Increasing Trust in Image Analysis by Detecting Trellis Quantization in JPEG Images

Privacy-Friendly Photo Sharing and Relevant Applications Beyond

The design and implementation of Medical Image Archiving, Processing, Analysis and Communication System for Teleradiology which DICOM server was developed using software such as Borland Delphi 6.0, PHP and MySQL for image archive, image compression, image processing, image analysis and image transmission. The system provides the following facilities: (1) DICOM-format image archive. (2) Medical Image Compression. (3) DICOM viewer and Image processing, and (4) Image analysis. The results shown that (1) our system can also convert the image data both single frame and multiframes in normal or automatic DICOM process into the standard DICOM 3.0 format without altering the image data. (2) The resulting images are then compressed with two different techniques-JPEG and JPEG2000. The significant advantage of JPEG2000 over normal JPEG is that the error from JPEG2000 compression is smaller than the error from JPEG. Nevertheless, both methods share a similar mishap; when the compression ratio increases, they both generate more error than the processes on lower compression ratio. (3) our system can open single frame and multiframes such as DICOM, INTERFILE, BITMAP, JPEG and JPEG2000 each of which can exhibit information in. dcm file format with no distortion and image processing which has also the capabilities to zoom in/out and contrast/color map. And, (4) Image analysis sets ROI semi-automatically on region of expected disease based on geometric active contour model from edge of variance image and Canny edge detection. Thence, our system can calculate of Glomerular Filtration Rate (GFR), Effective Renal Plasma Flow (ERPF), and plot of the Time Activity Curve or Renogram The system has been developed and provided medical image services over long distance which showed the usefulness of our approach.

This study aims to compare the integrity and reproducibility of measurements created from uncompressed and compressed digital images in order to implement compliance with 21 CFR Part 11 for image analysis studies executed using 21 CFR Part 58 compliant capture systems. Images of a 400-mesh electron microscope grid and H&E stained rat liver tissue were captured on an upright microscope with digital camera using commercially available analysis software. Digital images were stored as either uncompressed TIFFs or in one of five different levels of JPEG compression. The grid images were analyzed with automatic detection of bright objects while the liver images were segmented using color cube-based morphometry techniques, respectively, using commercially-available image analysis software. When comparing the feature-extracted measurements from the TIFF uncompressed to the JPEG compressed images, the data suggest that JPEG compression does not alter the accuracy or reliability to reproduce individual data point measurements in all but the highest compression levels. There is, however, discordance if the initial measure was obtained with a TIFF format and subsequently saved as one of the JPEG levels, suggesting that the use of compression must precede feature extraction. It is a common practice in software packages to work with TIFF uncompressed images. However, this study suggests that the use of JPEG compression as part of the analysis work flow was an acceptable practice for these images and features. Investigators applying image file compression to other organ images will need to validate the utility of image compression in their work flow. A procedure to digitally acquire and JPEG compress images prior to image analysis has the potential to reduce file archiving demands without compromising reproducibility of data.

The local variance of image intensity is a typical measure of image smoothness. It has been extensively used, for example, to measure the visual saliency or to adjust the filtering strength in image processing and analysis. However, to the best of our knowledge, no analytical work has been reported about the effect of JPEG compression on image local variance. In this paper, a theoretical analysis on the variation of local variance caused by JPEG compression is presented. First, the expectation of intensity variance of 8×8 non-overlapping blocks in a JPEG image is derived. The expectation is determined by the Laplacian parameters of the discrete cosine transform coefficient distributions of the original image and the quantization step sizes used in the JPEG compression. Second, some interesting properties that describe the behavior of the local variance under different degrees of JPEG compression are discussed. Finally, both the simulation and the experiments are performed to verify our derivation and discussion. The theoretical analysis presented in this paper provides some new insights into the behavior of local variance under JPEG compression. Moreover, it has the potential to be used in some areas of image processing and analysis, such as image enhancement, image quality assessment, and image filtering.

