Abstract
Thresholding is a popular method of image segmentation. Many thresholding methods utilize only the gray level information of pixels in the image, which may lead to poor segmentation performance because the spatial correlation information between pixels is ignored. To improve the performance of thresolding methods, a novel two-dimensional histogram—called gray level-local variance (GLLV) histogram—is proposed in this paper as an entropic thresholding method to segment images with bimodal histograms. The GLLV histogram is constructed by using the gray level information of pixels and its local variance in a neighborhood. Local variance measures the dispersion of gray level distribution of pixels in a neighborhood. If a pixel’s gray level is close to its neighboring pixels, its local variance is small, and vice versa. Therefore, local variance can reflect the spatial information between pixels. The GLLV histogram takes not only the gray level, but also the spatial information into consideration. Experimental results show that an entropic thresholding method based on the GLLV histogram can achieve better segmentation performance.
Highlights
Image segmentation is the process of grouping an image’s pixels into homogenous regions with respect to one or more characteristics, such as texture, color, and brightness
For bi-level thresholding, only one threshold is selected to segment the image into two classes, while for multi-level thresholding, more than one threshold should be determined to segment the image into multiple classes because the histogram of the image has more than two modes [5]
If the test images are from a dataset, the ground true are taken from the same dataset, and if the test images are taken from reference, the ground true are taken from references
Summary
Image segmentation is the process of grouping an image’s pixels into homogenous regions with respect to one or more characteristics, such as texture, color, and brightness. Different images with the same histogram may have the same threshold, which is not reasonable To overcome this shortcoming, many researchers devoted their efforts to integrating spatial correlation information between pixels into the thresholding process. Abutaleb proposed the concept of a two-dimensional (2D) histogram, which incorporates spatial information as well as the gray-level of pixels to enhance the performance of Kapur’s entropic thresholding method. Our research results show that the filtering operation in the construction of the 2D-D and GLGM histograms has a great effect on the performance of entropic thresholding methods. A novel thresholding method is presented by taking the spatial information between pixels into consideration To this end, a novel 2D histogram—called gray level local variance (GLLV).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
