Finding Golgi Stacks in Electron Micrographs

Neil Fordyce, Stephen McKenna, Christian Hacker, John Lucocq

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Transmission electron microscopy (EM) can acquire images in which a range of subcellular organelles are clearly resolved simultaneously. There exist mature stereology techniques for extracting quantitative specimen information from section-based EM images and such techniques have been adopted successfully for use in immuno-EM. A bottleneck preventing the application of these nanomorphomics methods to high throughput applications is the recognition of organelle structures. This papers addresses this issue for one important organelle, the Golgi apparatus. A support vector machine is trained as a local Golgi detector based on rotationally invariant features. The SVM output is used to drive a graph-cuts segmentation. The ability of the method to detect and segment Golgi stacks is evaluated on a set of 36 micrographs.
Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis
Subtitle of host publicationMIUA
EditorsConstantino Reyes-Aldasoro, Greg Slabaugh
Place of PublicationUnited Kingdom
PublisherBritish Machine Vision Association and Society for Pattern Recognition
Pages59-64
Number of pages6
ISBN (Print)1901725510
Publication statusPublished - Jul 2014
Event18th Annual Conference in Medical Image Understanding and Analysis - Moore Complex, Royal Holloway, London, United Kingdom
Duration: 9 Jul 201411 Jul 2014
http://www.city.ac.uk/medical-image-understanding-and-analysis-2014

Conference

Conference18th Annual Conference in Medical Image Understanding and Analysis
Abbreviated titleMIUA 2014
CountryUnited Kingdom
CityLondon
Period9/07/1411/07/14
Internet address

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    Fordyce, N., McKenna, S., Hacker, C., & Lucocq, J. (2014). Finding Golgi Stacks in Electron Micrographs. In C. Reyes-Aldasoro, & G. Slabaugh (Eds.), Medical Image Understanding and Analysis: MIUA (pp. 59-64). British Machine Vision Association and Society for Pattern Recognition. http://www.city.ac.uk/medical-image-understanding-and-analysis-2014/proceedings