Impact of Altering Various Image Parameters on Human Epidermal Growth Factor Receptor 2 Image Analysis Data Quality

The aim of the project was to make the software which on the basis on image of greenhouse tomato allows for the extraction of its characteristics. Data gathered during the image analysis and processing were used to build learning sets of artificial neural networks. Program enables to process pictures in jpeg format, acquisition of statistical information of the picture and export them to an external file. Produced software is intended to batch analyze collected research material and obtained information saved as a csv file. Program allows for analysis of 33 independent parameters implicitly to describe tested image. The application is dedicated to processing and image analysis of greenhouse tomatoes. The program can be used for analysis of other fruits and vegetables of a spherical shape.

Holistic representations of natural scenes are an effective and powerful source of information for semantic classification and analysis of images. Despite the technological hardware and software advances, consumer single-sensor imaging devices technology are quite far from the ability of recognising scenes and/or to exploit the visual content during (or after) acquisition time. The frequency domain has been successfully exploited to holistically encode the content of natural scenes in order to obtain a robust representation for scene classification. The authors exploit a holistic representation of the scene in the discrete cosine transform domain fully compatible with the JPEG format. The advised representation is coupled with a logistic classifier to perform classification of the scene at superordinate level of description (e.g. natural against artificial), or to discriminate between multiple classes of scenes usually acquired by a consumer imaging device (e.g. portrait, landscape and document). The proposed method is able to work in constrained domain. Experiments confirm the effectiveness of the proposed method. The obtained results closely match state-of-the-art methods in terms of accuracy outperforming in terms of computational resources.

Orthogonal moment functions have long been used in image analysis. This paper proposes a novel approach based on discrete orthogonal Tchebichef Moment for efficient image compression. The method incorporates simplified mathematical framework techniques using matrices, as well as a block-wise reconstruction technique to eliminate possible occurrences of numerical instabilities at higher moment orders. The comparison between Tchebichef Moment compression and JPEG compression has been done. The results show significant advantages for Tchebichef Moment in terms of its image quality and compression rate. Tchebichef moment provides a more compact support to the image via sub-block reconstruction for compression. Tchebichef Moment Compression has clear potential to perform better for broader domain on real digital images and graphically generated images.

The extraction of rotation invariant representation is important for many signal processing tasks such as image analysis, computer vision, pattern recognition and so forth. In this paper, we show that, under certain conditions, the Two-Dimensional Fractional Fourier Transform (2D-FRFT) possesses this attractive property through mathematical analysis and extensive computer simulations. Based on this property, we propose a novel digital image watermarking method which combines 2D chirp signal with the addition and rotation invariant properties of 2D-FRFT in order to improve robustness and security of digital image watermark in 2D-FRFT domain. Experimental results show that the proposed method is robust against numerous watermarking attacks including rotation geometrical transform, JPEG compression, crop, median filtering, histogram equalization, salt & pepper noise, Gaussian lowpass filter, shift, dithering and Gaussian noise, and secures against unauthorized information copying and redistribution.

Corner detection is a critical component of many image analysis and image understanding tasks, such as object recognition and image matching. Our research indicates that existing corner detection algorithms cannot properly depict the difference between edges and corners and this results in wrong corner detections. In this paper, the capability of second-order generalized (isotropic and anisotropic) Gaussian directional derivative filters to suppress Gaussian noise is evaluated. The second-order generalized Gaussian directional derivative representations of step edge, L-type corner, Y- or T-type corner, X-type corner, and star-type corner are investigated and obtained. A number of properties for edges and corners are discovered which enable us to propose a new image corner detection method. Finally, the criteria on detection accuracy and average repeatability under affine image transformation, JPEG compression, and noise degradation, and the criteria on region repeatability are used to evaluate the proposed detector against nine state-of-the-art methods. The experimental results show that our proposed detector outperforms all the other tested detectors.

