An algorithm for fast adaptive image binarization with applications in radiotherapy imaging

Abstract

Locally adaptive image binarization with a sliding-window threshold can be an effective tool for various image processing tasks. We have used the method for the detection of bone ridges in radiotherapy portal images. However, a straight-forward implementation of sliding-window processing is too time consuming for routine use. Therefore, we have developed a new thresholding criterion suitable for incremental update within the sliding window, and we show that our algorithm gives better results on difficult portal images than various publicly available adaptive thresholding routines. For small windows, the routine is also faster than an adaptive implementation of the Otsu algorithm that uses interpolation between fixed tiles, and the resulting images are equally good.

Segmentation of X‐ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures

“…CL-Otsu [Otsu, 1979] is one of the most commonly used thresholding techniques; the concept behind this method is to find a threshold value which minimizes the within-class variances of background and foreground voxel classes, which is equivalent to maximizing the variance between the means of the two clustered classes [Sund and Eilertsen, 2003]. CLKittler is a minimum error thresholding method that assumes that the image histogram can be represented by two Gaussian distributions [Kittler and Illingworth, 1986].…”

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

“…Thresholding approach is the most common procedure used in different applications, for example, in biomedical image analysis [41,42], handwritten character identification [43], automatic target recognition [44], change-detection applications [45–47], reconstruction of a map of interference fringes [48], and segmentation based on colour images [49]. Colour is one of the most significant low-level feature that can be used to extract homogeneous regions which are related to objects or part of objects most of the time, multilevel thresholding technique approaches [50,51], thresholding approach in Otsu algorithm [52], threshold approach in segmentation of satellite images [53], and other applications [15,54].…”

Robust automatic detection and removal of fiducial projections in fluoroscopy images: An integrated solution

“…Image binarization. Image binarization is an important step for various medical image processing and document analysis tasks [36,37]. The well-known method described by Ostu [38] can give excellent results.…”