Segmentation of Carbon Nanotube Images Through an Artificial Neural Network

The segmentation of nanotube is an important task for Nanotechnology. The performance of segmentation stage determines the accuracy of the measurement process of nanotube when assessing the quality of nanomaterials. In this work we propose two algorithms for segmenting carbon nanotube images. The first one uses a matched filter bank in the preprocessing step and a neural network for segmenting images from Scanning Electron Microscopy. The second algorithm includes the Perona-Malik filter for enhancing the nanotube information. The segmentation phase is composed by the relaxed Otsu's threshold and an artificial neural network. This algorithm is applied on images from Transmission Electron Microscopy. After the segmentation, for both algorithms, a preprocessing based on mathematical morphology is carried out. The performance of the proposed algorithms is numerically evaluated by using real image databases. Overall accuracy of 92.74% and 73.99% were obtained for the first and second algorithm respectively.