A comparison of methods and computation for multi-resolution low- and band-pass transforms for image processing

In this chapter, we become familiar with image analysis and processing. We learn definition and computation of two of the most popular 2D orthogonal image transformations, the 2D discrete Fourier transform (DFT) and 2D discrete cosine transform (DCT). We see how to use them for image filtering in frequency domain. Next, we find out the definition of 2D convolution of matrices and their usage for image filtering, i.e. calculation of local weighted mean values of image pixels. We learn 2D filter weights, the most frequently used for image smoothing (de-noising) and edge detection (masks of Sobel, Prewitt, Roberts, Laplacian). We become also familiar of some non-linear image processing with median and morphological filters (erosion, dilatation). In this chapter, principles of 2D wavelet transform of an image using pair of wavelet low- and high-pass filter are presented in an image processing example/exercise in an image processing example/exercise. Basics of JPEG standard (compression of still pictures) and MPEG standard (compression of moving pictures, i.e., movies) are summarized. Finally, example of image watermarking is shown.

The advancement of high-content optical microscopy has enabled the acquisition of very large three-dimensional (3D) image datasets. The analysis of these image volumes requires more computational resources than a biologist may have access to in typical desktop or laptop computers. This is especially true if machine learning tools are being used for image analysis. With the increased amount of data analysis and computational complexity, there is a need for a more accessible, easy-to-use, and efficient network-based 3D image processing system. The distributed and networked analysis of volumetric image data (DINAVID) system was developed to enable remote analysis of 3D microscopy images for biologists. We present an overview of the DINAVID system and compare it to other tools currently available for microscopy image analysis. DINAVID is designed using open-source tools and has two main sub-systems, a computational system for 3D microscopy image processing and analysis and a 3D visualization system. DINAVID is a network-based system with a simple web interface that allows biologists to upload 3D volumes for analysis and visualization. DINAVID enables the image access model of a center hosting image volumes and remote users analyzing those volumes, without the need for remote users to manage any computational resources. The DINAVID system, designed and developed using open-source tools, enables biologists to analyze and visualize 3D microscopy volumes remotely without the need to manage computational resources. DINAVID also provides several image analysis tools, including pre-processing and several segmentation models.

Received: 2018-06-07 | Accepted: 2018-06-19 | Available online: 2018-06-30 https://doi.org/10.15414/afz.2018.21.02.77-83 The aim of this study was to distinguishing between kernels of maize hybrids by the use of image analysis tools. We analyzed 10 registered Zea mays L. hybrids (5 – dent, 2 – semi-flint to flint, and 3 – semi-flint to dent type). Different parameters on ventral, dorsal, corolla side, and lateral side cross section of kernel were measured. Sample per each hybrid comprised 50 maize kernels. Acquired bio-images were processed by software Zeiss AxioVision Rel. 4.8. We analyzed the segmented regions of interest on the kernels. The data for area (mm 2 ), height and width (mm) were gathered from these regions. The hybrid ZE EDOX significantly differed (p < 0.05) from all other hybrids almost in all traits. It is the hybrid with the smallest area of the whole kernel, floury endosperm proportion, and depressed part on corolla. The new trait the area of the depressed part on the kernel corolla was measured. The hybrids with smaller proportion of floury endosperm had smaller area of depressed part, and vice versa. The image analysis methods can usefully contribute to selection of proper hybrids for different types of use. Keywords: maize, Zea mays L., kernel, image analysis References BLASCHKE, T. et al. (2014) Geographic Object-Based Image Analysis – Towards a new paradigm. In ISPRS Journal of Photogrammetry and Remote Sensing, vol. 87, pp. 180–191. https://doi.org/10.1016/j.isprsjprs.2013.09.014 DE CARVALHO, M. L. M., VAN AELST, A. C., VAN ECK, J. W., HOEKSTRA, F. A. (1999) Pre-harvest stress cracks in maize (Zea mays L.) kernels as characterized by visual, X-ray and low temperature scanning electron microscopical analysis: effect on kernel quality. In Seed Science Research, vol. 9, pp. 227–236. DELL´AQUILA, A. (2006) Computerised seed imaging: a new tool to evaluate germination quality. In Communications in Biometry and Crop Science, vol. 1, no. 1, pp. 20–31. ERASMUS, C. (2003) Maize kernel translucency measurement by Image Analysis and its relationship to vitreousness and dry milling performance. PhD thesis. Pretoria: University of Pretoria. FOX, G. and MANLEY, M. (2009) Hardness methods for testing maize kernels. In Journal of Agricultural and Food Chemistry, vol. 57, no. 13, pp. 5647–5657. doi: https://doi.org/10.1021/jf900623w GLASBEY, C. A. and HORGAN, G. W. (2001) Image analysis in agriculture research. In Quantitative Approaches in System Analysis, special issue, vol. 23, pp. 43–54. GUELPA, A., DU PLESIS, A., KIDD, M., MANLEY, M. (2015) Nondestructive estimation of maize (Zea mays L.) kernel hardness by means of an X-ray micro-computed tomography (μCT) density calibration. In Food and Bioprocess Technology, vol. 8, no. 6, pp. 1419–1429. JANDA, J. and MICHALEC, V. (1982) Mayze. Bratislava: Priroda. 408 p. MUTTERER, J. and ZINCK, E. (2013) Quick‐and‐clean article figures with FigureJ. In Journal of Microscopy, vol. 252, pp. 89– 91. doi: https://doi.org/10.1111/jmi.12069 ROBUTTI, J. L., HOSENEY, R. C. and WASSOM, C. E. (1974). Modified opaque-2 corn endosperms. ll. Structure viewed with a scanning electron microscope. In American Association of Cereal Chemists, vol. 51, pp. 173–180. RODRIGUEZ-PULIDO, F. J. et al. (2012) Ripeness estimation of grape berries and seeds by image analysis. In Computers and Electronics in Agriculture, vol. 82, pp. 128–133. SCHINDELIN, J., RUEDEN, C. T., HINER, M. C., ELICEIRI, K. W. (2015) The ImageJ ecosystem: An open platform for biomedical image analysis. In Mol. Reprod. Dev., vol. 82, pp. 518–529. doi: https://doi.org/10.1002/mrd.22489 UCHIDA, S. (2013) Image processing and recognition for biological images. In Development, growth and differentiation, vol. 55, pp. 523–549. doi: https://doi.org/10.1111/dgd.12054 WATSON, S. A. (1987) Structure and composition. In WATSON, S. A. and RAMSTAD, P. E. (Eds.). Corn chemistry and technology. St. Paul: American Association of Cereal Chemists. WIWART M. et al. (2012) Identification of hybrids of spelt and wheat and their parental forms using shape and color descriptors. In Computers and Electronics in Agriculture, vol. 83, pp. 68–76. https://doi.org/10.1016/j.compag.2012.01.015

