Medical Image Enhancement Based on HVC

Medical X-ray image is used as the important information and basis for the clinical diagnosis, while its quality digressed because of the interferences caused by the human body structure, equipments, and environmental factors. This paper verifies that the X-ray image degradation caused by the X-ray scattering which is similar as the haze scattering. Dark channel prior is widely used on haze removing, we applied the dark channel prior method in medical X-ray image enhancement. The experimental results show that the method can effectively increase the image contrast, highlight the details.

In the process of medical image formation, the medical image is often interfered by various factors, and it is deteriorated by some new noise that may reduce the quality of the obtained image, which affect the clinical diagnosis seriously. A new medical image enhancement method is proposed in this article. Firstly, the initial medical image is decomposed into the NSCT domain with a low-frequency sub-band, and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. An adaptive thresholding method is used for denoising the coefficients of the high-frequency sub-bands. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The results of experiment show that the proposed method is superior to other methods in image entropy, EME, and PSNR. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 199–205, 2015

In this paper we use the HSI color space as an alternative to RGB space. The HSI space considers the image as a combination of the components: hue, saturation and intensity. In this paper we propose to design a chromatic filter in order to obtain improvements in the enhancement of medical images. In image processing systems it is usual specify colors in a form compatible with the hardware used. The RGB color model, where is computationally convenient, is not very useful in the specification and color recognition. The human being does not recognize a color by having an amount of red, green or blue components, but uses attributes perceptual of hue, saturation and intensity. The chromatics models HSI, HLS, HSV, and its variants, encodes the color with the above attributes and are defined as intuitive spaces.

One of the most important preprocessing techniques is image Contrast Enhancement which is a technique that improve visual quality of an input image becoming more suitable for human analysis and perception. Numerous researches have been already developed for enhancement of Medical image for their important application, Traditional Gamma Correction is found to be one of the simplest technique for the contrast enhancement of medical image. This technique uses a set of varying parameter (y, c) which adjust effectively gray value intensity of the input image. Otherwise, Adaptive Gamma Correction technique have been appeared and have been proved its effectiveness by the use of adaptive (y, c) parameter which are determined adaptively from statistical information of input image. Traditional Gamma Correction (TGC) and Adaptive Gamma Correction (AGC) have been applied on three brain MRI modalities for patient with Multiple Sclerosis pathology. Qualitative and quantitative results are presented to illustrate the comparison of TGC and AGC to enhance the contrast of MRI images for better appearance of normal tissue and diseased tissue affected by MS pathology.

Whether voter sees message as issue or image material depends on predisposition This study argues that, like meaning, political issue and image information does not rest solely in the content of a political advertisement, but rests also in the minds of the perceivers. Although the mass media literature addresses the effects of cognitive variables on the processing of political information,1 this study explores the existence of a cognitive bias which affects an individual's processing of political advertisements. Research suggests that the selective recall of either image or issue political information may result from the motivation to attend to image or issue information,2 from the existence of a schema which facilitates the processing of image or issue information,3 or the elaboration of one's political belief system.4 But do these motivations, schemata and beliefs result in a predisposition to process predominantly image or issue information, regardless of the image and issue content of the information? This question is particularly relevant to political advertising research for two reasons: 1) political advertisers have long been criticized for focusing on candidates' images to the neglect of issue information,5 and 2) content analytic studies6 and studies of individual processing differences7 have not yet merged. Moreover, if processing biases exist, what are the demographic and political characteristics associated with image or issue information orientations? For example, Swanson and Freeman report that discriminating among political candidates on the basis of political criteria (e.g. issues, ideologies, and political background was not related to reported interest in politics, or the frequency with which people discuss politics with friends.8 However, it was related to political activities such as donating money to campaigns and working for campaigns. If we assume that political motivations, schemata and beliefs mediate the processing of political advertisements, two important research questions are raised. RQ 1: Do image and issue political information processing orientations exist? RQ 2: If image and issue processing biases exist, what political and demographic variables are related to this processing bias. Method The sample was composed of 113 members of men's and women's organizations (59% males, and 41% females). The groups were exposed to two televised political advertisements for opposing United States Senate candidates in another state. The ads were selected because of their relatively equal and similar image and issue information representation, the discussion of issues pertinent to the area, the unfamiliarity of the candidates to the subjects and the lack of partisan labels in the ads. Senatorial ads were employed because past research has shown that senatorial level campaigns initiate moderate to high political involvement among the general public.9 To further stimulate involvement in this artificial situation, subjects were led to believe that the candidates were seeking party nomination in their state for the next senatorial campaign. The ads were not systematically content analyzed for image and issue content. However, 25 pre-test subjects evaluated the ads for comparable ease in evaluating both candidates on all image and issue criteria. The specific differences between the ads is not central to the proposed hypothesis, however. The hypothesis argues that voters will respond consistently with an image or issue orientation regardless of the content of the ad. As a representation of initial information processing, cognitive responses seem a valid measure for identifying image and issue processing biases.10 The general paradigm for assessing cognitive responses in research involves oral or written listing of the subject's thoughts immediately following exposure to the message. Each of these responses were content analyzed as being image, issue or other. …

