Statistical shape modeling of the diaphragm for application to Rb-82 cardiac PET-CT studies

It is important when motion-correcting Rb-82 cardiac PET-CT scans that diaphragm motion is accounted for, to avoid attenuation-correction artifacts. In the absence of a gated CT, a model of the diaphragm could assist in identifying the diaphragm position in noisy PET images as a step towards performing respiratory-matched attenuation-correction. To test this, a shape model was constructed from a training set of 10 gated CT datasets, in which the diaphragm was segmented. Principal Component Analysis was performed on corresponding landmarks from all surfaces to extract modes of variation in shape and motion between patients. Fitting the model to a segmented surface was then achieved by weighting each mode to minimize the sum of squared differences between the fitted and original surfaces: this was carried out for datasets used in its construction and previously unseen datasets, using a leave-one-out approach. It was found that 95 of training data variation was described in only 5 modes, indicating that 5 parameters need to be fitted in order to fully describe the diaphragm over the respiratory cycle. Model success was measured in terms of the residual differences after fitting and was found to be 3.8 ± 1.0 mm per landmark for the 10 leave-one-out models. Since the slice thickness in the PET data is 3.3 mm, it is likely that this level of error is tolerable in this application. Furthermore, the overall diaphragm shape was reproduced well in the presence of these errors, further indicating the validity of this approach. These results demonstrate the potential of this technique in benefiting the prediction of the time-varying diaphragm position. This could therefore provide a valuable technique in determining diaphragm motion in cardiac PET-CT studies. ©2008 IEEE.</p

Automatic segmentation of low resolution fetal cardiac data using snakes with shape priors

This paper presents a level set deformable model to segment all four chambers of the fet al. heart simultaneously. We show its results in 2D on 53 images taken from only 8 datasets. Due to our lack of sufficient data we built only a mean template from the training data instead of a full Active Shape Model. Using rigid registration the template was registered to unseen images and the snakes were guided by individual chamber priors as they evolved in unison to segment missing cardiac structures in the presence of high noise. Using a leave one out approach most of the segmentation errors are within 3 pixels of manually traced contours.</p

Automatic segmentation of liver using a topology adaptive snake

Most attempts at automatic segmentation of liver from computerised tomography images to date have relied on low-level segmentation techniques, such as thresholding and mathematical morphology, to obtain the basic liver structure. The derived boundary can then be smoothed or refined using more advanced methods. In this paper we present a method by which a topology adaptive active contour model, or snake, accurately segments liver tissue from CT images. The use of conventional snakes for liver segmentation is difficult due to the presence of other organs closely surrounding the liver. Our technique avoids this problem by adding an inflationary force to the basic snake equation, and initialising the snake inside the liver. Once the user has initialised the snake for one CT slice, the starting locations for other slices in a dataset are determined automatically from the center of gravity of the segmented area of previous slice. We present results from over 500 images, covering 4 different healthy datasets, and each liver slice is segmented in 2D before being compared to the equivalent segmentation performed by hand. Statistical analysis of the datasets shows that, in each case, there is no significant difference between the areas and the snake-segmented liver to the areas of hand segmented liver, here treated as the gold standard.</p

Automatic 3D segmentation of the liver from computed tomography images, a discrete deformable model approach

Automatic segmentation of the liver has the potential to assist in the diagnosis of disease, preparation for organ transplantation, and possibly assist in treatment planning. This paper presents initial results from work that extends on previous two-dimensional (2D) segmentation methods by implementing full three-dimensional (3D) liver segmentation, using a self-reparameterising discrete deformable model. This method overcomes many of the weaknesses inherent in 2D segmentation techniques, such as the inability to automatically segment separate lobes of the liver in each image slice, and sensitivity to individual-slice noise. Results are presented showing volumetric and overlap comparison of twelve automatically segmented livers with their corresponding manually segmented livers, which were treated as the gold standard for this study. © 2006 IEEE.</p

Level set snake algorithms on the fetal heart

The fetal heart has very thin intra-chamber walls which are often not resolved by ultrasound scanners and may drop out as a result of imaging. In order to measure blood volumes from all chambers in isolation, deformable model approaches were used to segment the chambers and fill in the missing structural information. Three level set algorithms in the fetal cardiac segmentation literature (two without and, one with the use of a shape prior) were applied to real ultrasound data. The shape prior term was extracted from the shape prior level set and incorporated into the amorphous snakes for a fairer comparison. To our knowledge this is the first time these existing fetal cardiac non shape based segmentation algoridims have been modified for shape awareness in this way. © 2007 IEEE.</p

In-vitro validation of a novel model-based approach to the measurement of arterial blood flow waveforms from dynamic digital x-ray images

