Automatic thresholding from the gradients of region boundaries

Abstract

We present an approach for automatic threshold segmentation of greyscale images. The procedure is inspired by a reinterpretation of the strategy observed in human operators when adjusting thresholds manually and interactively by means of 'slider' controls. The approach translates into two methods. The first one is suitable for single or multiple global thresholds to be applied globally to images and consists of searching for a threshold value that generates a phase whose boundary coincides with the largest gradients in the original image. The second method is a variation, implemented to operate on the discrete connected components of the thresholded phase (i.e. the binary regions) independently. Consequently, this becomes an adaptive local threshold procedure, which operates relative to regions, rather than to local image subsets as is the case in most local thresholding methods previously published. Adding constraints for specifying certain classes of expected objects in the images can improve the output of the method over the traditional 'segmenting first, then classify' approach.

Measurement error in μCT‐based three‐dimensional geometric morphometrics introduced by surface generation and landmark data acquisition

“…algorithm by Otsu (1979) implemented in the Auto Local Threshold function was chosen as one of the automatic local thresholding algorithms because local versions of the algorithm by Otsu (1979) yielded good results in previous studies (e.g. Landini et al 2017;Healy et al 2018). The three-dimensionality of the data was accounted for by using the intersecting-3 strategy described in Supporting Information Text S1.…”

“…Our choice of automatic local thresholding algorithms and corresponding parameters was based on previous studies (Otsu algorithm;Landini et al 2017;Healy et al 2018) and on the performance of different algorithm-parameter combinations applied to a rather arbitrarily created stack of reconstructed phantom images (MidGrey algorithm). We did not evaluate the effects of image noise, pixel size or contrast of bone and soft tissue.…”

Mereotopological Correction of Segmentation Errors in Histological Imaging

“…For the nuclear regions, colour deconvolution [19] is used to separate the colour image information into haematoxylin-only and eosin-only images. A regional gradient and circularity and size constraints algorithm [20] is applied twice to the haematoxylin image. The first pass identifies fairly circular nuclear regions (with circularity ≥0.6), within a range of expected sizes (minimum and maximum areas of 400 and 3600 pixels, respectively, all determined empirically).…”

The effects of cracks on the quantification of the cancellous bone fabric tensor in fossil and archaeological specimens: a simulation study

“…The CT scans were acquired using either a Siemens Inveon (Siemens AG, Germany) or a GE BrightSpeed (GE Healthcare, UK); scan settings are detailed in Table 1. The scans were processed in the open-source software Ima-geJ 1.47 (http://imagej.nih.gov/ij/), and segmented using the local thresholding algorithm of Bernsen (1986), as implemented in Ima-geJ (Landini, 2008;Landini et al 2016; see also Appendix). For the large bird bones scanned using the GE Brightspeed, the scans were resampled to isotropic voxels and at triple the original resolution using a bicubic interpolation algorithm; this did not alter the underlying structure in the scan data, but did facilitate a more effective segmentation of the cancellous bone.…”

“…In this study, the CT scans were segmented using the algorithm of Bernsen (1986), as implemented in ImageJ (Landini, 2008;Landini et al 2016; note that the implementation operates on 8-bit images only). This algorithm is a binary pixel classifier, identifying whether a given pixel in an image belongs to the foreground (bone tissue, assigned a value of 1) or background (intertrabecular spaces, assigned a value of 0).…”