ArticleInternational journal of biomedical imaging2011
A general system for automatic biomedical image segmentation using intensity neighborhoods.
Article in International journal of biomedical imaging, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- SD-UNet: Stripping Down U-Net for Segmentation of Biomedical Images on Platforms with Low Computational Budgets.Diagnostics (Basel, Switzerland) · 2020Article
- A supervised learning framework for pancreatic islet segmentation with multi-scale color-texture features and rolling guidance filters.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2016Article
- A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis.PloS one · 2014Article
- An expert support system for breast cancer diagnosis using color wavelet features.Journal of medical systems · 2012Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Image segmentation is important with applications to several problems in biology and medicine. While extensively researched, generally, current segmentation methods perform adequately in the applications for which they were designed, but often require extensive modifications or calibrations before being used in a different application. We describe an approach that, with few modifications, can be used in a variety of image segmentation problems. The approach is based on a supervised learning strategy that utilizes intensity neighborhoods to assign each pixel in a test image its correct class based on training data. We describe methods for modeling rotations and variations in scales as well as a subset selection for training the classifiers. We show that the performance of our approach in tissue segmentation tasks in magnetic resonance and histopathology microscopy images, as well as nuclei segmentation from fluorescence microscopy images, is similar to or better than several algorithms specifically designed for each of these applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.