We have developed a waveform shape model-based algorithm for the extraction of blood flow from dynamic arterial x-ray angiographic images. We have carried out in-vitro validation of this technique. A pulsatile physiological blood flow circuit was constructed using an anthropomorphic cerebral vascular phantom to simulate the cerebral arterial circulation with whole blood as the fluid. Instantaneous recording of flow from an electromagnetic flow meter (EMF) provided the gold standard measurement. Biplane dynamic digital x-ray images of the vascular phantom with injection of contrast medium were acquired at 25 fps using a PC frame capture card with calibration using a Perspex cube. Principal component analysis was used to construct a shape model by collecting 434 flow waveforms from the EMF under varying flow conditions. Blood flow waveforms were calculated from the angiographic data by using our previous concentration-distance curve matching (ORG) algorithm and by using the new model-based (MB) algorithm. Both instantaneous and mean flow values calculated using the MB algorithm showed greater correlation, less bias, and lower variability than those calculated using the ORG algorithm when compared to the EMF values. We have successfully demonstrated that use of a priori waveform shape information can improve flow measurements from dynamic x-ray angiograms.</p

A hybrid method for haemorrhage segmentation in trauma brain CT

Traumatic brain injuries are important causes of disability and death. Physicians use CT or MRI images to observe the trauma and measure its severity for diagnosis and treatment. Due to the overlap of haemorrhage and normal brain tissues, segmentation methods sometimes lead to false results. In this paper, we present a hybrid method to segment the haemorrhage region in trauma brain CT images. Firstly, the images are partitioned to small segments called superpixels and supervoxels in 2D and 3D spaces, respectively. Then the haemorrhage superpixels/supervoxels are grouped using their average intensity as feature. Finally, a distance regularized level-set is used to accurately delineate the exact boundary of the haemorrhage region. Evaluation is performed using the Jaccard overlap measure of our proposed technique against a modified distance regularized level-set and against the manually segmented ground truth. Our results suggest that performing level-set after superpixel/supervoxel segmentation provides better segmentation than superpixel/supervoxel intensity grouping alone and both these schemes perform better than the modified distance regularized level-set evolution method.</p

Classification of audio signals using statistical features on time and wavelet transform domains

This paper presents a study on musical signal classification, using wavelet transform analysis in conjunction with statistical pattern recognition techniques. A comparative evaluation between different wavelet analysis architectures in terms of their classification ability, as well as between different classifiers is carried out. We seek to establish which statistical measures clearly distinguish between the three different musical styles of rock, piano, and jazz. Our preliminary results suggest that the features collected by the adaptive splitting wavelet transform technique performed better compared to the other wavelet based techniques, achieving overall classification accuracy of 91.67, using either the Minimum Distance Classifier or the Least Squares Minimum Distance Classifier. Such a system can play a useful part in multimedia applications which require content based search, classification, and retrieval of audio signals, as defined in MPEG-7.</p

Validation of an optical flow algorithm to measure blood flow waveforms in arteries using dynamic digital X-ray images

We have developed a weighted optical flow algorithm for the extraction of instantaneous blood velocity from dynamic digital x-ray images of blood vessels. We have carried out in-vitro validation of this technique. A pulsatile physiological blood flow circuit was constructed using sections of silicone tubing to simulate blood vessels with whole blood as the fluid. Instantaneous recording of flow from an electromagnetic flow meter (EMF) provided the gold standard measurement. Biplanar dynamic digital x-ray images of the blood vessel with injection of contrast medium were acquired at 25 fps using a PC frame capture card. Imaging of a Perspex calibration cube allowed 3D reconstruction of the vessel and determination of true dimensions. Blood flow waveforms were calculated off-line on a Sun workstation using the new algorithm. The correlation coefficient between instantaneous blood flow values obtained from the EMF and the x-ray method was r = 0.871, n = 1184, p<0.0001. The correlation coefficient for average blood flow was r = 0.898, n = 16, p<0.001. We have successfully demonstrated that our new algorithm can measure pulsatile blood flow in a vessel phantom. We aim to use this algorithm to measure blood flow clinically in patients undergoing vascular interventional procedures.</p

Level set segmentation of the fetal heart

Segmentation of the fetal heart can facilitate the 3D assessment of the cardiac function and structure. Ultrasound acquisition typically results in drop-out artifacts of the chamber walls. This paper presents a level set deformable model to simultaneously segment all four cardiac chambers using region based information. The segmented boundaries are automatically penalized from intersecting at walls with signal dropout. Root mean square errors of the perpendicular distances between the algorithm's delineation and manual tracings are within 7 pixels (<2mm) in 2D and under 3 voxels (<4.5mm) in 3D. The ejection fraction was determined from the 3D dataset. Future work will include further testing on additional datasets and validation on a phantom. © Springer-Verlag Berlin Heidelberg 2005.</p