In this paper we proposed a multispectral enhancement scheme in which the spectral colors of the stained tissue-structure of interest and its background can be independently modified by the user to further improve their visualization and color discrimination. The colors of the background objects are modified by transforming their N-band spectra through an NxN transformation matrix, which is derived by mapping the representative samples of their original spectra to the spectra of their target colors using least mean square method. On the other hand, the color of the tissue structure of interest is modified by modulating the transformed spectra with the sum of the pixel's spectral residual-errors at specific bands weighted through an NxN weighting matrix; the spectral error is derived by taking the difference between the pixel's original spectrum and its reconstructed spectrum using the first M dominant principal component vectors in principal component analysis. Promising results were obtained on the visualization of the collagen fiber and the non-collagen tissue structures, e.g., nuclei, cytoplasm and red blood cells (RBC), in a hematoxylin and eosin (H&E) stained image.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4605764 | PMC |
http://dx.doi.org/10.3233/ACP-2012-0069 | DOI Listing |
Sci Rep
January 2025
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain.
In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories.
View Article and Find Full Text PDFACS Nano
December 2024
State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yu-Tian Road, Shanghai 200083, China.
The demand for broadband, room-temperature infrared, and terahertz (THz) detectors is rapidly increasing owing to crucial applications in telecommunications, security screening, nondestructive testing, and medical diagnostics. Current photodetectors face significant challenges, including high intrinsic dark currents and the necessity for cryogenic cooling, which limit their effectiveness in detecting low-energy photons. Here, we introduce a high-performance ultrabroadband photodetector operating at room temperature based on two-dimensional black arsenene (b-As) nanosheets.
View Article and Find Full Text PDFSci Rep
December 2024
School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao, 266520, China.
This paper presents a deep learning model based on an active learning strategy. The model achieves accurate identification of vegetation types in the study area by utilizing multispectral data obtained from preprocessing of unmanned aerial vehicle (UAV) remote sensing equipment. This approach offers advantages such as high data accuracy, mobility, and easy data collection.
View Article and Find Full Text PDFJ Imaging
December 2024
Radiology Department, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
This study investigates radiomic efficacy in post-surgical traumatic spinal cord injury (SCI), overcoming MRI limitations from metal artifacts to enhance diagnosis, severity assessment, and lesion characterization or prognosis and therapy guidance. Traumatic spinal cord injury (SCI) causes severe neurological deficits. While MRI allows qualitative injury evaluation, standard imaging alone has limitations for precise SCI diagnosis, severity stratification, and pathology characterization, which are needed to guide prognosis and therapy.
View Article and Find Full Text PDFSmall
December 2024
Cardiovascular Research Center, Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA.
Autophagy is a key biological process that has proven extremely difficult to detect noninvasively. To address this, an autophagy detecting nanoparticle (ADN) was recently developed, consisting of an iron oxide nanoparticle decorated with cathepsin-cleavable arginine-rich peptides bound to the near-infrared fluorochrome Cy5.5.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!