Automatic segmentation of cristae membranes in 3d electron microscopy tomography images using artificial neural networks

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2016

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Karadeniz, Merih Alphan

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Electron Microscopy Tomography (EMT) technique produces 3D images of cells comprising hundreds of slices of high resolution frames. Segmentation of membranes in these images are necessary in order to reveal the relations between the structural components of the cell and its behaviour. The physical shape of the crista which is a membrane of the mitochondria has been hypostatized for being an early indicator for many diseases or mitochondrial dysfunctions. Automatic segmentation of cristae in EMT images are necessary since it needs a huge human effort to manually segment these membranes. In this study, a method for automatic and robust segmentation of the crista membrane in mitochondria is proposed. The method incorporates a pre-processing stage in which a bilateral image smoothing is applied for noise removal while preserving the crista membrane boundaries. The cristae membranes are first detected by an artificial neural network (ANN) trained on cropped mitochondria images from three different data sets. When a portion of the membrane boundary is almost or totally invisible, ANN may produce disconnected segmentation. In order to overcome this issue and increase the final performance by means of detecting the barely invisible membrane boundaries and decreasing false alarms, a boundary growing method called ‘directional growing’ is proposed. The method is tested with examples from four different data sets and numerical and visual analysis of the results are conducted

Subject Keywords

Tomography., Electron microscopy., Imaging systems in medicine., Image processing, Neural networks (Computer science).

URI

http://etd.lib.metu.edu.tr/upload/12620402/index.pdf
https://hdl.handle.net/11511/26098

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Graduate School of Informatics, Thesis

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Citation Formats

M. A. Karadeniz, “Automatic segmentation of cristae membranes in 3d electron microscopy tomography images using artificial neural networks,” M.S. - Master of Science, Middle East Technical University, 2016.