Medical image synthesis is important in diverse healthcare applications, such as computer-aided diagnosis, medical image analysis, and educational tools. While Generative Adversarial Networks (GANs) have shown remarkable success in generating natural images, their application to medical images often falls short in faithfully capturing essential anatomical features. In this paper, we introduce a new approach that focuses on tissue-specific color encoding to enhance medical image synthesis using GANs. Our method deviates from the conventional practice of directly training GANs on gray-scale medical images. Instead, we initiate the process by generating and encoding various gray-scale representations of distinct tissues into separate color channels within composite images. These tissue-specific color images are then utilized to train a GAN model. The GAN, once trained, excels in producing high-quality synthetic images for individual tissues, and when combined, these tissue images yield final synthesized images that better portray the intricate tissue characteristics found in medical data. We have conducted an experimental study to validate the effectiveness of our approach in comparison to alternative methods with both qualitative and quantitative assessments to evaluate the quality of synthesized individual tissues and their combined final results.

Soil measurement and evaluation are crucial to various aspects of agriculture, including agricultural productivity, nutrient management, water management, and pH Regulation. Hyperspectral imaging is an advanced technique used to capture and analyze a wide range of light wavelengths (or spectral bands) across the electromagnetic spectrum. Hyperspectral imaging in soil research involves the use of this advanced imaging technique to analyze the spectral properties of soils. It allows researchers to capture detailed information about the composition, texture, and conditions of soil across a wide range of wavelengths in the electromagnetic spectrum. This in-depth spectral analysis provides valuable insights for studying soil health, nutrient content, moisture levels, and other critical parameters. However, existing hyperspectral analysis of soil relies on using imaging systems to exclusively capture information from the soil surface. This yields a two-dimensional image in which each pixel represents a spectrum vector. In this paper, we provide a new 3D hyperspectral data capturing features deep into the soil where each voxel represents a spectrum vector. For effective analysis of this type of new hyperspectral data, we develop a 3D visualization tool to not only directly visualize individual spectrum of the soil volume but also provide a way to cluster such high dimensional data leveraging a deep learning-based method through autoencoder.

We present a novel web-based framework, named Pixel-Based Edge Bundling (PBEB), for effectively and interactively visualizing large graphs. Our framework combines an image-based edge-bundling method and a parallel texture-based processing scheme, allowing us to effectively and efficiently compute edge similarities using kernel density estimation and subsequently group these edges into bundles based on their similarities. We discuss several challenges related to developing large-graph visualization on web-based platforms. To accelerate the edge bundling process and enable interactivity in web-based environments, we leverage texture-based parallel processing, a standard feature of WebGL. Our framework optimizes an end-to-end process, from bundling to rendering, enabling practical and interactive visualization of large graphs in a web-based setting. We demonstrate the superior performance of our framework by conducting comparisons with existing web-based and CUDA-based edge-bundling methods using various standard graphics cards on different devices.