CHLOE: A Software Tool for Automatic Novelty Detection in Microscopy Image Datasets
The recent advancements in automated microscopy and information systems allow the acquisition and storage of massive datasets of microscopy images. Here we describe CHLOE, a software tool for automatic extraction of novelty in microscopy image datasets. The tool is based on a comprehensive set of nu...
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Format: | Article |
Language: | English |
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Ubiquity Press
2014-09-01
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Series: | Journal of Open Research Software |
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Online Access: | http://openresearchsoftware.metajnl.com/articles/40 |
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author | Saundra Manning Lior Shamir |
author_facet | Saundra Manning Lior Shamir |
author_sort | Saundra Manning |
collection | DOAJ |
description | The recent advancements in automated microscopy and information systems allow the acquisition and storage of massive datasets of microscopy images. Here we describe CHLOE, a software tool for automatic extraction of novelty in microscopy image datasets. The tool is based on a comprehensive set of numerical image content descriptors reflecting image morphology, and can be used in combination with ROI detection and segmentation tools such as ITK. The rich feature set allows automatic detection of repetitive outlier images that are visually different from the common images in the dataset. The code and software are publicly available for free download at <a href="http://vfacstaff.ltu.edu/lshamir/downloads/chloe">http://vfacstaff.ltu.edu/lshamir/downloads/chloe</a>. |
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format | Article |
id | doaj.art-607972e205fb4ae3a878a472a87fb54c |
institution | Directory Open Access Journal |
issn | 2049-9647 |
language | English |
last_indexed | 2024-12-12T05:07:36Z |
publishDate | 2014-09-01 |
publisher | Ubiquity Press |
record_format | Article |
series | Journal of Open Research Software |
spelling | doaj.art-607972e205fb4ae3a878a472a87fb54c2022-12-22T00:37:03ZengUbiquity PressJournal of Open Research Software2049-96472014-09-0121e25e2510.5334/jors.bg39CHLOE: A Software Tool for Automatic Novelty Detection in Microscopy Image DatasetsSaundra Manning0Lior Shamir1Lawrence Technological UniversityLawrence Technological UniversityThe recent advancements in automated microscopy and information systems allow the acquisition and storage of massive datasets of microscopy images. Here we describe CHLOE, a software tool for automatic extraction of novelty in microscopy image datasets. The tool is based on a comprehensive set of numerical image content descriptors reflecting image morphology, and can be used in combination with ROI detection and segmentation tools such as ITK. The rich feature set allows automatic detection of repetitive outlier images that are visually different from the common images in the dataset. The code and software are publicly available for free download at <a href="http://vfacstaff.ltu.edu/lshamir/downloads/chloe">http://vfacstaff.ltu.edu/lshamir/downloads/chloe</a>.http://openresearchsoftware.metajnl.com/articles/40Image analysisMicroscopyOutlier detection. |
spellingShingle | Saundra Manning Lior Shamir CHLOE: A Software Tool for Automatic Novelty Detection in Microscopy Image Datasets Journal of Open Research Software Image analysis Microscopy Outlier detection. |
title | CHLOE: A Software Tool for Automatic Novelty Detection in Microscopy Image Datasets |
title_full | CHLOE: A Software Tool for Automatic Novelty Detection in Microscopy Image Datasets |
title_fullStr | CHLOE: A Software Tool for Automatic Novelty Detection in Microscopy Image Datasets |
title_full_unstemmed | CHLOE: A Software Tool for Automatic Novelty Detection in Microscopy Image Datasets |
title_short | CHLOE: A Software Tool for Automatic Novelty Detection in Microscopy Image Datasets |
title_sort | chloe a software tool for automatic novelty detection in microscopy image datasets |
topic | Image analysis Microscopy Outlier detection. |
url | http://openresearchsoftware.metajnl.com/articles/40 |
work_keys_str_mv | AT saundramanning chloeasoftwaretoolforautomaticnoveltydetectioninmicroscopyimagedatasets AT liorshamir chloeasoftwaretoolforautomaticnoveltydetectioninmicroscopyimagedatasets |