BACKGROUND AND AIMS: Analysis of anatomical sections of wood provides important information for understanding the secondary growth and development of plants. This study reports on a new method for the automatic detection and characterization of cell files in wood images obtained by light microscopy. To facilitate interpretation of the results, reliability coefficients have been determined, which characterize the files, their cells and their respective measurements. METHODS: Histological sections and blocks of the gymnosperms Pinus canariensis, P. nigra and Abies alba were used, together with histological sections of the angiosperm mahogany (Swietenia spp.). Samples were scanned microscopically and mosaic images were built up. After initial processing to reduce noise and enhance contrast, cells were identified using a 'watershed' algorithm and then cell files were built up by the successive aggregation of cells taken from progressively enlarged neighbouring regions. Cell characteristics such as thickness and size were calculated, and a method was developed to determine the reliability of the measurements relative to manual methods. KEY RESULTS: Image analysis using this method can be performed in less than 20 s, which compares with a time of approx. 40 min to produce the same results manually. The results are accompanied by a reliability indicator that can highlight specific configurations of cells and also potentially erroneous data. CONCLUSIONS: The method provides a fast, economical and reliable tool for the identification of cell files. The reliability indicator characterizing the files permits quick filtering of data for statistical analysis while also highlighting particular biological configurations present in the wood sections.
BACKGROUND AND AIMS: Analysis of anatomical sections of wood provides important information for understanding the secondary growth and development of plants. This study reports on a new method for the automatic detection and characterization of cell files in wood images obtained by light microscopy. To facilitate interpretation of the results, reliability coefficients have been determined, which characterize the files, their cells and their respective measurements. METHODS: Histological sections and blocks of the gymnosperms Pinus canariensis, P. nigra and Abies alba were used, together with histological sections of the angiosperm mahogany (Swietenia spp.). Samples were scanned microscopically and mosaic images were built up. After initial processing to reduce noise and enhance contrast, cells were identified using a 'watershed' algorithm and then cell files were built up by the successive aggregation of cells taken from progressively enlarged neighbouring regions. Cell characteristics such as thickness and size were calculated, and a method was developed to determine the reliability of the measurements relative to manual methods. KEY RESULTS: Image analysis using this method can be performed in less than 20 s, which compares with a time of approx. 40 min to produce the same results manually. The results are accompanied by a reliability indicator that can highlight specific configurations of cells and also potentially erroneous data. CONCLUSIONS: The method provides a fast, economical and reliable tool for the identification of cell files. The reliability indicator characterizing the files permits quick filtering of data for statistical analysis while also highlighting particular biological configurations present in the wood sections.
Authors: Meng Wang; Xiaobo Zhou; Fuhai Li; Jeremy Huckins; Randall W King; Stephen T C Wong Journal: Bioinformatics Date: 2007-11-07 Impact factor: 6.937
Authors: Monica Marcuzzo; Pedro Quelhas; Ana Campilho; Ana Maria Mendonça; Aurélio Campilho Journal: Comput Biol Med Date: 2009-07-14 Impact factor: 4.589