
points on the contour are stationary. In our preliminary experiments, significant speedup (about 10 times) was achieved
on 3D data with almost no loss of segmentation accuracy.
Pyramid based methods in image processing provide a helpful framework for accelerating the propagation of information
over large spatial domains, increasing the efficiency for large scale applications. Combined with an anisotropic diffusion
scheme tailored to preserve the boundaries at a given level, an efficient way for enhancing large structures in 3D images is
presented. In our approach, the partial differential equation defining the evolution of the intensity in the image is solved
in an explicit scheme at multiple resolutions in an ascending-descending cycle. Intensity 'flux' between distant voxels is
allowed, while preserving borders relative to the scale. Experiments have been performed both with phantoms and with
real data from 3D Transrectal Ultrasound Imaging. The effectiveness of the method to remove speckle noise and to enhance
large structures such as the prostate has been demonstrated. For instance, using two scales reduces the computation time
by 87% as compared to a single scale. Furthermore, we show that the boundaries of the prostate are mainly preserved, by
comparing with manually outlined edges.
Proc. SPIE. 6513, Medical Imaging 2007: Ultrasonic Imaging and Signal Processing
KEYWORDS: Image processing algorithms and systems, 3D acquisition, 3D image enhancement, Visualization, Signal attenuation, Ultrasonography, Image segmentation, Image processing, 3D image processing, Anisotropic diffusion
Ultrasound (US) is one of the most used imaging modalities today as it is cheap, reliable, safe and widely
available. There are a number of issues with US images in general. Besides reflections which is the basis
of ultrasonic imaging, other phenomena such as diffraction, refraction, attenuation, dispersion and scattering
appear when ultrasound propagates through different tissues. The generated images are therefore corrupted by
false boundaries, lack of signal for surface tangential to ultrasound propagation, large amount of noise giving
rise to local properties, and anisotropic sampling space complicating image processing tasks.
Although 3D Transrectal US (TRUS) probes are not yet widely available, within a few years they will likely be
introduced in hospitals. Therefore, the improvement of automatic segmentation from 3D TRUS images, making
the process independent of human factor is desirable. We introduce an algorithm for attenuation correction,
reducing enhancement/shadowing effects and average attenuation effects in 3D US images, taking into account
the physical properties of US. The parameters of acquisition such as logarithmic correction are unknown, therefore
no additional information is available to restore the image. As the physical properties are related to the direction
of each US ray, the 3D US data set is resampled into cylindrical coordinates using a fully automatic algorithm.
Enhancement and shadowing effects, as well as average attenuation effects, are then removed with a rescaling
process optimizing simultaneously in and perpendicular to the US ray direction. A set of tests using anisotropic
diffusion are performed to illustrate the improvement in image quality, where well defined structures are visible.
The evolution of both the entropy and the contrast show that our algorithm is a suitable pre-processing step for
We developed a fast centerline-based segmentation (CBS) algorithm for the extraction of colon in computer-aided detection (CAD) for CT colonography (CTC). CBS calculates local centerpoints along thresholded components of abdominal air, and connects the centerpoints iteratively to yield a colon centerline. A thick region encompassing the colonic wall is extracted by use of region-growing around the centerline. The resulting colonic wall is employed in our CAD scheme for the detection of polyps, in which polyps are detected within the wall by use of volumetric shape features. False-positive detections are reduced by use of a Bayesian neural network. The colon extraction accuracy of CBS was evaluated by use of 38 clinical CTC scans representing various preparation conditions. On average, CBS covered more than 96% of the visible region of colon with less than 1% extracolonic components in the extracted region. The polyp detection performance of the CAD scheme was evaluated by use of 121 clinical cases with 42 colonoscopy-confirmed polyps 5-25 mm. At a 93% by-polyp detection sensitivity for polyps ≥5 mm, a leave-one-patient-out evaluation yielded 1.4 false-positive polyp detections per CT scan.
