Automated methods for neuron segmentation and analysis of electron microscope images - Chen, Jingyun; More, Heather L.; Gibson, Eli; Donelan, J. Maxwell; Beg, Mirza Faisal (CMBES Proceedings, 2010)
To study changes in neuron size, number and distribution over a wide range of animal sizes, it is necessary for us to identify the axons and myelin of each neuron in electron microscope images of nerve cross-sections. Current methods commonly in use involve manually labeling each axon, which is extremely time-consuming as a single nerve contains thousands of axons. In order to make this process more efficient, we developed a computer-assisted neuron segmentation and analysis method. First we acquired a set of sub-images with identical size and resolution using a scanning electron microscope. We then developed an algorithm which used cross-correlation to stitch the sub-images into large images containing whole neuron clusters for segmentation. We developed a second algorithm to pre-process the stitched image, then segment and individually label axons using combined morphological operations. The myelin of each neuron was also segmented using a region growing algorithm with the geometric centers of axons as seeds. The final output of our algorithm is a histogram of axon and myelin sizes. We used this method to analyze nerves from different animal species including elephant, rat and shrew [1]. The typical processing time for a 4 6 million-pixel image on a PC (1.66GHz Pentium M Processor, 1G RAM) was approximately 5 minutes. The mislabel rate (percentage of false detections plus failed detections) is currently under 10% and improving. The method was proven to be well-suited for studying the effect of animal size on axon size and number.