HVS theory plays important role in the application of digital image watermarking technique. When inserting watermarking, the visual masking feature of HVS could be fully used to design digital watermarking algorithm with good perceived performance. When extracting watermarking from the damaged image, human's visual feature could be combined to recover the damaged image so as to obtain better effect. This article combines with human visual feature to study digital watermarking technique, utilizes watermarking redundant technique to insert watermarking, extracts watermarking in line with the damage situation of watermarking, and combines with visual redundancy feature to achieve a image scrambling algorithm that is easy to recover and a recovery scheme for damaged scrambling image.

Image fusion is the effective combination of multiple images into a single fused image. The goal of the fusion is use the similar and complementary information of the source images in order to obtain an informative depiction of the scene for further processing. Many multi-scale image fusion algorithms have been formulated on the basis that the human visual system is sensitive to edge information. However, these algorithms make use of standard mathematical operators, which do not reflect human visual system characteristics over a large range of background luminance intensities. Accordingly, this paper proposes new image fusion algorithms using a new Parameterized Logarithmic Stationary Wavelet Transform (PL-SWT), which combines the advantages of the Stationary Wavelet Transform (SWT) and the Parameterized Logarithmic Image Processing (PLIP) model, a parameterized framework for processing images. An analysis of the PLIP model shows that it is capable of providing a balance between logarithmic and standard mathematical operators based on image dependent characteristics. Consequently, the use of the parameterized model is extended for to both pixel- and region-based fusion approaches. Experimental results via computer simulation illustrate the improved performance of the proposed image fusion algorithms by both qualitative and quantitative means.

We have developed several three-dimensional display systems that are matched to the human visual field characteristics. In this article, we describe our developed display systems, which are matched for the human communication in the closerange, medium-range, and distant rage categories.

Clinical medical images often suffer from compromised quality, which negatively impacts the diagnostic process by both clinicians and AI algorithms. While GAN-based enhancement methods have been commonly developed in recent years, delicate model training is necessary due to issues with artifacts, mode collapse, and instability. Diffusion models have shown promise in generating high-quality images superior to GANs, but challenges in training data collection and domain gaps hinder applying them for medical image enhancement. Additionally, preserving fine structures in enhancing medical images with diffusion models is still an area that requires further exploration. To overcome these challenges, we propose structure-preserved diffusion models for generalizable medical image enhancement (GEDM). GEDM leverages joint supervision from enhancement and segmentation to boost structure preservation and generalizability. Specifically, synthetic data is used to collect high-low quality paired training data with structure masks, and the Laplace transform is employed to reduce domain gaps and introduce multi-scale conditions. GEDM conducts medical image enhancement and segmentation jointly, supervised by high-quality references and structure masks from the training data. Four datasets of two medical imaging modalities were collected to implement the experiments, where GEDM outperformed state-of-the-art methods in image enhancement, as well as follow-up medical analysis tasks.

Surveillance video cameras capture large amounts of continuous video streams every day. To analyze or investigate any significant events, it is a laborious and boring job to identify these events from the huge video data if it is done manually. Existing approaches sometimes neglect key frames with significant visual contents and/or select some unimportant frames with low/no activity. To solve this problem, in this paper, a video summarization technique is proposed by combining three multimodal human visual sensitive features, such as foreground objects, motion information, and visual saliency. In a video stream, foreground objects are one of the most important pieces of a video as they contain more detailed information and play a major role in important events. Moreover, motion is another stimulus of a video that significantly attracts human visual attention. To obtain this, motion information is calculated in the spatial domain as well as the frequency domain. Spatial motion information can select object motion accurately; however, it is sensitive to illumination changes. On the other hand, frequency motion information is robust to illumination change, although it is easily affected by noise. Therefore, motion information in both the spatial and the frequency domains is employed. Furthermore, the visual attention cue is a sensitive feature to measure the indication of a user's attraction label for determining key frames. As these features individually cannot perform very well, they are combined to obtain better results. For this purpose, an adaptive linear weighted fusion scheme is proposed to combine the features to rank video frames for summarization. Experimental results reveal that the proposed method outperforms the state-of-the-art methods.

Product packaging design is a work that integrates graphic design and three-dimensional design. 3D visualization technology and digital virtual interaction technology in virtual reality are developing rapidly in packaging design applications. On the one hand, this study discusses the application of digital technology in the three-dimensional design of packaging structure, including packaging carton structure design, packaging container modeling design, and packaging two-dimensional visual design. On the other hand, this study studies the color correction algorithm of packaging design and proposes a color correction based on the visual characteristics of human eyes. The pattern search algorithm finds a local optimum, and the initial value affects the correction result. Therefore, inspired by the color perception properties of the human visual system, the image is corrected by combining visual multichannel and visual nonlinearity. The experimental results of the algorithm are more consistent with human visual perception.

