IJ-OpenCV: Combining ImageJ and OpenCV for processing images in biomedicine

Correlation between clinical risk factors and tracheal intubation difficulty in infants with Pierre-Robin syndrome: A retrospective study

“…To calculate the throat area, the annotation file was first converted to a OpenCV performed subsequent processing in the Python environment. First, the single-channel image that was obtained during the previous step underwent color space conversion using the cvtColor function of OpenCV and was converted into a grayscale image (Figure 1d) [24] [26] . The grayscale image was then thresholded (the threshold was set to 1) using the threshold function and becoming a binary image (Figure 1e) [25] [26] .…”

Multi-Template Matching: a versatile tool for object-localization in microscopy images

“…The implementation in KNIME is slower, because of the shared processing between the workflow and the local python installation but has improved modularity. Previous implementations of template matching (Peravali et al , 2011; Tseng et al , 2011; Domínguez et al , 2017) do not allow to use different templates for object detections, neither do they provide a way to prevent multiple detections of the same object, which motivated the implementation of a NMS. Finally, the demonstrated template matching pipeline could also facilitate feedback microcopy applications by interfacing it with the control software of automated microscopes, thus enabling the automated acquisition of ROI for tracking or automated zooming-in on target structures without manual intervention (Peravali et al , 2011; Pandey et al , 2019).…”

“…We implemented template matching (Brunelli, 2009) as both a Fiji plugin and a KNIME workflow compatible with images of any bit depth. To predict the position of a template within a target image, the algorithm first computes a correlation map using the function matchTemplate from OpenCV, within Fiji (Domínguez et al , 2017) or KNIME (Fig.1B, 1E and Suppl. Fig.1B).…”

Correlation between clinical risk factors and tracheal intubation difficulty in infants with Pierre-Robin syndrome: A retrospective study

“…To calculate the throat area, the annotation file was first converted to a OpenCV performed subsequent processing in the Python environment. First, the single-channel image that was obtained during the previous step underwent color space conversion using the cvtColor function of OpenCV and was converted into a grayscale image (Figure 1d) [21] [23] . The grayscale image was then thresholded (the threshold was set to 1) using the threshold function and becoming a binary image (Figure 1e) [22] [23] .…”