Currently a lot of plant biology research focuses on understanding the genetic, physiological, and ecology of plants. Root is an important organ for plant to uptake nutrient and water from the surrounding soil. The capability of plant to obtain nutrient and water is closely related to root physiology. Quantitative measurement and analysis of plant root architecture parameters are very important for understanding and study growth of plant. A fundamental aim of developmental plant root biology is to understand how the three-dimensional morphology of plant roots arises through cellular mechanisms. However, traditional anatomical studies of plant development have mainly relied on two-dimensional images. Though this may be sufficient for some aspects of plant biology, deeper understanding of plant growth and function increasingly requires at least some amount of three dimensional measures and use chemical staining as a technique to bring pseudo structure and segmentation to the cross section image data. Thus parameters like uniformity of illumination and thickness of the specimen then becomes critical. Unfortunately these are also the causes of major variations. The variation of thickness of specimen can be interpreted as an effect which increases the latent dimensionality of the data. Addressing the variability due to specimen thickness can then be viewed in a manifold learning framework, wherein it is assumed that the data of interest lies on an embedded manifold within the higher-dimensional space and can be visualized in low dimensional space, using manifold learning constraints.

Date Published: 8 May 2012
PDF: 11 pages
Proc. SPIE 8406, Mobile Multimedia/Image Processing, Security, and Applications 2012, 84060S (8 May 2012); doi: 10.1117/12.921608

Published in SPIE Proceedings Vol. 8406:
Mobile Multimedia/Image Processing, Security, and Applications 2012
Sos S. Agaian; Sabah A. Jassim; Eliza Yingzi Du, Editor(s)