Sarang Lakare

3D digital cleansing using segmentation rays

We propose a novel approach for segmentation and digital cleansing of endoscopic organs. Our method can be used for a variety of segmentation needs with little or no modification. It aims at fulfilling the dual and often conflicting requirements of a fast and accurate segmentation and also eliminates the undesirable partial volume effect which contemporary approaches cannot. For segmentation and digital cleansing, we use the peculiar characteristics exhibited by the intersection of any two distinct-intensity regions. To detect these intersections we cast rays through the volume, which we call the segmentation rays as they assist in the segmentation. We then associate a certain task of reconstruction and classification with each intersection the ray detects. We further use volumetric contrast enhancement to reconstruct surface lost by segmentation (if any), which aids in improving the quality of the volume rendering.

Image segmentation approach to extract colon lumen through colonic material tagging and hidden Markov random field model for virtual colonoscopy

Virtual colonoscopy provides a safe, minimal-invasive approach to detect colonic polyps using medical imaging and computer graphics technologies. Residual stool and fluid are problematic for optimal viewing of the colonic mucosa. Electronic cleansing techniques combining bowel preparation, oral contrast agents, and image segmentation were developed to extract the colon lumen from computed tomography (CT) images of the colon. In this paper, we present a new electronic colon cleansing technology, which employs a hidden Markov random filed (MRF) model to integrate the neighborhood information for overcoming the non-uniformity problems within the tagged stool/fluid region. Prior to obtaining CT images, the patient undergoes a bowel preparation. A statistical method for maximum a posterior probability (MAP) was developed to identify the enhanced regions of residual stool/fluid. The method utilizes a hidden MRF Gibbs model to integrate the spatial information into the Expectation Maximization (EM) model-fitting MAP algorithm. The algorithm estimates the model parameters and segments the voxels iteratively in an interleaved manner, converging to a solution where the model parameters and voxel labels are stabilized within a specified criterion. Experimental results are promising.

A gradient magnitude based region growing algorithm for accurate segmentation

An accurate segmentation is critical for clinical application of medical images. The undesirable partial-volume-effect, which lies on a boundary between a high intensity region and a low intensity region, makes unerring boundary determination a difficult task. A new approach to segmentation is required for removing the adverse effect on the boundary, which is unwanted especially from the point of view of volume rendering. Here, the authors propose a gradient magnitude based region growing algorithm for accurate segmentation. The gradient is useful for enhancing the boundary because it emphasizes the difference among voxel values. By analyzing the gradient magnitude, the authors can see the sufficient contrast which must be presented on the boundary region and they use this contrast to increase the accuracy of their segmentation method. The authors pay attention only to the boundary region, not to the whole large volumetric dataset itself, making it more computationally efficient.

Electronic colon cleansing using segmentation rays for virtual colonoscopy

We present an electronic colon cleansing algorithm using a new segmentation technique based on segmentation rays. These rays are specially designed to analyze the intensity profile as they traverse through the dataset. When this intensity profile matches any of the pre-defined profiles, the rays perform certain task of reconstruction. We use these rays to detect the intersection between air and residual fluid, and between residual fluid and soft-tissue. One of the most important advantages of segmentation rays over other segmentation techniques is the detection of partial volume regions. Segmentation rays can accurately detect partial volume regions and remove them if needed. Once partial volume is eliminated, removal of other unwanted regions (e.g., tagged fluid) is relatively easy. This approach to electronic cleansing is extremely fast as it requires minimal computation.

Interactive electronic biopsy for 3D virtual colonscopy

We propose an interactive electronic biopsy technique for more accurate colon cancer diagnoses by using advanced volume rendering technologies. The volume rendering technique defines a transfer function to map different ranges of sample values of the original volume data to different colors and opacities, so that the interior structure of the polyps can be clearly recognized by human eyes. Specifically, we provide a user- friendly interface for physicians to modify various parameters in the transfer function, so that the physician can interactively change the transfer function to observe the interior structures inside the abnormalities. Furthermore, to speed up the volume rendering procedure, we propose an efficient space-leaping technique by observing that the virtual camera parameters are often fixed when the physician modifies the transfer function. In addition, we provide an important tool to display the original 2D CT image at the current 3D camera position, so that the physician is able to double check the interior structure of a polyp with the density variation in the corresponding CT image for confirmation. Compared with the traditional biopsy in the procedure of optical colonoscopy, our method is more flexible, noninvasive, and therefore without risk.

