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Stitching of microscopic images for quantifying neuronal growth and spine plasticity
- Stitching of microscopic images for quantifying neuronal growth and spine plasticity
- Song S.; Son J.; Kim M.-H.
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol. 6453 LNCS, no. PART 1, pp. 45 - 53
- Document Type
- Conference Paper
- 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. © 2010 Springer-Verlag.
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- 엘텍공과대학 > 컴퓨터공학과 > Journal papers
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