EXPLORATION OF THE PHYSICAL PROPERTIES OF INTERNAL CHARACTERISTICS OF SUGAR MAPLE LOGS AND RELATIONSHIPS WITH CT IMAGES

Two groups of sugar maple ( Acer saccharum Marsh) logs were scanned using an X-ray scanner to identify and locate their main internal characteristics. In the design of this exploratory study, five logs produced from a freshly cut tree (Group 1) and three logs (Group 2) sampled from a sawmill yard, were scanned to identify the various wood types present: rot; knots; colored heartwood; and sapwood. Based on these computed tomography (CT) images, four or five disks (20 mm thick) were cut from each log. Blocks of 10 x 10 x 10 mm were then cut from each disk representing areas of each type of wood. The green density, basic density, and moisture content of each block were measured to assess within-tree variations between logs and within wood type. CT grey levels in the CT images were then measured for individual blocks to assess the feasibility of identifying wood types. Finally, a correlation analysis was carried out between physical properties and CT grey levels for each internal characteristic. The results generally indicated that, for both groups of logs, the type of wood was the most significant source of variation in green density, basic density, and moisture content. Statistically significant differences in these physical properties were also observed between sapwood and the other types of wood. This is a result of practical importance since sapwood in sugar maple is the main driver of product value. From the grey level variation observed in CT images, it is possible to separate sapwood from colored heartwood and knots. The contrast in grey level between the sapwood and the area of rot is not so obvious, but it can be enhanced by means of statistical methods. The correlation analysis indicated that green density was the variable that best correlated with grey level variations in CT images. A linear relationship between green density and grey level was established for each type of wood. The coefficient of determination (R 2 ) obtained from the simple regression analysis between grey level and green density varied between 0.32 and 0.82 depending on wood type. Nevertheless, significant differences were observed in the slopes of the curves. It is hypothesized that these differences could be mainly attributed to differences in the content and orientation of crystalline structures present in each type of wood.

[1]  T. Shimokawa,et al.  Production of 2,5-dimethoxyhydroquinone by the brown-rot fungus Serpula lacrymans to drive extracellular Fenton reaction , 2004 .

[2]  Urban Nordmark,et al.  Knot Identification from CT Images of Young Pinus sylvestris Sawlogs Using Artificial Neural Networks , 2002 .

[3]  J. Lingeman,et al.  Correction of helical CT attenuation values with wide beam collimation: in vitro test with urinary calculi. , 2001, Academic radiology.

[4]  J. Oja,et al.  Evaluation of knot parameters measured automatically in CT-images of Norway spruce (Picea abies (L.) Karst.) , 2000, Holz als Roh- und Werkstoff.

[5]  J. Lingeman,et al.  Helical CT of urinary calculi: effect of stone composition, stone size, and scan collimation. , 2000, AJR. American journal of roentgenology.

[6]  L. Moberg.  Models of Internal Knot Diameter for Pinus sylvestris , 2000 .

[7]  J. Oja,et al.  The appearance of resin pockets in CT-images of Norway spruce (Picea abies (L.) Karst.) , 1999, Holz als Roh- und Werkstoff.

[8]  Kentaro Suzuki,et al.  Characterization of acetylated wood decayed by brown-rot and white-rot fungi , 1999, Journal of Wood Science.

[9]  B. Saltzman,et al.  Accurate determination of chemical composition of urinary calculi by spiral computerized tomography. , 1998, The Journal of urology.

[10]  L. Björklund.  The interior knot structure of Pinus sylvestris stems , 1997 .

[11]  R. Geise.  Computed Tomography: Physical Principles, Clinical Applications, and Quality Control , 1995 .

[12]  Anita Tailor,et al.  Introductory digital image processing: a remote sensing perspective: Jensen, J R Prentice-Hall, Englewood Cliffs, NJ, USA (1986) £51.30 pp 392 , 1986 .

[13]  J. G. Carroll,et al.  Radiation effects on organic materials , 1960 .

[14]  R. D. Gibbs,et al.  STUDIES IN TREE PHYSIOLOGY: IV. FURTHER INVESTIGATIONS OF SEASONAL CHANGES IN MOISTURE CONTENT OF CERTAIN CANADIAN FOREST TREES , 1957 .

[15]  S. Grundberg,et al.  Classification of scots pine (Pinus sylvestris) knots in density images from CT scanned logs , 2007, Holz als Roh- und Werkstoff.

[16]  Hans Petersson,et al.  Predicting knot diameter of Pinus sylvestris in Sweden , 1999 .

[17]  S. Guddanti,et al.  Replicating sawmill sawing with TOPSAW using CT images of a full-length hardwood log , 1998 .

[18]  Suchendra M. Bhandarkar,et al.  Machine Vision and Applications c ○ Springer-Verlag 1999 CATALOG: a system for detection and rendering of internal log defects using computer tomography , 1997 .

[19]  Philip H. Steele,et al.  Increased lumber value from optimum orientation of internal defects with respect to sawing pattern in hardwood sawlogs , 1994 .

[20]  Steven H Bullard,et al.  Relative Kerf and Sawing Variation Values for Some Hardwood Sawing Machines , 1992 .

[21]  Paul C. Wang,et al.  NMR imaging of internal features in wood , 1989 .

[22]  Charles W. McMillin,et al.  Ultrafast CT scanning of an oak log for internal defects , 1989 .

[23]  Brian V. Funt,et al.  Detection of internal log defects by automatic interpretation of computer tomography images , 1987 .

[24]  Charles W. McMillin,et al.  Locating knots by industrial tomography- A feasibility study , 1984 .

[25]  A. J. Panshin,et al.  Textbook of Wood Technology , 1964 .

[26]  H. M. Dale,et al.  STUDIES ON HEARTWOOD FORMATION AND STAINING IN SUGAR MAPLE, ACER SACCHARUM MARSH. , 1955 .