An adaptive Fuzzy Inference Perceptual model has been proposed for watermarking of video streams. The model is designed to be adaptive to the human visual characteristics to attest the owner identification and discourage the unauthorized copying. The proposed model integrates the human visual characteristics of video motion sub-regions, in the frequency multi-resolution wavelet domain, with multi-dimensional fuzzy inference perceptual model. The designed fuzzy multi-variable input system inherits luminance, texture and gradient to generate an adaptive watermark embedding strength factor. Interacting motion estimation is applied for the selection of the candidate frames and sub-blocks. A fuzzy-based scheme has been proposed to generate a perceptual membership degree for strength watermark embedding factor for the selected candidate motion blocks. Experiments have been carried out with different benchmark test videos of different sizes, visual characteristics and sampling rates. Various sizes of watermark signatures have been applied on the model. Several experimental attacks have been applied such as frame dropping, frame averaging, JPEG compression, Gaussian noises. It manifests considerable robustness against various geometric and signal processing attacks. Several attacks have been applied to the proposed scheme and the experiments revealed promising results in terms of visual quality and extracted watermark distortion. In addition, the model has been compared with different other watermarking schemes in literature. The proposed model showed superior performance in terms of fidelity, robustness to attacks and high level of imperceptibility.

In video communication, the quality of video is mainly determined by bitrate in general. Moreover, the effect of video contents and their visual perception on video quality assessment (VQA) is often overlooked. However, in fact, for different videos, although the bitrates are the same, their VQA scores are still significantly different. Hence, it is assumed that the bitrate, video contents, and human visual characteristics mainly affect the VQA. Based on the above three aspects, in this paper, we designed a bitrate-based no-reference (NR) VQA metric combining the visual perception of video contents, namely, BRVPVC. In this metric, first an initial VQA model was proposed by only considering the bitrate alone. Then, the visual perception model for video contents was designed based on the texture complexity and local contrast of image, temporal information of video, and their visual perception features. Finally, two models were synthesized by adding certain weight coefficients into an overall VQA metric, namely, BRVPVC. Furthermore, ten reference videos and 150 distorted videos in the LIVE video database were used to test the metric. Moreover, based on the results of evaluating the videos in LIVE, VQEG, IRCCyN, EPFL-PoliMI, IVP, CSIQ, and Lisbon databases, the performance of BRVPVC is respectively compared with that of six full-reference (FR) metrics and ten NR VQA metrics. The results show that our VQA metric has a higher accuracy than six common FR VQA metrics and eight NR VQA metrics, and it is close to other two NR VQA metrics in accuracy. The corresponding values of Pearson linear correlation coefficient and Spearman rank order correlation coefficient reached 0.8547 and 0.8260, respectively. In addition, the computational complexity of proposed VQA metric is lower than video signal-to-noise ratio, video quality model, motion-based video integrity evaluation, spatiotemporal most apparent distortion, V-BLINDS, and V-CORNIA metrics. Moreover, the proposed metric has a better generalization property than these metrics.

The logarithmic image processing (LIP) theory is a mathematical framework that provides a set of specific algebraic and functional operations and structures that are well adapted to the representation and processing of nonlinear images, and more generally of non-linear signals, valued in a bounded intensity range. This very well structured theory determined us to use the logarithmic image representation in our approach for defining a new set of mathematical morphology operators based upon structuring elements with a variable geometrical shape or adaptative structuring elements. The purpose of this paper is to define and to analyze the new multiplicative logarithmic morphological operators used in medical image enhancement. Finally, the experimental results reveal that this method has wide potential areas of impact which may include: Digital X-ray, Digital Mammography, Computer Tomography Scans, Nuclear Magnetic Resonance Imagery and Telemedicine Applications.

The color logarithmic image processing (CoLIP) is a mathematical framework for the representation and processing of color images. It is psychophysically well justified since it is consistent with several human visual perception laws and characteristics. It is mathematically and computationally relevant since it allows to consider color images as vectors in an abstract linear space, contrary to the classical color spaces (e.g., RGB and \(L^*a^*b^*\)). The first purpose of this chapter is to present the mathematical fundamentals of the CoLIP together with its main psychophysical connections (Grasmann’s law, color matching functions, chromaticity diagram, and the Maxwell triangle). The second purpose is to present some basic image processing and analysis techniques for contrast enhancement (histogram equalization, dynamic range maximization, and toggle contrast calculation), white balance correction, color transfer, K-means clustering, and filtering. Most of them are applied on various original color images in a comparative way between CoLIP, RGB, and \(L^*a^*b^*\) color spaces.