Development of Digital Image Processing Algorithms via FPGA Implementation

The first quarter of 2007 started as an active season for the real-time image processing community. The annual SPIE Conference on Real-Time Image Processing, which was held in San Jose, CA, highlighted recent developments in this growing branch of image processing. As one of the few regular events with its focus matching that of JRTIP, one witnessed the growth in applications of real-time image and video processing. The conference served as an effective instrument to foster the dissemination of recent results and to leverage the cohesion of the real time image processing community. The preparations for the next SPIE Conference on Real-Time Image Processing in 2008 are already progressing and its call-for-papers is included at the end of this issue. In 2007, JRTIP is going to seek involvement of more scientists in the field of real-time image and video processing. After 1 year of operation, the editors-in-chief have started to recruit additional associate editors to enlarge the expertise base of the editorial board, to solicit manuscript submissions from potential authors, and to make the reviewing process more efficient. Interested colleagues should contact the editors-in-chief expressing their Associate Editor candidature in support of one or more fields within the focus of JRTIP. In addition, they wish to encourage researchers to act as guest editors for special issues on any real-time image or video processing related topics of interest to them. It is possible to schedule two special issues in 2007, provided that a draft call-for-papers is sent to the editors-in-chief as soon as possible. The third issue of JRTIP presents four articles, two are survey papers and two are original research papers. These articles address different real-time image processing perspectives ranging from algorithmic to hardware issues. The first survey paper deals with the real-time filtering in 2D and 3D using ordered statistics that are supported by a rich set of experimental results derived from several algorithmic variations. Performance measurements are used to demonstrate the effect of noise suppression and preservation of fine image structures and to show the DSP implementation of denoising filters. The second survey paper introduces a non-invasive analysis of dynamic processes in living cells using microscopic computer vision techniques for understanding the interactions between cellular and molecular functional units and for providing novel insights into the cell biology. The third article is an original research paper covering a machine vision application involving vision-based position sensing for aerial refueling of unmanned aerial vehicles in real-time. A full image processing chain is investigated and tested in a MATLAB/Simulink soft real-time environment as well as in a Linux/RTAI hard real-time environment. The feasibility of the real-time implementation is shown using off-the-shelf commercial hardware. The fourth article is another original research paper describing a hardware architecture for real-time fractal coding for image compression. The introduced parallel hardware architecture contains units for domain M. F. Carlsohn (&) Ingenieurberatung fur Computer Vision und Bildkommunikation, Am Heiddamm 36g, 28355 Bremen, Germany e-mail: matthias.carlsohn@t-online.de

Photonic crystal fiber as a new type of optical fiber has been extensively applied because of its unique properties. The effective mode area of optical fiber is an important parameter, which has a great influence on the performance of optical fiber. In this study, digital image processing algorithm was used for preprocessing to improve the accuracy of calculation of mode field area. Then the effective mode field area of optical fiber was calculated using Matlab based Gauss fitting method. Take single-mode fiber G.652 as an example, the effective mode field area was calculated using the traditional algorithm and digital image processing algorithm respectively. It was found that the results obtained using digital image processing algorithm were within the allowed error range, suggesting the effectiveness of the algorithm. Then the calculation of the effective mode area of the triangular lattice photonic crystal fiber further verified the reliability of the algorithm.

Reconfigurable algorithm for digital image processing is now a growing area of research in real time embedded systems. The Edge Detection Algorithms are one of the primary algorithms for object detection through segmentation and extraction in digital image processing. These algorithms are usually used in software but can also be implemented for special purposes in hardware including high processing speed and good accuracy. The limitation of the processor speed affects the software implementation of image processing algorithms. The advances in VLSI technology have solved this. This project uses the Hardware Description Language (HDL) to implement the Sobel edge detection algorithm for Field Programmable Gate Array devices (FPGA) and focuses on the outstanding features of FPGA technology. The analysis shows that the hardware Sobel edge operator implements provide a higher speed than the simulation software. The proposed implementation uses a modified architecture that reduces hardware resources effectively. The images are transmitted through serial UART communication from PC to FPGA computer.

Roughness and condition prediction models for airfield pavements using digital image processing

In this study, dimensions, mass and shape characteristics of five commercial loquat cultivars (‘Akko XIII’, ‘Champagne de Grasse’, ‘Guzelyurt 6’, ‘Hafifcukurgobek’ and ‘Sayda’) and two genotypes (‘KKTC3’ and ‘KKTC4’) were determined. Physical properties such as dimension and shape were determined both by digital caliper and image processing to compare each other. Among the cultivars and genotypes tested, ‘KKTC4’ (58.88 mm) had the highest length while ‘KKTC3’ had the highest major diameter, minor diameter, mass, geometric mean diameter, surface area and volume, with a mean of 46.31 mm, 43.61 mm, 60.12 g, 48.69 mm, 74.82 cm2, and 61.25 cm3, respectively. The greatest value of elongation and the smallest value of sphericity were obtained from ‘Akko XII’ with 1.33, and ‘Akko XII’ was more oblong than the other cultivars and genotypes. The smallest value of elongation and the greatest value of sphericity were obtained from ‘Sayda’ with 1.12 and 91.16%, respectively. The results of comparison between the digital caliper and image processing showed that using the image processing method could potentially be rapid and alternative to the conventional measurement method. Key words: Eriobotrya japonica, image processing, loquat, physical properties.

