Matches in SemOpenAlex for { <https://semopenalex.org/work/W2469483744> ?p ?o ?g. }
- W2469483744 endingPage "1377" @default.
- W2469483744 startingPage "1366" @default.
- W2469483744 abstract "Segmentation and analysis of histological images provides a valuable tool to gain insight into the biology and function of microglial cells in health and disease. Common image segmentation methods are not suitable for inhomogeneous histology image analysis and accurate classification of microglial activation states has remained a challenge. In this paper, we introduce an automated image analysis framework capable of efficiently segmenting microglial cells from histology images and analyzing their morphology. The framework makes use of variational methods and the fast-split Bregman algorithm for image denoising and segmentation, and of multifractal analysis for feature extraction to classify microglia by their activation states. Experiments show that the proposed framework is accurate and scalable to large datasets and provides a useful tool for the study of microglial biology." @default.
- W2469483744 created "2016-07-22" @default.
- W2469483744 creator A5004641991 @default.
- W2469483744 creator A5010263565 @default.
- W2469483744 creator A5014775730 @default.
- W2469483744 creator A5015805264 @default.
- W2469483744 creator A5029951436 @default.
- W2469483744 creator A5043671940 @default.
- W2469483744 creator A5078023911 @default.
- W2469483744 creator A5078754523 @default.
- W2469483744 creator A5083787153 @default.
- W2469483744 date "2017-11-01" @default.
- W2469483744 modified "2023-09-22" @default.
- W2469483744 title "Novel Methods for Microglia Segmentation, Feature Extraction, and Classification" @default.
- W2469483744 cites W1963696719 @default.
- W2469483744 cites W1967450909 @default.
- W2469483744 cites W1970433280 @default.
- W2469483744 cites W1988785687 @default.
- W2469483744 cites W1992062767 @default.
- W2469483744 cites W2009917599 @default.
- W2469483744 cites W2010945445 @default.
- W2469483744 cites W2020822045 @default.
- W2469483744 cites W2039242833 @default.
- W2469483744 cites W2045573972 @default.
- W2469483744 cites W2050422776 @default.
- W2469483744 cites W2055522016 @default.
- W2469483744 cites W2080499279 @default.
- W2469483744 cites W2087257420 @default.
- W2469483744 cites W2088167200 @default.
- W2469483744 cites W2099540110 @default.
- W2469483744 cites W2103559027 @default.
- W2469483744 cites W2107376426 @default.
- W2469483744 cites W2111271085 @default.
- W2469483744 cites W2114487471 @default.
- W2469483744 cites W2116784956 @default.
- W2469483744 cites W2119895789 @default.
- W2469483744 cites W2123763663 @default.
- W2469483744 cites W2126575561 @default.
- W2469483744 cites W2126658928 @default.
- W2469483744 cites W2129916129 @default.
- W2469483744 cites W2137319779 @default.
- W2469483744 cites W2142058898 @default.
- W2469483744 cites W2142160474 @default.
- W2469483744 cites W2155085197 @default.
- W2469483744 cites W2155280009 @default.
- W2469483744 cites W2157400052 @default.
- W2469483744 cites W2157757554 @default.
- W2469483744 cites W2163111080 @default.
- W2469483744 cites W2173423263 @default.
- W2469483744 cites W2176540740 @default.
- W2469483744 cites W2274288231 @default.
- W2469483744 cites W2293331563 @default.
- W2469483744 cites W2294876837 @default.
- W2469483744 cites W2296479498 @default.
- W2469483744 cites W2343789953 @default.
- W2469483744 cites W4234242299 @default.
- W2469483744 cites W658985630 @default.
- W2469483744 doi "https://doi.org/10.1109/tcbb.2016.2591520" @default.
- W2469483744 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27429441" @default.
- W2469483744 hasPublicationYear "2017" @default.
- W2469483744 type Work @default.
- W2469483744 sameAs 2469483744 @default.
- W2469483744 citedByCount "23" @default.
- W2469483744 countsByYear W24694837442017 @default.
- W2469483744 countsByYear W24694837442018 @default.
- W2469483744 countsByYear W24694837442019 @default.
- W2469483744 countsByYear W24694837442020 @default.
- W2469483744 countsByYear W24694837442021 @default.
- W2469483744 countsByYear W24694837442022 @default.
- W2469483744 countsByYear W24694837442023 @default.
- W2469483744 crossrefType "journal-article" @default.
- W2469483744 hasAuthorship W2469483744A5004641991 @default.
- W2469483744 hasAuthorship W2469483744A5010263565 @default.
- W2469483744 hasAuthorship W2469483744A5014775730 @default.
- W2469483744 hasAuthorship W2469483744A5015805264 @default.
- W2469483744 hasAuthorship W2469483744A5029951436 @default.
- W2469483744 hasAuthorship W2469483744A5043671940 @default.
- W2469483744 hasAuthorship W2469483744A5078023911 @default.
- W2469483744 hasAuthorship W2469483744A5078754523 @default.
- W2469483744 hasAuthorship W2469483744A5083787153 @default.
- W2469483744 hasBestOaLocation W24694837442 @default.
- W2469483744 hasConcept C124504099 @default.
- W2469483744 hasConcept C125308379 @default.
- W2469483744 hasConcept C138885662 @default.
- W2469483744 hasConcept C144133560 @default.
- W2469483744 hasConcept C153180895 @default.
- W2469483744 hasConcept C154945302 @default.
- W2469483744 hasConcept C162853370 @default.
- W2469483744 hasConcept C25694479 @default.
- W2469483744 hasConcept C2776401178 @default.
- W2469483744 hasConcept C31972630 @default.
- W2469483744 hasConcept C41008148 @default.
- W2469483744 hasConcept C41895202 @default.
- W2469483744 hasConcept C52622490 @default.
- W2469483744 hasConcept C65885262 @default.
- W2469483744 hasConcept C89600930 @default.
- W2469483744 hasConceptScore W2469483744C124504099 @default.
- W2469483744 hasConceptScore W2469483744C125308379 @default.