Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.

https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-19/issue-1/010901/Medical-hyperspectral-imaging-a-review/10.1117/1.JBO.19.1.010901.pdf

Medical hyperspectral imaging: a review

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The Essential Physics Of Medical Imaging

PURPOSETo update key recommendations of the American Society of Clinical Oncology/College of American Pathologists estrogen (ER) and progesterone receptor (PgR) testing in breast cancer guideline.M...

https://ascopubs.org/doi/pdfdirect/10.1200/JCO.19.02309

Estrogen and Progesterone Receptor Testing in Breast Cancer: ASCO/CAP Guideline Update.

Deep learning approach for active classification of electrocardiogram signals

U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Patch-based methods have attracted significant attention in recent years within the field of image processing for a variety of problems including denoising, inpainting, and super-resolution interpolation. Despite their prevalence for processing 2-D signals, they have received little attention in the 1-D signal processing literature. In this letter, we explore application of one such method, the nonlocal means (NLM) approach, to the denoising of biomedical signals. Using ECG as an example, we demonstrate that a straightforward NLM-based denoising scheme provides signal-to-noise ratio improvements very similar to state of the art wavelet-based methods, while giving $\sim$ 3 $\times$ or greater reduction in metrics measuring distortion of the denoised waveform.

Nonlocal Means Denoising of ECG Signals

The present invention provides a hyperspectral imaging system which demonstrates changes in tissue oxygen delivery, extraction and saturation during shock and resuscitation including an imaging apparatus for performing real-time or near real­time assessment and monitoring of shock, including hemorrhagic, hypovolemic, cardiogenic, neurogenic, septic or burn shock. The information provided by the hyperspectral measurement can deliver physiologic measurements that support early detection of shock and also provide information about likely outcomes.

Hyperspectral/multispectral imaging in determination, assessment and monitoring of systemic physiology and shock

Background:Skin color changes and mottling are frequently described signs of hemorrhagic shock (HEM). Based on this, we developed a noninvasive, noncontact hyperspectral imaging system (HSI), which quantifies and depicts the surface tissue saturation of oxygen (SHSIO2) for each pixel in a region of

Hyperspectral Imaging: A New Approach to the Diagnosis of Hemorrhagic Shock

Non-Local Means (NLM) and its variants have proven to be effective and robust in many image denoising tasks. In this letter, we study approaches to selecting center pixel weights (CPW) in NLM. Our key contributions are 1) we give a novel formulation of the CPW problem from a statistical shrinkage perspective; 2) we construct the James-Stein shrinkage estimator in the CPW context; and 3) we propose a new local James-Stein type CPW (LJSCPW) that is locally tuned for each image pixel. Our experimental results showed that compared to existing CPW solutions, the LJSCPW is more robust and effective under various noise levels. In particular, the NLM with the LJSCPW attains higher means with smaller variances in terms of the peak signal and noise ratio (PSNR) and structural similarity (SSIM), implying it improves the NLM denoising performance and makes the denoising less sensitive to parameter changes.

James–Stein Type Center Pixel Weights for Non-Local Means Image Denoising

In this paper, we propose a so-called probabilistic non-local means (PNLM) method for image denoising. Our main contributions are: 1) we point out defects of the weight function used in the classic NLM; 2) we successfully derive all theoretical statistics of patch-wise differences for Gaussian noise; and 3) we employ this prior information and formulate the probabilistic weights truly reflecting the similarity between two noisy patches. The probabilistic nature of the new weight function also provides a theoretical basis to choose thresholds rejecting dissimilar patches for fast computations. Our simulation results indicate the PNLM outperforms the classic NLM and many NLM recent variants in terms of peak signal noise ratio (PSNR) and structural similarity (SSIM) index. Encouraging improvements are also found when we replace the NLM weights with the probabilistic weights in tested NLM variants.

Brian H. Tracey

Papers

Nonlocal Means Denoising of ECG Signals

Hyperspectral/multispectral imaging in determination, assessment and monitoring of systemic physiology and shock

Hyperspectral Imaging: A New Approach to the Diagnosis of Hemorrhagic Shock

James–Stein Type Center Pixel Weights for Non-Local Means Image Denoising

Probabilistic Non-Local Means