We developed a new method for automated detection of colonic polyps in CT colonography. The colon is extracted from CT images by use of a centerline-based colon segmentation method. Polyp candidates are detected by use of hysteresis thresholding and fuzzy merging. The regions of the polyp candidates are segmented by use of conditional morphological dilation. False-positive polyp candidates are reduced by a region-based supine-prone correspondence method and by a Bayesian neural network with shape and texture features. To evaluate the method, CT colonography was performed for 121 patients with standard technique and single- and multi-detector helical scanners by use of 2.5-5.0 mm collimations, 1.0-5.0 mm reconstruction intervals, and 60-100 mA tube currents. Twenty-eight patients had a total of 42 polyps: 22 polyps were 5-10 mm, and 20 polyps were 11-25 mm in size. A leave-one-out evaluation of the CAD scheme with by-patient elimination yielded 93% by-polyp and by-patient detection sensitivities with 2.0 false-positive detections per data set on average. The average computation time was 4 minutes per data set. The results indicate that the CAD scheme may be useful in improving the performance of computer-aided detection for colon cancer in a clinical screening setting.
We are developing a computer-aided scheme for the detection of colonic polyps and masses in CT colonography. The colon is extracted automatically from CT images by use of a knowledge-guided technique. The detection of polyps and masses is based on shape index and curvedness features. A feature-guided segmentation technique is used to extract the regions of detected polyps. A quadratic discriminant classifier is used for reducing false-positive detections and for determining the final output based on shape index, gradient concentration, and CT value features. To evaluate the technique, we performed CT colonography for 72 patients with cleansed colons and by use of a standard technique with helical CT scanning. Thirteen patients had a total of 20 polyps measuring 5-12mm, and four patients had 4 masses measuring 25-40 mm in diameter. In a by-polyp(by-mass) leave-one-out evaluation, the CAD scheme detected 95% of the polyps(all masses) with an average of 1.5(0.5) false-positive detections per patient. These preliminary results suggest that our CAD scheme is potentially a useful tool for providing rapid interpretation and high diagnostic accuracy for CT colonography.
We developed a method for generating the centerline of a colon in CT
Colonography that is computationally fast, and robust to collapsed regions. Patients underwent CT Colonography after standard pre-colonoscopy cleansing. The colonic lumen was segmented using an existing anatomy-based approach, and a distance map of the colonic lumen was computed using a distance transform. The centerline was computed as follows: Local maxima representative for the centerline were sparsely extracted from the distance map. Iteratively, each
pair of maxima satisfying a set of connection criteria were connected, creating a graph-like structure containing a main centerline with additional branches. Branches were later removed and the resulting centerline was stored.
Centerlines of the colon were computed, and also manually and independently drawn by two radiologists, for 33 CT Colonographic data
sets. The data sets were chosen to give a wide spectrum of colons, ranging from cases with good segmentation and extension to cases with collapsed regions and numerous extra-colonic components such
as small bowel. On average, 94% of the human-generated centerlines were correctly identified by the computer-generated centerlines. The average displacement between the human- and computer-generated centerlines was 4.0 mm. Average centerline computation time was less than 4 seconds.
A computer-aided diagnosis scheme for the detection of colonic polyps in CT colonography has been developed, and its performance has been assessed based on clinical cases with colonoscopy-confirmed polyps. In the scheme, the colon was automatically segmented by use of knowledge-guided segmentation from 3-dimensional isotropic volumes reconstructed from axial CT slices in CT colonography. Polyp candidates are detected by first computing of 3-dimensional geometric features that characterize polyps, and then segmenting of connected components corresponding to suspicious regions by hysteresis thresholding and fuzzy clustering based on these geometric features. False-positive detections are reduced by computation of 3-dimensional texture features characterizing the internal structures of the polyp candidates, followed by application of discriminant analysis to the feature space generated by the geometric and texture features. We applied our scheme to 43 CT colonographic cases with cleansed colon, including 12 polyps larger than 5 mm. In a by-dataset analysis, the CAD scheme yielded a sensitivity of 95% with 1.2 false positives per data set. The false negative was one of the two polyps in a single patient. Consequently, in by-patient analysis, our method yielded 100\% sensitivity with 2.0 false positives per patient. The results indicate that our CAD scheme has the potential to detect clinically important polyp cases with a high sensitivity and a relatively low false-positive rate.