The digital image acquisition and processing technology already mature also in domains and so on visual communication obtains day by day[1]. However, the image acquisition and processing technology had many kinds, for example white balance processing and image denoising methods. The traditional image acquisition and processing systems are implemented using software by the PC or DSP chip[2], and sometimes software in series can not meet the requirement of high speed image processing. this article has given FPGA in the image acquisition and processing simple application, applied FPGA in the image acquisition and processing to be possible to enhance the image processing the speed, reduced takes the logical unit. The system designed by above-mentioned way achieves a satisfying effect by experimental verification.

Stochastic encoding represents a value using the probability of ones in a random bit stream. Computation based on this encoding has good fault-tolerance and low hardware cost. However, one of its major issues is long processing time. We have to use a long enough bit stream to represent a value to guarantee that random fluctuations introduce only small errors to final computation results. For example, for most digital image processing algorithms, we need a 512-bit stream to represent an 8-bit pixel value stochastically to guarantee that the final computation error is less than 5%. To solve this issue, this paper proposes to share bits between adjacent bit streams to represent adjacent deterministic values. For example, in image processing applications, the bit stream which represents the current pixel value can share parts of the bits in the bit stream which represents the previous pixel value. We use an image contrast stretching algorithm to evaluate this method. Our experimental results show that the proposed methods can improve the performance by 90%.

Digital radiography systems are capable of capturing a wide range of x‐ray exposures in a single image. The wide latitude of these capture devices dictates that images are digitally processed before display. Attempting to directly print or display the full dynamic range of the captured image will result in an image with low contrast that is unacceptable for diagnostic interpretation. Image processing algorithms are used to identify the range of exposures (represented as code values) that correspond to the diagnostically relevant regions, followed by a rendering process to transform signal values in the diagnostically relevant regions to display values. The image processing algorithms need to perform these steps completely automatically, or with a minimum amount of human intervention, to facilitate technologists' workflow. Image processing performance is ultimately gauged by the ability to automatically and consistently generate images of diagnostic quality. Recognition of the diagnostically relevant exposures consists of segmentation of the collimation, direct exposure, and anatomy regions. This is a challenging computer‐recognition problem because several factors cause the characteristics of the image, and corresponding code value histogram, to vary. These factors include the body part and projection, exposure technique, collimation, use of anti‐scatter devices, use of contrast media, etc. Robust segmentation will also automatically recognize other features such as prosthetic implants, pacemakers, and left‐right markers. Once the image is properly segmented, the signal values for the diagnostically relevant regions are transformed into values for display. Rendering the region of interest for display generally consists of establishing the image grayscale rendition, and signal equalization and contrast enhancement, which is based on spatial frequency decomposition and reconstruction. Because of the tremendous flexibility to render images to different aims, understanding the rendering preferences of radiologists is a critical component of digital radiography image processing. Establishing the aim appearance for different exam types, then adapting the image processing algorithms and database parameters to automatically and consistently deliver the preferred rendering, will affect both the technologists' and the radiologists' satisfaction with the capture device. This course will review fundamentals of image processing for digital radiography. Specific course content will include a discussion of radiographic image segmentation, correlation of image content with different regions of the histogram, and a review of image‐rendering functions, including the tone scale, spatial frequency methods to enhance contrast, and signal equalization. The course will also include a review of the role of image processing in the end‐to‐end image chain (capture through interpretation), with considerations for image quality performance of different capture and display devices, the effect of the human visual system and ambient viewing conditions, and grayscale calibration.

SP21 Needle architecture: vulnerability of different nerves to intrafascicular injection with different needle bevels

Worldwide, breast cancer is the most common type of cancer that mainly affects women. Several diagnosis techniques based on optical instrumentation and image analysis have been developed, and these are commonly used in conjunction with conventional diagnostic devices such as mammographs, ultrasound, and magnetic resonance imaging of the breast. The cost of using these instruments is increasing, and developing countries, whose deaths indices due to breast cancer are high, cannot access conventional diagnostic methods and have even less access to newer techniques. Other studies, based on the analysis of images acquired by traditional methods, require high resolutions and knowledge of the origin of the captures in order to avoid errors. For this reason, the design of a low-cost diffuse optical mammography system for biomedical image processing in breast cancer diagnosis is presented. The system combines the acquisition of breast tissue photographs, diffuse optical reflectance (as a biophotonics technique), and the processing of digital images for the study and diagnosis of breast cancer. The system was developed in the form of a medical examination table with a 638 nm red-light source, using light-emitted diode technology (LED) and a low-cost web camera for the acquisition of breast tissue images. The system is automatic, and its control, through a graphical user interface (GUI), saves costs and allows for the subsequent analysis of images using a digital image-processing algorithm. The results obtained allow for the possibility of planning in vivo measurements. In addition, the acquisition of images every 30° around the breast tissue could be used in future research in order to perform a three-dimensional (3D) reconstruction and an analysis of the captures through deep learning techniques. These could be combined with virtual, augmented, or mixed reality environments to predict the position of tumors, increase the likelihood of a correct medical diagnosis, and develop a training system for specialists. Furthermore, the system allows for the possibility to develop analysis of optical characterization for new phantom studies in breast cancer diagnosis through bioimaging techniques.

