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Evaluation and benchmark for biological image segmentation

Lookup NU author(s): Professor Boguslaw ObaraORCiD


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This paper describes ongoing work on creating a benchmarking and validation dataset for biological image segmentation. While the primary target is biological images, we believe that the dataset would be of help to researchers working in image segmentation and tracking in general. The motivation for creating this resource comes from the observation that while there are a large number of effective segmentation methods available in the research literature, it is difficult for the application scientists to make an informed choice as to what methods would work for her particular problem. No one single tool exists that is effective on a diverse set of application contexts and different methods have their own strengths and limitations. We describe below three different classes of data, ranging in scale from subcellular to cellular to tissue level images, each of which pose their own set of challenges to image analysis. Of particular value to the image processing researchers is that the data comes with associated ground truth information that can be used to evaluate the effectiveness of different methods. The analysis and evaluation are also integrated into a database framework that is available online at http://dough. ece. ucsb. edu. © 2008 IEEE.

Publication metadata

Author(s): Gelasca ED, Byun J, Obara B, Manjunath BS

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 15th IEEE International Conference on Image Processing (ICIP 2008)

Year of Conference: 2008

Pages: 1816-1819

Online publication date: 12/12/2008

ISSN: 1522-4880

Publisher: IEEE


DOI: 10.1109/ICIP.2008.4712130

Library holdings: Search Newcastle University Library for this item

ISBN: 9781424417650