Sharat Chandran

Nadi Tarangini: A Pulse Based Diagnostic System

Ayurveda is a traditional medicine and natural healing system in India. Nadi-Nidan (pulse-based diagnosis) is a prominent method in Ayurveda, and is known to dictate all the salient features of a human body. In this paper, we provide details of our procedure for obtaining the complete spectrum of the nadi pulses as a time series. The system Nadi Tarangini contains a diaphragm element equipped with strain gauge, a transmitter cum amplifier, and a digitizer for quantifying analog signal. The system acquires the data with 16-bit accuracy with practically no external electronic or interfering noise. Prior systems for obtaining the nadi pulses have been few and far between, when compared to systems such as ECG. The waveforms obtained with our system have been compared with these other similar equipment developed earlier, and is shown to contain more details. The pulse waveform is also shown to have the desirable variations with respect to age of patients, and the pressure applied at the sensing element. The system is being evaluated by Ayurvedic practitioners as a computer-aided diagnostic tool.

A high-throughput active contour scheme for segmentation of histopathological imagery

In this paper a minimally interactive high-throughput system which employs a color gradient based active contour model for rapid and accurate segmentation of multiple target objects on very large images is presented. While geodesic active contours (GAC) have become very popular tools for image segmentation, they tend to be sensitive to model initialization. A second limitation of GAC models is that the edge detector function typically involves use of gray scale gradients; color images usually being converted to gray scale, prior to gradient computation. For color images, however, the gray scale gradient image results in broken edges and weak boundaries, since the other channels are not exploited in the gradient computation. To cope with these limitations, we present a new GAC model that is driven by an accurate and rapid object initialization scheme; hierarchical normalized cuts (HNCut). HNCut draws its strength from the integration of two powerful segmentation strategies-mean shift clustering and normalized cuts. HNCut involves first defining a color swatch (typically a few pixels) from the object of interest. A multi-scale, mean shift coupled normalized cuts algorithm then rapidly yields an initial accurate detection of all objects in the scene corresponding to the colors in the swatch. This detection result provides the initial contour for a GAC model. The edge-detector function of the GAC model employs a local structure tensor based color gradient, obtained by calculating the local min/max variations contributed from each color channel. We show that the color gradient based edge-detector function results in more prominent boundaries compared to the classical gray scale gradient based function. By integrating the HNCut initialization scheme with color gradient based GAC (CGAC), HNCut-CGAC embodies five unique and novel attributes: (1) efficiency in segmenting multiple target structures; (2) the ability to segment multiple objects from very large images; (3) minimal human interaction; (4) accuracy; and (5) reproducibility. A quantitative and qualitative comparison of the HNCut-CGAC model against other state of the art active contour schemes (including a Hybrid Active Contour model (Paragios-Deriche) and a region-based AC model (Rousson-Deriche)), across 196 digitized prostate histopathology images, suggests that HNCut-CGAC is able to outperform state of the art hybrid and region based AC techniques. Our results show that HNCut-CGAC is computationally efficient and may be easily applied to a variety of different problems and applications.

Parallel computational geometry of rectangles

Rectangles in a plane provide a very useful abstraction for a number of problems in diverse fields. In this paper we consider the problem of computing geometric properties of a set of rectangles in the plane. We give parallel algorithms for a number of problems usingn processors wheren is the number of upright rectangles. Specifically, we present algorithms for computing the area, perimeter, eccentricity, and moment of inertia of the region covered by the rectangles inO(logn) time. We also present algorithms for computing the maximum clique and connected components of the rectangles inO(logn) time. Finally, we give algorithms for finding the entire contour of the rectangles and the medial axis representation of a givenn × n binary image inO(n) time. Our results are faster than previous results and optimal (to within a constant factor).

Rendering Wave Effects with Augmented Light Field

Ray–based representations can model complex light transport but are limited in modeling diffraction effects that require the simulation of wavefront propagation. This paper provides a new paradigm that has the simplicity of light path tracing and yet provides an accurate characterization of both Fresnel and Fraunhofer diffraction. We introduce the concept of a light field transformer at the interface of transmissive occluders. This generates mathematically sound, virtual, and possibly negative‐valued light sources after the occluder. From a rendering perspective the only simple change is that radiance can be temporarily negative. We demonstrate the correctness of our approach both analytically, as well by comparing values with standard experiments in physics such as the Youngs double slit. Our implementation is a shader program in OpenGL that can generate wave effects on arbitrary surfaces.

Fast, parallel, GPU-based construction of space filling curves and octrees

Space Filling Curves (SFC) are particularly useful in linearization of data living in two and three dimensional spaces and have been used in a number of applications in scientific computing, and visualization. Interestingly, octrees, another versatile data structure in computer graphics, can be viewed as multiple SFCs at varying resolutions, albeit with parent-child relationship. In this paper we provide a parallel implementation of SFCs and octrees on GPUs that rely on algorithms designed to minimize or eliminate communications.

