Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram
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 thres...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-04-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/19/5/191 |
| _version_ | 1798024375962697728 |
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| author | Xiulian Zheng Hong Ye Yinggan Tang |
| author_facet | Xiulian Zheng Hong Ye Yinggan Tang |
| author_sort | Xiulian Zheng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T18:01:27Z |
| format | Article |
| id | doaj.art-e698877c92db4adaaa266cf18eea676c |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T18:01:27Z |
| publishDate | 2017-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-e698877c92db4adaaa266cf18eea676c2022-12-22T04:10:28ZengMDPI AGEntropy1099-43002017-04-0119519110.3390/e19050191e19050191Image Bi-Level Thresholding Based on Gray Level-Local Variance HistogramXiulian Zheng0Hong Ye1Yinggan Tang2Department of Electric Automation Technology, College of Electrical and Information Engineering, Quzhou University, Quzhou 324000, Zhejiang, ChinaDepartment of Electric Automation Technology, College of Electrical and Information Engineering, Quzhou University, Quzhou 324000, Zhejiang, ChinaInstitute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, ChinaThresholding 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.http://www.mdpi.com/1099-4300/19/5/191image segmentationthresholdingShannon entropygray level-local variance histogram |
| spellingShingle | Xiulian Zheng Hong Ye Yinggan Tang Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram Entropy image segmentation thresholding Shannon entropy gray level-local variance histogram |
| title | Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram |
| title_full | Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram |
| title_fullStr | Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram |
| title_full_unstemmed | Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram |
| title_short | Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram |
| title_sort | image bi level thresholding based on gray level local variance histogram |
| topic | image segmentation thresholding Shannon entropy gray level-local variance histogram |
| url | http://www.mdpi.com/1099-4300/19/5/191 |
| work_keys_str_mv | AT xiulianzheng imagebilevelthresholdingbasedongraylevellocalvariancehistogram AT hongye imagebilevelthresholdingbasedongraylevellocalvariancehistogram AT yinggantang imagebilevelthresholdingbasedongraylevellocalvariancehistogram |