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- W2034647784 abstract "In material science and bio-medical domains the quantity and quality of microscopy images is rapidly increasing and thereis a great need to automatically detect, delineate and quantify particles, grains, cells, neurons and other functional objectswithin these images. These are challenging problems for image processing because of the variability in object appearancethat inevitably arises in real world image acquisition and analysis. One of the most promising (and practical) ways toaddress these challenges is interactive image segmentation. These algorithms are designed to incorporate input from ahuman operator to tailor the segmentation method to the image at hand. Interactive image segmentation is now a key toolin a wide range of applications in microscopy and elsewhere. Historically, interactive image segmentation algorithms havetailored segmentation on an image-by-image basis, and information derived from operator input is not transferred betweenimages. But recently there has been increasing interest to use machine learning in segmentation to provide interactive toolsthat accumulate and learn from the operator input over longer periods of time. These new learning algorithms reduce theneed for operator input over time, and can potentially provide a more dynamic balance between customization andautomation for different applications. This paper reviews the state of the art in this area, provides a unified view of thesealgorithms, and compares the segmentation performance of various design choices." @default.
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- W2034647784 date "2015-02-27" @default.
- W2034647784 modified "2023-09-25" @default.
- W2034647784 title "Segmentation and learning in the quantitative analysis of microscopy images" @default.
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- W2034647784 doi "https://doi.org/10.1117/12.2083776" @default.
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