P-glycoprotein is implicated in clinical drug resistance; thus, rapid quantitative analysis of its expression and activity is of paramout importance to the design and success of novel therapeutics. The scope for the application of quantitative imaging and image analysis tools in this field is reported here at "proof of concept" level. P-glycoprotein expression was utilized as a model for quantitative immunofluorescence and subsequent spatial intensity distribution analysis (SpIDA). Following expression studies, p-glycoprotein inhibition as a function of verapamil concentration was assessed in two cell lines using live cell imaging of intracellular Calcein retention and a routine monolayer fluorescence assay. Intercellular and sub-cellular distributions in the expression of the p-glycoprotein transporter between parent and MDR1-transfected Madin-Derby Canine Kidney cell lines were examined. We have demonstrated that quantitative imaging can provide dose-response parameters while permitting direct microscopic analysis of intracellular fluorophore distributions in live and fixed samples. Analysis with SpIDA offers the ability to detect heterogeniety in the distribution of labeled species, and in conjunction with live cell imaging and immunofluorescence staining may be applied to the determination of pharmacological parameters or analysis of biopsies providing a rapid prognostic tool.

The recent development of complex chemical and small interfering RNA (siRNA) collections has enabled large-scale cell-based phenotypic screening. High-content and high-throughput imaging are widely used methods to record phenotypic data after chemical and small interfering RNA treatment, and numerous image processing and analysis methods have been used to quantify these phenotypes. Currently, there are no standardized methods for evaluating the effectiveness of new and existing image processing and analysis tools for an arbitrary screening problem. We generated a series of benchmarking images that represent commonly encountered variation in high-throughput screening data and used these image standards to evaluate the robustness of five different image analysis methods to changes in signal-to-noise ratio, focal plane, cell density and phenotype strength. The analysis methods that were most reliable, in the presence of experimental variation, required few cells to accurately distinguish phenotypic changes between control and experimental data sets. We conclude that by applying these simple benchmarking principles an a priori estimate of the image acquisition requirements for phenotypic analysis can be made before initiating an image-based screen. Application of this benchmarking methodology provides a mechanism to significantly reduce data acquisition and analysis burdens and to improve data quality and information content.

