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Hindi Research Article
A Hybrid Cnn-Rf Method for Electron Microscopy Images Segmentation
Guibao Cao1, Shuangling Wang1, Benzheng Wei2, Yilong Yin1* and Gongping Yang11 School of Computer Science and Technology, Shandong University, Jinan, China
2College of Science and Technology, Shandong University of Traditional Chinese Medicine, Jinan, China
- Corresponding Author:
- Yilong Yin
School of Computer Science and Technology
Shandong University, Jinan, China
E-mail: ylyin@sdu.edu.cn
Received date August 16, 2013; Accepted date October 05, 2013; Published date October 15, 2013
Citation: Cao G, Wang S, Wei B, Yin Y, Yang G (2013) A Hybrid Cnn-Rf Method for Electron Microscopy Images Segmentation. J Biomim Biomater Tissue Eng 18:114. doi:10.4172/1662-100X.1000114
Copyright: © 2013 Cao G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
To get new insights into the function and structure of the brain,neuroanatomists need to build 3D reconstructions of brain tissue from electron microscopy (EM) images. One key step towards this is to get automatic segmentation of neuronal structures depicted in stacks of electron microscopy images. However, due to the visual complex appearance of neuronal structures, it is challenging to automatically segment membranes in the EM images. Based on Convolutional Neural Network (CNN) and Random Forest classifier (RF), a hybrid CNN-RF method for EM neuron segmentation is presented. CNN as a feature extractor is trained firstly, and then well behaved features are learned with the trained feature extractor automatically. Finally, Random Forest classifier is trained on the learned features to perform neuron segmentation. Experiments have been conducted on the benchmarks for the ISBI2012 EM Segmentation Challenge, and the proposed method achieves the effectiveness results: The Rand error, Warping error and Pixel error attains to 0.109388991, 0.001455688 and 0.072129307, respectively.
