Automated Reconstruction of Neurovascular Networks in Knife-Edge Scanning Microscope Rat Brain Nissl Data Set Conference Paper uri icon


  • Springer International Publishing AG 2016. Analyzing mammalian brain image can help to understand the interaction between cerebral blood flow and its surrounding tissue. However, extracting the geometry of the vasculature and the cells is difficult because of the complexity of the brain. In this paper, we propose an approach for reconstructing the neurovascular networks from Knife-Edge Scanning Microscope (KESM) rat Nissl data set. The proposed method includes the following steps. First, we enhanced the raw image data using homomorphic filtering, fast Fourier transform, and anisotropic diffusion. Next, we initially extracted the vessel cross section from the image using dynamic global thresholding. Subsequently, we computed local properties of the connected components to remove various sources of noise. Finally, the proposed method connected small and large discontinuities in the vascular traces. To validate the performance of the proposed method, we compared reconstruction results of the proposed method with an existing method (Lims method [1,2]). The comparison results show that the proposed method outperforms the previous method: faster and robust to noise.

published proceedings

  • Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

author list (cited authors)

  • An, W., & Choe, Y.

citation count

  • 2

complete list of authors

  • An, Wookyung||Choe, Yoonsuck

publication date

  • January 2016