Existing bridges are aging and deteriorating. Furthermore, large number of bridges exist in transportation networks meanwhile maintenance budgets are being squeezed. This state of affairs necessities the development of automatic bridge defects evaluation model using computer vision technologies to overcome the limitations of visual inspection. The digital images are prone to degradation by noises during the image acquisition phase. The absence of efficient bridge defects image restoration method results in inaccurate condition assessment models and unreliable bridge management systems. The present study introduces a self-adaptive two-tier method for detection of noises and restoration of bridge defects images. The first model adopts Elman neural network coupled with invasive weed optimization algorithm to identify the type of noise that corrupts images. In the second model, moth-flame optimization algorithm is utilized to design a hybrid image filtering protocol that involves an integration of spatial domain and frequency domain filters. The proposed detection model was assessed through comparisons with other machine learning models as per split validation and tenfold cross validation. It attained the highest classification accuracies, whereas the accuracy, sensitivity, specificity, precision, F-measure and Kappa coefficient are 95.28%, 95.24%, 98.07%, 95.25%, 95.34%. 95.43% and 0.935, respectively in the separate noise recognition module. The capabilities of the proposed restoration model were evaluated against some well-known good-performing optimization algorithms in addition to some conventional restoration models. Moth-flame optimization algorithm outperformed other restoration models, whereas peak signal to noise ratio, mean-squared error, normalized absolute error and image enhancement factor are 25.359, 176.319, 0.0585 and 7.182, respectively.

Non-linear filters have proved to be powerful tools in image analysis, enhancement and restoration. Filters such as the ranked order, median, alpha-trimmed mean, weighted median and the class of morphological filters have been investigated thoroughly and have been shown to be indispensable in certain image processing applications. Another interesting, though unrelated, research area has been the use of fuzzy set theoretic operations in image processing. The underlying idea in employing the fuzzy set theory is to employ fuzzy membership grades to represent image characteristics and features. This paper seeks to unify these two methodologies resulting in an interesting approach to image enhancement problems. The emphasis is on using non-linear operations to generate a local fuzzy property plane. This is then used as an adaptive filter based on local image characteristics. In this paper, we propose a novel localized filtering approach similar to non- linear filters, the point of departure being that we work in the fuzzy property plane rather than the image domain itself. For instance, we may realize a fuzzy implementation based on the median filter which traditionally uses actual pixel intensities, to generate fuzzy memberships instead. In our experiments, we consider a locally adaptive contrast enhancement of x-ray images typically having low contrast. The results are compared with traditional enhancement techniques, such as histogram equalization. The results provide a better subjective quality as compared to other approaches as is also evident from the histogram distribution of the processed images.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Image analysis tools are currently in use for a variety of applications in the fields of forensics and security printing. Objective, quantitative data allow for characterization and comparison of a variety of image attributes.In forensics, document examiners can use quantitative image analysis systems in authentication, detection and analysis of forgeries, counterfeits and altered documents. Statistical analysis enables population characterization that can help in forgery detection and supports the potential for source identification. In security printing, image analysis tools can be applied to measure the quality of printed output and assess wear for bank notes.Flexible hardware options allow for a broad application of image quality measurements in a variety of image analysis situations. Camera and scanner-based image quality systems can be used for quantitative analysis of image content. A laser-based system can be used for height and depth profiling. Oblique angle illumination and scanning can be used for impression detection. A flatbed scanner can be used in transmission mode for the evaluation of bank note wear and for watermark analysis.In this paper, we will be discussing the use of a variety of hardware configurations for image analysis in forensics and security printing.

Automated determination of poplar chip size distribution based on combined image and multivariate analyses

A new graphical tool (Multimedia University’s RSIMANA—Remote-Sensing Image Analyzer) developed for image analysis is described in this paper. MATLAB and ENVI are some of the commercially available tools in the market that aid in image processing and analysis. But their current versions are of limited assistance in image analysis; for example, MATLAB can extract the area of irregular objects and patterns in images, but not their length. ENVI is more focused on image processing than on image analysis functions. Other commercially available tools are also prohibitively expensive. This indicates the need to develop a userfriendly graphical tool that meets research objectives in the educational environment.

Bilateral filter (BF) performs sharpness enhancement and noise removal by using two Gaussian filters, the domain filter in spatial domain and the range filter in intensity domain. To apply the BF to infrared (IR) small target detection, the standard deviation of the two Gaussian filters need to be changed adaptively between the background region and the target region. This paper presents a new BF for small target detection with the adaptive standard deviation based on the analysis of the edge component, also having the variable filter size. At first, threshold of pixel edge components for four directions decides whether any pixel is potential small targets or not. For the proposed BF operation for the potential small target pixels, its edge component is mapped to two standard deviations of the domain filter and the range filter in the proposed BF by mapping function. When the BF comes to a target region, the two standard deviations increase in proportion to the edge component to blur the small targets. To further blur the small targets, the filter size of the BF also increases by its edge component. This enables the BF to perform better and become more suitable in the field of small target detection Experimental results demonstrate that the proposed method is more robust and efficient than the conventional methods.