Introduction Peripheral arterial disease (PAD) is a fairly common degenerative vascular condition in diabetic patients that leads to inadequate blood flow (BF), this disease is mainly due to atherosclerosis that causes chronic narrowing of arteries, which can precipitate acute thrombotic events. In patients with diabetes, atherosclerosis is the main reason for reducing life expectancy, as long as diabetic nephropathy and retinopathy are the largest contributors to end-stage renal disease and blindness, respectively. Objective This was an assessment of dilatation of the blood vessels on diabetic patients vs. healthy volunteers by using digital processing of imaging’s. Materials and Methods The study subject was ultrasound imaging processing of blood vessels dilation on low extremities of diabetic patients, the results were compared with ultrasound images of healthy subjects. Results The digital images processing suggests that there is a significant difference among images experimental of the diabetic group and healthy volunteers’ images, the control group. Discussion The digital imaging processing performed in the Matlab platform is an adequate procedure for blood vessels dilation analysis of the ultrasound images taken from the lower extremities in diabetic patients.

In this paper we introduce a new real-time parallel image processing system based on an IEEE1394-based PC-cluster. PC-clusters are becoming standard tools in the field of computer vision, especially for real-time multi-view image processing. To reduce the cost of system construction, we have employed IEEE1394 bus as a base network of the PC-cluster. IEEE1394 bus has the throughput of up to 400Mbps, which is large enough to transfer uncompressed video data, and supports not only asynchronous data transfer but isochronous one, which is inevitable for real-time data transfer. In addition, since we have several products of IEEE1394-based digital cameras, we can easily integrate real-time image processing system including camera systems. This paper presents an overview of our real-time parallel image processing system on an IEEE1394based PC-cluster, referring to basic features of IEEE1394. We also show some experimental results to evaluate the performance of the PC-cluster, comparing it with the performance of our previous Myrinet-based PC-cluster. Then we show a prototypical application of the system, which is real-time volume reconstruction from multi-view images by visual cone intersection method.

This article presents a fast and highly efficient algorithm developed to reconstruct a three-dimensional (3D) volume with a high spatial precision from a set of field ion microscopy (FIM) images, and specific tools developed to characterize crystallographic lattice and defects. A set of FIM digital images and image processing algorithms allow the construction of a 3D reconstruction of the sample at the atomic scale. The capability of the 3D FIM to resolve the crystallographic lattice and the finest defects in metals opens a new way to analyze materials. This spatial precision was quantified on tungsten, analyzed at different analyzing conditions. A specific data mining tool, based on Fourier transforms, was also developed to characterize lattice distortions in the reconstructed volumes. This tool has been used in simulated and experimental volumes to successfully locate and characterize defects such as dislocations and grain boundaries.

Zoonosis in Colombia is a subject of high epidemiological importance due to the transmission of pathogens to humans. T. gondii is an intracellular parasite (transmitted to human population) that causes various congenital diseases; including chorioretinitis, microcephaly, hydrocephaly, encephalomyelitis, mental retardation and hepatosplenomegaly, among others. The parasite affects also immunocompromissed patients that suffer from any type of cancer, organ transplantation and HIV. A deeper study on toxoplasmosis requires analysis of the developmental stages of the parasite, including the cyst stage, which allows it to evade the immune system and to get established in the host organism permanently. The goal of this work was to develop a digital image processing algorithm based on texture recognition to allow automatic detection of the cysts associated to pathogen T. gondii from isolated Colombian native strains in meat samples for human consumption, using computational software Matlab 2013, specifically through the Image Processing Toolbox. This algorithm allowed to segment the biological images, highlighting typical features of the cysts for calculate its eccentricity as a circularity criterion and finally to validate the efficiency of the algorithm.

Solar-blind ultraviolet(UV) imaging is an effective discharge detection method. To quantitatively describe discharge characteristics, a new method using digital image processing algorithms to extract image parameters for characterizing discharge characteristics is proposed. The discharge areas were extracted by threshold segmentation, morphological filtering, and small area elimination algorithms on UV images. The boundary point coordinates of each discharge area are obtained using a multi area boundary tracking algorithm, and the spot area parameter is defined to quantify the discharge intensity, and the total running time for the above image processing is 0.59 seconds. The parallel line projection algorithm in the Radon algorithm was used to obtain the spatial distribution curve of discharge intensity along the surface of the insulator.