Using shape entropy as a feature to lesion boundary segmentation with level sets

Accurate lesion segmentation in retinal imagery is an area of vast research. Of the many segmentation methods available very few are insensitive to topological changes on noisy surfaces. This paper presents an extension to earlier work on a novel stopping mechanism for level sets. The elementary features scheme (ELS) in [5] is extended to include shape entropy as a feature used to ’look back in time’ and find the point at which the curve best fits the real object. We compare the proposed extension against the original algorithm for timing and accuracy using 50 randomly selected images of exudates with a database of clinician demarcated boundaries as ground truth. While this work is presented applied to medical imagery, it can be used for any application involving the segmentation of bright or dark blobs on noisy images

A Robust Quasi-dense Matching Approach for Underwater Images

While different techniques for finding dense correspondences in images taken in air have achieved significant success, application of these techniques to underwater imagery still presents a serious challenge, especially in the case of “monocular stereo” when images constituting a stereo pair are acquired asynchronously. This is generally because of the poor image quality which is inherent to imaging in aquatic environments (blurriness, range-dependent brightness and color variations, time-varying water column disturbances, etc.). The goal of this research is to develop a technique resulting in maximal number of successful matches (conjugate points) in two overlapping images. We propose a quasi-dense matching approach which works reliably for underwater imagery. The proposed approach starts with a sparse set of highly robust matches (seeds) and expands pair-wise matches into their neighborhoods. The Adaptive Least Square Matching (ALSM) is used during the search process to establish new matches to increase the robustness of the solution and avoid mismatches. Experiments on a typical underwater image dataset demonstrate promising results

Colour constancy using von Kries transformations: colour constancy "goes to the Lab"

Colour constancy algorithms aim at correcting colour towards a correct perception within scenes. To achieve this goal they estimate a white point (the illuminant's colour), and correct the scene for its in uence. In contrast, colour management performs on input images colour transformations according to a pre-established input pro le (ICC pro le) for the given con- stellation of input device (camera) and conditions (illumination situation). The latter case presents a much more analytic approach (it is not based on an estimation), and is based on solid colour science and current industry best practises, but it is rather in exible towards cases with altered conditions or capturing devices. The idea as outlined in this paper is to take up the idea of working on visually linearised and device independent CIE colour spaces as used in colour management, and to try to apply them in the eld of colour constancy. For this purpose two of the most well known colour constancy algorithms White Patch Retinex and Grey World Assumption have been ported to also work on colours in the CIE LAB colour space. Barnard's popular benchmarking set of imagery was corrected with the original imple- mentations as a reference and the modi ed algorithms. The results appeared to be promising, but they also revealed strengths and weaknesses

Lesion boundary segmentation using level set methods

This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided by a gradient map built using a combination of histogram equalization and robust statistics. The stopping mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object. We implement the level set using a fast upwind scheme and compare the proposed method against five other segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician marked-up boundaries as ground truth

Design of Novel Algorithm and Architecture for Gaussian Based Color Image Enhancement System for Real Time Applications

This paper presents the development of a new algorithm for Gaussian based color image enhancement system. The algorithm has been designed into architecture suitable for FPGA/ASIC implementation. The color image enhancement is achieved by first convolving an original image with a Gaussian kernel since Gaussian distribution is a point spread function which smoothen the image. Further, logarithm-domain processing and gain/offset corrections are employed in order to enhance and translate pixels into the display range of 0 to 255. The proposed algorithm not only provides better dynamic range compression and color rendition effect but also achieves color constancy in an image. The design exploits high degrees of pipelining and parallel processing to achieve real time performance. The design has been realized by RTL compliant Verilog coding and fits into a single FPGA with a gate count utilization of 321,804. The proposed method is implemented using Xilinx Virtex-II Pro XC2VP40-7FF1148 FPGA device and is capable of processing high resolution color motion pictures of sizes of up to 1600x1200 pixels at the real time video rate of 116 frames per second. This shows that the proposed design would work for not only still images but also for high resolution video sequences.Comment: 15 pages, 15 figure

Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Deep-sea image processing

High-resolution seafloor mapping often requires optical methods of sensing, to confirm interpretations made from sonar data. Optical digital imagery of seafloor sites can now provide very high resolution and also provides additional cues, such as color information for sediments, biota and divers rock types. During the cruise AT11-7 of the Woods Hole Oceanographic Institution (WHOI) vessel R/V Atlantis (February 2004, East Pacific Rise) visual imagery was acquired from three sources: (1) a digital still down-looking camera mounted on the submersible Alvin, (2) observer-operated 1-and 3-chip video cameras with tilt and pan capabilities mounted on the front of Alvin, and (3) a digital still camera on the WHOI TowCam (Fornari, 2003). Imagery from the first source collected on a previous cruise (AT7-13) to the Galapagos Rift at 86°W was successfully processed and mosaicked post-cruise, resulting in a single image covering area of about 2000 sq.m, with the resolution of 3 mm per pixel (Rzhanov et al., 2003). This paper addresses the issues of the optimal acquisition of visual imagery in deep-seaconditions, and requirements for on-board processing. Shipboard processing of digital imagery allows for reviewing collected imagery immediately after the dive, evaluating its importance and optimizing acquisition parameters, and augmenting acquisition of data over specific sites on subsequent dives.Images from the deepsea power and light (DSPL) digital camera offer the best resolution (3.3 Mega pixels) and are taken at an interval of 10 seconds (determined by the strobe\u27s recharge rate). This makes images suitable for mosaicking only when Alvin moves slowly (≪1/4 kt), which is not always possible for time-critical missions. Video cameras provided a source of imagery more suitable for mosaicking, despite its inferiority in resolution. We discuss required pre-processing and imageenhancement techniques and their influence on the interpretation of mosaic content. An algorithm for determination of camera tilt parameters from acquired imagery is proposed and robustness conditions are discussed

The Perceptual Consequences of Curved Screens

Flat panels are by far the most common type of television screen. There are reasons, however, to believe that curved screens create a greater sense of immersion, reduce distracting reflections, and minimize some perceptual distortions that are commonplace with large televisions. To examine these possibilities, we calculated how curving the screen affects the field of view and the probability of seeing reflections of ambient lights. We find that screen curvature has a small beneficial effect on field of view and a large beneficial effect on the probability of seeing reflections. We also collected behavioral data to characterize perceptual distortions in various viewing configurations. We find that curved screens can in fact reduce problematic perceptual distortions on large screens, but that the benefit depends on the geometry of the projection on such screens