Nevoscopy: three-dimensional computed tomography of nevi and melanomas in situ by transillumination

The thickness of a malignant nevus has been found to be an important prognostic factor for patients with melanoma. We have designed a new method of imaging nevi that permits their thickness to be measured in situ. Using fiber optics directed into the surrounding skin, we transilluminate the nevus. Three images are picked up by a digitizing TV camera: the vertical image (90 degrees ), a glancing image (180 degrees ), and one at 45 degrees , obtained by using two front-silvered mirrors held next to the nevus in a "nevoscope." The digitized images are used in a computed tomography algorithm to calculate approximate vertical cross sections of the nevus. The algorithm is one we recently developed to permit reconstruction from a very few projections. Our method is completely noninvasive. It may be used to check all the nevi on a patient. Without excisions, we could establish a baseline three-dimensional shape for each nevus, follow any changes in time, and obtain an early warning of increase in thickness and possible malignancy.     

Bayesian recognition of local 3-d shape by approximating image intensity functions with quadric polynomials

The recognition in image data of viewed patches of spheres, cylinders, and planes in the 3-D world is discussed as a first step to complex object recognition or complex object location and orientation estimation. Accordingly, an image is partitioned into small square windows, each of which is a view of a piece of a sphere, or of a cylinder, or of a plane. Windows are processed in parallel for recognition of content. New concepts and techniques include approximations of the image within a window by 2-D quadric polynomials where each approximation is constrained by one of the hypotheses that the 3-D surface shape seen is either planar, cylindrical, or spherical; a recognizer based upon these approximations to determine whether the object patch viewed is a piece of a sphere, or a piece of a cylinder, or a piece of a plane; lowpass filtering of the image by the approximation. The shape recognition is computationally simple, and for large windows is approximately Bayesian minimum-probability-of-error recognition. These classifications are useful for many purposes. One such purpose is to enable a following processor to use an appropriate estimator to estimate shape, and orientation and location parameters for the 3-D surface seen within a window.     

Fast computation of the difference of low-pass transform

This paper defines the difference of low-pass (DOLP) transform and describes a fast algorithm for its computation. The DOLP is a reversible transform which converts an image into a set of bandpass images. A DOLP transform is shown to require O(N2) multiplies and produce O(N log(N)) samples from an N sample image. When Gaussian low-pass filters are used, the result is a set of images which have been convolved with difference of Gaussian (DOG) filters from an exponential set of sizes. A fast computation technique based on ``resampling' is described and shown to reduce the DOLP transform complexity to O(N log(N)) multiplies and O(N) storage locations. A second technique, ``cascaded convolution with expansion,' is then defined and also shown to reduce the computational cost to O(N log(N)) multiplies. Combining these two techniques yields an algorithm for a DOLP transform that requires O(N) storage cells and requires O(N) multiplies.     

Machine vision applied to vehicle guidance

Research on the semiautonomous operation of mobile robots in typical pathways is described. The image of the pathway will consist of two nearly vertical lines bounding a region with little texture (the pathway) after correction for perspective. In order to identify pathway boundaries, regions in the image space are examined using an edge detection algorithm, edges between regions are determined by the algorithm, and those corresponding to straight or nearly straight lines with large slope (path boundaries) are identified by means of the Hough transform. Once the path boundaries are identified, the horizontal distance from camera to road edge is determined. Next, a method to detect cubics in the roadway (i.e., obstacles) is presented. The region of interest in the roadway (from the camera to some predetermined distance in front of it) is known from the path boundary algorithm. The interior of this region is examined for edges. If edges are detected, it means that obstacles or shadows are present. A method to separate obstacles from shadows using stere vision is then presented.     

The structure of nanocomposite 1D cationic conductor crystal@SWNT

Nanocomposites consisting of one-dimensional (1D) crystals of the cationic conductors CuI, CuBr and AgBr inside single-walled carbon nanotubes, mainly (n, 0), were obtained using the capillary technique. 1D crystal structure models were proposed based on the high resolution transmission electron microscopy performed on a FEI Titan 80-300 at 80 kV with aberration correction. According to the models and image simulations there are two modifications of 1D crystal: hexagonal close-packed bromine (iodine) anion sublattice (growth direction <001>) and 1D crystal cubic structure (growth direction <112>) compressed transversely to the nanotube (D(m) ～1.33 nm) axis. Tentatively this kind of 1D crystal can be considered as monoclinic. One modification of the anion sublattice reversibly transforms into the other inside the nanotube, probably initiated by electron beam heating. As demonstrated by micrographs, copper or silver cations can occupy octahedral positions or are statistically distributed across two tetrahedral positions. A 1DAgBr@SWNT (18, 0; 19, 0) pseudoperiodic 'lattice distortion' is revealed resulting from convolution of the nanotube wall function image with 1D cubic crystal function image.