Robust colon residue detection using vector-quantization-based classification for virtual colonoscopy

We present an automatic and robust tagged-residue detection technique using vector quantization based classification. This technique enables electronic cleansing even on poorly tagged datasets, leading to more effective virtual colonoscopy. In order to reduce the sensitivity towards intensity variation among the tagged residual material, we use a multi-step technique. First, we apply classification using an unsupervised and self-adapting vector quantization algorithm. Then, we sort the resultant classes by their average intensities. We apply thresholding on these classes based on a conservative threshold. This helps us in differentiating soft tissue inside tagged material from poorly tagged region or noise.

Light Weight Space Leaping using Ray Coherence

We present a space leaping technique for accelerating volume rendering with very low space and run-time complexity. Our technique exploits the ray coherence during ray casting by using the distance a ray traverses in empty space to leap its neighboring rays. Our technique works with parallel as well as perspective volume rendering, does not require any preprocessing or 3D data structures, and is independent of the transfer function. Being an image-space technique, it is independent of the complexity of the data being rendered. It can be used to accelerate both time-coherent and noncoherent animation sequences.

Volume Exploration Made Easy Using Feature Maps

We present a framework that enables an intuitive, feature-centric exploration of segmented volumetric datasets. Our system is geared towards users familiar with the basic elements of volume rendering, but who seek to conduct volume exploration in a guided fashion. It provides the infrastructure to organize features and objects extracted from a volume dataset, via segmentation or otherwise, and provides the functionality to view these features with standard volume rendering tools. A novel aspect of our system is that it does not require a separate binary tag volume to indicate the presence of a feature (or object). Instead, we mark a feature by migrating its density range, including its smooth boundary, to a private interval. This avoids the aliasing problems associated with binary tag volumes as well as the extensive run-time costs incurred to resolve these. In addition, since the smooth boundaries of the features are preserved, any volume renderer can be used for data visualization, without modification.

OpenVL: the open volume library

OpenVL is a modular, extensible, and high performance library for handling volumetric datasets. It provides a standard, uniform, and easy to use API for accessing volumetric data. It allows the volumetric data to be laid out in different ways to optimize memory usage and speed. It supports reading/writing of volumetric data from/to files in different formats using plugins. It provides a framework for implementing various algorithms as plugins that can be easily incorporated into user applications. The plugins are implemented as shared libraries which can be dynamically loaded as needed. OpenVL is developed openly and is a free software available on the web.

Fantastic Voyage of the Virtual Colon

We pioneered a visualization-based alternative to conventional optical colonoscopy, called virtual colonoscopy (VC), for screening patients for colonic polyps, the precursor of colon cancer. Unlike optical colonoscopy, VC is patient friendly since the patient undergoes a less rigorous bowel preparation. VC is also a fast, non-invasive, more accurate, and cost-effective procedure for mass screening of colon polyps. In VC, the patient’s abdomen is imaged by a helical or multi-slice computed tomography (CT) scanner during a 40-second singlebreath-hold. Supine and prone scans are acquired, each typically consists of 350 to 700 axial 512×512 images of sub-millimeter resolution. A 3D model of the colon is then reconstructed from the CT scan by automatically segmenting the colon out of the rest of the abdomen and employing an electronic cleansing algorithm for computer-based removal of the residual material and accurate reconstruction of the soft surface behind the removed material. This is accomplished using a novel segmentation rays algorithm. The visualization software, running on a PC, allows the physician to interactively navigate through the colon using a physically-based navigation system with fast volume rendering for visualization and the center line of the colon as a guide for the navigation. An intuitive user interface with customized tools supports measurements and virtual biopsy to inspect suspicious regions.