Computerised image analysis of biocrystallograms originating from agricultural products

Procedures are presented for computerised image analysis of biocrystallogra m images, originating from biocrystallization investigations of agricultura l products. The biocrystallization method is based on the crystallographic phenomenon that when adding biological substances, such as plant extracts, to aqueous solutions of dihydrate CuCl2 biocrystallograms with reproducible dendritic crystal structures are formed during crystallisation. The morpho logical features found in the structures are traditionally applied for visu al ranking or classification, e.g. in comparative studies of the effects of farming systems on crop quality. The circular structures contain predomina ntly a single centre from where ramifications expand in a zonal structure. In previous studies primarily texture analysis was applied, and the images analysed and classified by means of a circular region-of-interest (ROI), i. e. the region specified for analysis. In the present study the objective wa s to examine how the discriminative information relevant for classification purposes is distributed over the zonal structure, and how the information is affected by the varying location of the crystallisation centre. The text ure analysis procedures were applied to a so-called degradation series of 3 3 images, including seven groups representing discrete 'treatment levels'. The biocrystallograms were produced over seven consecutive days, on the bas is of a single carrot extract degrading while stored at 6 degrees C. This d egradation is known to induce systematic changes in morphological features over a number of successive days. The biocrystallograms were scanned at 600 dpi. with 256 grey levels. Eight first-order statistical parameters were c alculated for four resolution scales, and 15 second-order parameters for fi ve scales, giving a total of 107 observations for each image. Classificatio n of an individual image was performed by means of stepwise discriminant an alysis. Four main types, and several subtypes and sizes of ROI were examine d. The 33 images as well as a subset of 21 images were examined. When impos ing a restriction on the centre location in the subset. thereby reducing th e within-group variance, the scores were markedly improved. Classifications of the total set and the subset showed scores up to 84.8 and 100%, respect ively. A number of parameters showed a monotonic relationship with degradat ion day number. Multiple linear regressions based on up to eight parameters indicated strong relationships, with R-2 UP to 0.98. It is concluded that the procedures were able to discriminate the seven groups of images, and ar e applicable for biocrystallization investigations of agricultural products . Perspectives for the application of image analysis are briefly mentioned. (C) 1999 Elsevier Science B.V. All rights reserved.