Alok Sarwal

3-D reconstruction of coronary arteries

Coronary arteriography is a technique used for evaluating the state of the coronary arteries. Matching of coronary arteries from multiple views is necessary for obtaining a 3-D (three dimensional) description of the arterial tree. Overlapping vessels and artifacts due to digital subtraction of the angiogram background make the matching process quite difficult. Simplex method based linear programming and relaxation based consistent labeling are applied to skeletons from three views in order to obtain a matching of branches between views. Results show low error for 3-D reconstruction, by overlapping its forward projection with the skeleton extracted from one view.<<ETX>>

A gray-level thinning method for delineation and representation of arteries.

The quantification of three dimensional (3D) properties of coronary arteries is of significant importance. The performance of the 3D analysis is critically based on low-level representation of the arterial tree for different projections. A skeletal representation of arteries can provide appropriate data structure for registration of multiple angiographic projections and it can be further utilized for 3D reconstruction of the arterial tree. This paper presents an automated method for extracting the skeletal points of an arterial tree directly from the gray-level information without determining the edges a priori. It offers the advantage of improved reliability compared to methods based on detecting dual edges of the arteries. Novel application of filtering techniques provide accurate estimates of the statistics of the background. A recursive search scheme is used to aggregate the skeletal representation at multiple resolutions. Results on a set of Digitally Subtracted Angiograms (DSA) have been presented.

Three dimensional reconstruction of coronary arteries from two views

Geometric measurement of localized lumen stenosis of the coronary artery is an important indicator of heart disease. Three-dimensional (3-D) reconstruction of coronary arteries is of significant interest for providing accurate quantification of coronary vasculature and stenosis information. The overall method of coronary arterial analysis, as presented here includes: segmentation of the coronary vessels followed by medial axes and diameter feature detection for the entire arterial tree; registration of the arterial trees from the two views; 3-D reconstruction from the two view medial axes and correction for any error in 3-D geometry (pose) to obtain final 3-D reconstruction. Measurements were made for performing an error analysis of a pig-cast phantom and human data, in order to evaluate the presented method.

A system for MR brain image segmentation

A system for manual segmentation of multispectral MR images into anatomical structures is described. The system consists of a SPARC workstation and a PIXAR image computer. The system simultaneously displays an enlarged working area as well as T/sub 1/ weighted, and short and long echo T/sub 2/ weighted images. Multiple linked cursors are also displayed for easier orientation. The operator can easily position the working area, specify the region in the working area, assign a label to the chosen region, and save the results in a file. The sophisticated man-machine interface, along with flexible choice of displayed images of the brain and their resolutions, make the system very precise and productive for manual segmentation with the interobserver variability less than 5%.

Three-dimensional reconstruction of coronary arteries using estimation techniques

Coronary arteriography is a technique used for evaluating the state of the coronary arteries. Matching of coronary arteries from multiple views is necessary for obtaining a 3-D description of the arterial tree. Overlapping vessels and artifacts due to digital subtraction of the angiogram background make the matching process quite difficult. The simplex method applied for linear programming and a relaxation technique for pre-processing the data are applied to skeletons from two views in order to obtain a matching of branches between views. The elements of the centerline along the branch are modeled as a Markov random field and a matching of each element in the two views is obtained by minimizing the energy of the matching contour. The element matching is treated as an estimation problem such that the a- posteriori probability is maximized. Results are provided for the 3-D reconstruction using these algorithms for automatic correspondence, and compared to those obtained by manual correspondence specification. This work was performed using a pig-cast realistic phantom. The results are encouraging.

Segmentation of mammographic microcalcifications

Mammography associated with clinical breast examination and self-breast examination is the only effective and viable method for mass breast screening. Some relevant techFiques to distinguish between benign and malignant microcalcifications are based on the computerized analysis of mammographic microcalcifications. Manual segmentation has been previously used to extract the immediate neighborhood surrounding the microcalcifications from the digitized gray-level image. The method* presented provides an alternative to manual segmentation and shows good robustness to statistical varincions in the background. Algorithms are applied to this segmented region to obtain the microcalcifications. This approach requires selection of a sub-image from the mammogram image. Features can be extracted from the segmented microcalcifications and used for training a neural network for classification of the suspect microcalcifications.

Multilevel lines of communication extraction

In this paper, a novel multi-level adaptive lines of communication extraction method for multispectral images is presented. The method takes into account both spectral and spatial characteristics of the data on different levels of processing. The principal background classes are obtained first using K-means clustering. Each pixel is examined next for classification using a minimum distance classifier with principal class signatures obtained in the previous level. In the next level, the neighborhood of each unclassified pixel is analyzed for inclusion of candidate classes for use as endmembers in a spectral unmixing model. If the list of candidate background classes is empty, the conditions for their inclusion are relaxed. The fractions of backgrounds and lines of communication signatures for the unclassified pixels are determined by means of linear least-squares method. If the results of unmixing are not satisfactory, the candidate clusters list is renewed, and unmixing is repeated. The lines of communication detection within each pixel is performed next. The line segments detection parameters are initialized, directional confidence is calculated, and line segment tracking is initialized. The line segments are incremented until the composite confidence becomes too low. At the end, segment connection, and lines of communications identification is performed. The proposed method was successfully applied to both synthetic and AVIRIS hyperspectral data sets.

Early detection of postoperative residual tumor using image subtraction

The detection after surgery of residual tumor from magnetic resonance (MR) images is difficult due to the low contrast level of the images. Gadolinium-enhanced MR imaging has been found valuable in detecting residual enhancing tumor when performed within 72 hours after surgery. The patient is scanned by the MR scanner with and without infusion of gadolinium, a contrast agent. Usually, the estimation of post-operative tumor volume is done by visual comparison of the T1 MR images obtained with and without gadolinium infusion. The T1 MR images, in most cases, without contrast demonstrates areas of hyper intensities (high brightness levels), consistent with hemorrhage. These hyper intense areas often make it difficult to detect residual tumor in post contrast images. This is due to the presence of both acute hemorrhage and gadolinium enhancement which have high brightness levels in T1 MR images. Even in MR images taken within 72 hours after surgery, detection of tumor enhancement in areas of increased T1 signal produced by blood products or by postoperative changes can be difficult when performed by the naked eye. Due to these problems, the quantification of residual tumor becomes a subjective issue among neuro-radiologists. Thus to reduce errors produced by the human factor, an automated procedure to detect residual tumor is required. We have developed a technique to differentiate tumor enhancement from postoperative changes and blood products on MR imaging. The technique involves fusion of pre- and post-gadolinium MR images performed in the immediate postoperative period. Computerized slice based substraction is then done on the corresponding fused images of the two sets. The subtraction process results in a composite slice, which is examined for differences between pre- and post-gadolinium studies. The presented technique was tested on 14 cases in which MR images were obtained from brain tumor patients within 72 hours after surgery. The subtraction technique easily distinguished residual enhancing tumor from postoperative surgical changes and was simple to perform. The technique proposed and developed has given good results and will be used in clinical trial and diagnosis. Future potentials of the technique are discussed and illustrative cases presented.