%0 Journal Article
%T Method: automatic segmentation of mitochondria utilizing patch classification, contour pair classification, and automatically seeded level sets
%A Richard J Giuly
%A Maryann E Martone
%A Mark H Ellisman
%J BMC Bioinformatics
%D 2012
%I BioMed Central
%R 10.1186/1471-2105-13-29
%X We report accuracy of the Cytoseg process on three types of tissue and compare it to a previous method based on Radon-Like Features. At step 1, we show that the patch classifier identifies mitochondria texture but creates many false positive pixels. At step 2, our contour processing step produces contours and then filters them with a second classification step, helping to improve overall accuracy. We show that our final level set operation, which is automatically seeded with output from previous steps, helps to smooth the results. Overall, our results show that use of contour pair classification and level set operations improve segmentation accuracy beyond patch classification alone. We show that the Cytoseg process performs well compared to another modern technique based on Radon-Like Features.We demonstrated that texture based methods for mitochondria segmentation can be enhanced with multiple steps that form an image processing pipeline. While we used a random-forest based patch classifier to recognize texture, it would be possible to replace this with other texture identifiers, and we plan to explore this in future work.The improved resolution and amount of detail afforded by emerging electron microscopy techniques, such as serial block-face scanning electron microscopy (SBFSEM) [1], is enabling researchers to explore scientific questions that were previously impossible. SBFSEM enables mapping of subcellular structures within large 3D regions, 1 mm กม 2 mm in the XY plane and greater than 0.5 mm in Z. However, the interpretation of data acquired with these techniques requires high-throughput segmentation that addresses the complexity and multi-scale nature of these data.The morphology and distribution of mitochondria has biological significance. For example, morphology of mitochondria has been studied as a means to detect abnormal cell states such as cancer [2]. Additionally, abnormal morphologies and distributions of mitochondria are associated with neural dysfu
%U http://www.biomedcentral.com/1471-2105/13/29