Traditional methods of collecting quantitative petrographic data from thin sections (modal mineralogy, size distribution, shapes, etc.) are time- and labor-intensive, and rarely have sample sizes adequate to statistically describe complex rocks (i.e. volcanic rocks). Although manual counting and measurements are now routinely supplemented by digital image analysis, the majority of quantitative petrographic studies still go through a manual digitization stage where object classes are traced before further analyses. This is a major rate-limiting step that reproduces the same problems of small n-values resulting from significant effort. We have valuated the potential and limitations of using the Trainable Weka Segmentation (TWS) plugin within the commonly used ImageJ / Fiji digital image analysis and processing environment. Specifically, we have assessed their capacity to classify, segment, and threshold user-defined petrographic features from a suite of images of progressively more complex volcanic rocks to accelerate the collection of quantitative petrographic data. TWS uses a fast-random-forest algorithm to classify an image based on a set of training pixels selected by the user - in this case different mineral phases, vesicles, etc. Training of the classifier is intuitive and fast. For example, three classes each with eleven training spots are classified in less than 1 minute for a medium to high-resolution image. Eight plane polarized light photomicrographs with increasing crystallinity and complexity were classified (i.e. trained) and automatically segmented using TWS. Samples where the assigned classes have distinct, homogeneous RGB values and sharp boundaries are successfully classified with TWS. However, samples where the classes are heterogeneous but similar, as a result of alteration for example, are not adequately classified. Once classified, two major efficiency gains are possible: (1) the classifier can be saved and applied again to any similar sample, and (2) the segmented image is immediately available for thresholding in ImageJ / Fiji (i.e. separating into class-specific images) without manual tracing or cut-and-paste. The thresholded images can then be measured using the image analysis tools in ImageJ / Fiji (e.g., dimensions, area, circularity, long-axis orientation, etc.).

The authors present OLIVE, an object-oriented language for machine vision and image processing that is intended to make it easier to develop efficient, portable applications. The goal in defining OLIVE is to provide a well-factored, intuitive, portable tool for image processing and analysis that insulates the user from details of the implementation while at the same time exploiting its capabilities (e.g. specialized hardware). The authors define OLIVE's principal object types-images and loci (abstractions of point sets and geometric entities)-and their corresponding operations, including the use of loci as generalized indexes for images. They present several examples of OLIVE for typical image processing and machine vision tasks. They discuss issues in the implementation of OLIVE, including a hardware architecture that simplifies the implementation while enhancing its performance.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, it is tried to transfer one of the powerful tools for image processing from the frequency domain to spatial domain. In which the frequency concepts is used in the field of image processing based on FPGA. The process is as follows that first a low-pass FIR filter in frequency domain is designed and then it is transferred from frequency domain to spatial domain and implemented as an IP Core standard structure in FPGA. The implemented IP CORE structure is streaming and the input and output images of type gray. Because the use of array and high-frequency processing in FPGA, it has wide applications in image processing field, especially for highspeed image processing. Finally, the implementation of 2D FIR filter and eyewitness in VGA's output and the comparison between the actual image and filtered image, it is obvious that high-frequency changes are decreased in the image.

ABSTRACTPerennial ryegrass (Lolium perenne L.) is widely used in grazing systems across the temperate world. Continued persistence of ryegrass pastures depends on our ability to further our understanding of the key traits or attributes responsible for improved plant performance. To this end, plant improvement is highly dependent on precise phenotyping capabilities. However, in‐field phenotyping is labor intensive, relies on experienced operators, and is often considered cost prohibitive. The aim of this study was to determine the accuracy of an image analysis tool developed to locate and estimate dry matter (DM) production of individual plants growing in the field. Fifty plants were used to develop the image analysis tool and another 1100 to determine its accuracy. Dry matter was measured by weighing oven‐dried harvested plant material and estimated both with the image analysis tool and by visual scoring. The image analysis tool was faster and estimates of plant area were more highly correlated with measured DM values, potentially capturing 25% more of the variation, than those from visual growth scores. The tool was able to effectively locate and distinguish between neighboring plants. The accuracy of the tool was greatest for production values below 15 g, and the tool would benefit from further development to improve accuracy above this threshold.