Digital image processing
Automated algorithms for multiscale morphometry of neuronal dendrites
Neural Computation
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
Microscopic Image Analysis for Life Science Applications
Microscopic Image Analysis for Life Science Applications
| Hi-index | 0.00 |
In neurobiology, morphological change of neuronal structures such as dendrites and spines is important for understanding of brain functions or neuro-degenerative diseases. Especially, morphological changes of branching patterns of dendrites and volumetric spine structure is related to cognitive functions such as experienced-based learning, attention, and memory. To quantify their morphologies, we use confocal microscopy images which enables us to observe cellular structure with high resolution and three-dimensionally. However, the image resolution and field of view of microscopy is inversely proportional to the field of view (FOV) we cannot capture the whole structure of dendrite at on image. Therefore we combine partially obtained several images into a large image using image stitching techniques. To fine the overlapping region of adjacent images we use Fourier transform based phase correlation method. Then, we applied intensity blending algorithm to remove uneven intensity distribution and seam artifact at image boundaries which is coming from optical characteristics of microscopy. Finally, based on the integrated image we measure the morphology of dendrites from the center of cell to end of each branch. And geometrical characteristics of spine such as area, location, perimeter, and roundness, etc. are also quantified. Proposed method is fully automatic and provides accurate analysis of both local and global structural variations of neuron.