Arterial Pulse System: Modern Methods For Traditional Indian Medicine

Ayurveda is one of the most comprehensive healing systems in the world and has classified the body system according to the theory of Tridosha to overcome ailments. Diagnosis similar to the traditional pulse-based method requires a system of clean input signals, and extensive experiments for obtaining classification features. In this paper we briefly describe our system of generating pulse waveforms and use various feature detecting methods to show that an arterial pulse contains typical physiological properties. The beat-to-beat variability is captured using a complex B-spline mother wavelet based peak detection algorithm. We also capture - to our knowledge for the first time - the self- similarity in the physiological signal, and quantifiable chaotic behavior using recurrence plot structures.

High-Throughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical Normalized Cuts

We present a system for accurately quantifying the presence and extent of stain on account of a vascular biomarker on tissue microarrays. We demonstrate our flexible, robust, accurate, and high-throughput minimally supervised segmentation algorithm, termed hierarchical normalized cuts (HNCuts) for the specific problem of quantifying extent of vascular staining on ovarian cancer tissue microarrays. The high-throughput aspect of HNCut is driven by the use of a hierarchically represented data structure that allows us to merge two powerful image segmentation algorithms-a frequency weighted mean shift and the normalized cuts algorithm. HNCuts rapidly traverses a hierarchical pyramid, generated from the input image at various color resolutions, enabling the rapid analysis of large images (e.g., a 1500 × 1500 sized image under 6 s on a standard 2.8-GHz desktop PC). HNCut is easily generalizable to other problem domains and only requires specification of a few representative pixels (swatch) from the object of interest in order to segment the target class. Across ten runs, the HNCut algorithm was found to have average true positive, false positive, and false negative rates (on a per pixel basis) of 82%, 34%, and 18%, in terms of overlap, when evaluated with respect to a pathologist annotated ground truth of the target region of interest. By comparison, a popular supervised classifier (probabilistic boosting trees) was only able to marginally improve on the true positive and false negative rates (84% and 14%) at the expense of a higher false positive rate (73%), with an additional computation time of 62\% compared to HNCut. We also compared our scheme against a k-means clustering approach, which both the HNCut and PBT schemes were able to outperform. Our success in accurately quantifying the extent of vascular stain on ovarian cancer TMAs suggests that HNCut could be a very powerful tool in digital pathology and bioinformatics applications where it could be used to facilitate computer-assisted prognostic predictions of disease outcome.

A parallel algorithm for enclosed and enclosing triangles

We consider the problems of computing the largest area triangle enclosed within a given n-sided convex polygon and the smallest area triangle which encloses a given convex polygon. We show that these problems are closely related by presenting a single sequential linear time algorithm which essentially solves both problems simultaneously. We also present a cost-optimal parallel algorithm that solves both of these problems in O(log log n) time using n/log log n processors on a CRCW PRAM. In order to achieve these bounds we develop new techniques for the design of parallel algorithms for computational problems involving the rotating calipers method.

A weighted mean shift, normalized cuts initialized color gradient based geodesic active contour model: applications to histopathology image segmentation

While geodesic active contours (GAC) have become very popular tools for image segmentation, they are sensitive to model initialization. In order to get an accurate segmentation, the model typically needs to be initialized very close to the true object boundary. Apart from accuracy, automated initialization of the objects of interest is an important pre-requisite to being able to run the active contour model on very large images (such as those found in digitized histopathology). A second limitation of GAC model is that the edge detector function is based on gray scale gradients; color images typically being converted to gray scale prior to computing the gradient. For color images, however, the gray scale gradient results in broken edges and weak boundaries, since the other channels are not exploited for the gradient determination. In this paper we present a new geodesic active contour model that is driven by an accurate and rapid object initialization scheme-weighted mean shift normalized cuts (WNCut). WNCut draws its strength from the integration of two powerful segmentation strategies-mean shift clustering and normalized cuts. WNCut involves first defining a color swatch (typically a few pixels) from the object of interest. A multi-scale mean shift coupled normalized cuts algorithm then rapidly yields an initial accurate detection of all objects in the scene corresponding to the colors in the swatch. This detection result provides the initial boundary for GAC model. The edge-detector function of the GAC model employs a local structure tensor based color gradient, obtained by calculating the local min/max variations contributed from each color channel (e.g. R,G,B or H,S,V). Our color gradient based edge-detector function results in more prominent boundaries compared to classical gray scale gradient based function. We evaluate segmentation results of our new WNCut initialized color gradient based GAC (WNCut-CGAC) model against a popular region-based model (Chan & Vese) on a total of 60 digitized histopathology images. Across a total of 60 images, the WNCut-CGAC model yielded an average overlap, sensitivity, specificity, and positive predictive value of 73%, 83%, 97%, 84%, compared to the Chan & Vese model which had corresponding values of 64%, 75%, 95%, 72%. The rapid and accurate object initialization scheme (WNCut) and the color gradient make the WNCut-CGAC scheme, an ideal segmentation tool for very large, color imagery.

Retrieving faces by the PIFS fractal code

The use of fractals in computer graphics and vision in modeling unstructured images, and compressing images, is well known. However the use of fractals for indexing images in content based retrieval of video and static images has not received as much attention. In this paper, we provide the theoretical arguments to justify their use in image retrieval. Our web-enabled implementation on the FERET database shows encouraging results.