INDEPENDENT, SEMI-AUTOMATIC CLASSIFICATION OF PETROGRAPHIC FEATURES IN VOLCANIC ROCKS USING FIJI AND WEKA
TWS uses a fast-random-forest algorithm to classify an image based on a set of training pixels selected by the user - in this case different mineral phases, vesicles, etc. Training of the classifier is intuitive and fast. For example, three classes each with eleven training spots are classified in less than 1 minute for a medium to high-resolution image. Eight plane polarized light photomicrographs with increasing crystallinity and complexity were classified (i.e. trained) and automatically segmented using TWS. Samples where the assigned classes have distinct, homogeneous RGB values and sharp boundaries are successfully classified with TWS. However, samples where the classes are heterogeneous but similar, as a result of alteration for example, are not adequately classified. Once classified, two major efficiency gains are possible: (1) the classifier can be saved and applied again to any similar sample, and (2) the segmented image is immediately available for thresholding in ImageJ / Fiji (i.e. separating into class-specific images) without manual tracing or cut-and-paste. The thresholded images can then be measured using the image analysis tools in ImageJ / Fiji (e.g., dimensions, area, circularity, long-axis orientation, etc.).