Digital image processing is used in many real-world applications and in the field of industrial automation. Diversifying the difficult environment has a huge effect on images by directly affecting the decision derived from image analysis and limits the acceleration of industrial automation. Environmental parameters such as light, time (day or night), and weather conditions play a vital role in the quality of photos captured in an outdoor environment. The usage of images captured from outdoor environments makes it difficult to recognize entities. For this, there exists a work to detect the amount of smoke in foggy images using a deep neural network. However, to our knowledge, no techniques are currently available that objectively assess the impact of environmental parameters such as rain, shadow, darkness, and fog, all of which can have a dramatic impact on image quality. In the data mining process, there are many approaches for numerical data; however, the lack of quantitative techniques for the preparation and understanding of image data can make image analysis a very tedious activity.To address this research gap, we present an innovative technique for evaluating the impact of environmental parameters on the image quality, compared with the quality affected by the JPEG image compression technique and the different levels of Gaussian noise. In addition to the impact of fog, we also analyse the effect of rain, shadow, and darkness on the images by utilizing image quality assessment metrics like Mean Squared Error (MSE), Peak signal-to-noise ratio (PSNR), Structural Content (SC), Oriented FAST and rotated BRIEF (ORB) and Structural Similarity Index (SSIM). We use foggy and traffic sign recognition datasets which have different levels of environmental impact. The quality values produced by SSIM are consistent with the different levels of impacts, JPEG image quality, and Gaussian noise. As might be anticipated, rain and darkness have a substantial impact on the image quality. The values produced by SSIM and their corresponding values for Gaussian noise can be used to quantitatively assess the image quality, thereby improving both image understanding and image preparation.

An image analysis approach to identification and measurement of marbling in the intact pork loin

<p>Color texture analysis is a critical component in various computer vision and image processing applications, including object recognition, medical imaging, and remote sensing. Traditional methods like J-Segmentation for color texture segmentation often struggle with capturing complex textures and maintaining color fidelity, especially when dealing with JPEG-compressed images. Quaternion Algebra’s unique ability to represent and manipulate color information in a multidimensional space allows for more accurate feature extraction and segmentation. Our approach Quaternion neural network (QNN) not only improves the segmentation accuracy but also preserves the visual quality of the segmented regions in JPEG images. We demonstrate the effectiveness of our method through extensive experimentation on diverse datasets, showcasing its superiority over existing techniques. The proposed approach not only achieves state-of-the-art results in terms of segmentation accuracy but also offers computational efficiency. This innovation holds great promise for applications in image analysis, computer vision, and medical imaging, where accurate color texture segmentation is paramount.</p>

Feces is one quick way to determine the health status of the birds and farmers rely on years of experience as well as professionals to identify and diagnose poultry diseases. Most often, farmers lose their flocks as a result of delayed diagnosis or a lack of trustworthy experts. Prevalent diseases affecting poultry birds may be quickly noticed from image of poultry bird's droppings using artificial intelligence based on computer vision and image analysis. This paper provides description of a dataset of both healthy and unhealthy poultry fecal imagery captured from selected poultry farms in south-west of Nigeria using smartphone camera. The dataset was collected at different times of the day to account for variability in light intensity and can be applied in machine learning models development for abnormality detection in poultry farms. The dataset collected is 19,155 images; however, after preprocessing which encompasses cleaning, segmentation and removal of duplicates, the data strength is 14,618 labeled images. Each image is 100 by 100 pixels size in jpeg format. Additionally, computer vision applications like picture segmentation, object detection, and classification can be supported by the dataset. This dataset's creation is intended to aid in the creation of comprehensive tools that will aid farmers and agricultural extension agents in managing poultry farms in an effort to minimize loss and, as a result, optimize profit as well as the sustainability of protein sources.

The Newcomb–Benford law states that in a set of natural numbers, the leading digit has a probability distribution that decays logarithmically. One of its major applications is the JPEG compression of images, a field of great interest for domains such as image forensics. In this article, we study JPEG compression from the point of view of Benford’s law. The article focuses on ways to detect fraudulent images and JPEG quality factors. Moreover, using the image’s luminance channel and JPEG coefficients, we describe a technique for determining the quality factor with which a JPEG image is compressed. The algorithm’s results are described in considerably more depth in the article’s final sections. Furthermore, the proposed idea is applicable to any procedure that involves the analysis of digital images and in which it is strongly suggested that the image authenticity be verified prior to beginning the analyzing process.