Texture Segementation Algorithms for Electron Microscopy Images



Image segmentation is a key bottleneck in analysis of electron microscope (EM) images, made challenging by the fact that many features in EM images differ not in intensity but in texture.

I have explored several options for texture segmentation of images, based on feature vectors generated from local histograms of filtered versions of the image, which are then classified using a Support Vector Machine (SVM).

I find that while these algorithms perform better than chance, they do not outperform a naive thresholding algorithm. More work must be done to optimize feature vectors for better separation and to improve the SVM classification for better segmentation.


Local Spectral Histogram Segmentation Algorithm

Local Binary Pattern Segmentation Algorithm


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