A novel statistical image thresholding method

Li, Zuoyong; Liu, Chuancai; Liu, Guanghai; Cheng, Yong; Yang, Xibei; Zhao, Cairong

doi:10.1016/j.aeue.2009.11.011

Cited by 57 publications

(31 citation statements)

References 21 publications

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“…When α is large the method degenerates to class variance sum, which make class variance discrepancy has limited effect. On the contrary, if α is small the method will be based on class variance discrepancy, and the effect of class variance sum will be neglected [12].…”

Section: = (16) (T)<= <= (T) or (T)<= <= (T)mentioning

confidence: 99%

“…Otsu method classified within clustering of gray-level information. Moreover, it is one of the most popular and effective image thresholding technique, so that many thresholding techniques have been constructed to revise Otsu method; each technique improved Otsu method in a specific way like [9] valley emphasis technique, [10] neighborhood valley emphasis technique, [11] variance and intensity contrast, and [12] variance discrepancy technique.…”

Section: Introductionmentioning

confidence: 99%

“…It based on classification image pixels into object and background depended on the relation between the gray level value of the pixels and the threshold [8,9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Thresholding Techniques for SAR Images

Al-Bayati

El-Zaart

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Section: = (16) (T)<= <= (T) or (T)<= <= (T)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Thresholding Techniques for SAR Images

Al-Bayati

El-Zaart

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…The minimum-error method ranked as the best in a comprehensive survey of image thresholding conducted by [2]. Qiao et al [15] analyzed the limitation of thresholding methods based on within-class variance for images whose background and object have very different sizes and suggested a thresholding criterion based on the convex combination of within class variance and intensity contrast between the object and background. Recently Li Z, et al [16] introduced another improvement for thresholding based on withinclass variance and proposed a novel statistical criterion for threshold selection that takes class variance sum and variance discrepancy into account at the same time.…”

Section: Manymentioning

confidence: 99%

A Novel Approach for MRI Brain Images Segmentation

A¹,

Abdel-Azim²

2013

IJIGSP

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Abstract-Segmentation of brain from magnetic resonance (MR) images has important applications in neuroimaging, in particular it facilitates in extracting different brain tissues such as cerebrospinal fluids, white matter and gray matter. That helps in determining the volume of the tissues in three-dimensional brain MR images, which yields in analyzing many neural disorders such as epilepsy and Alzheimer disease. The Fisher information is a measure of the fluctuations in the observations. In a sense, the Fisher information of an image specifies the quality of the image. In this paper, we developed a new thresholding method using the Fisher information measure and intensity contrast to segment medical images. It is the weighted sum of the Fisher information measure and intensity contrast between the object and background. This technique is a powerful method for noisy image segmentation. The method applied on a normal MR brain images and a glioma MR brain images. Experimental results show that the use of the Fisher information effectively segmented MR brain images.

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“…A survey of thresholding methods and their applications exists in literature [2]. Of which, the variance-based thresholding technology is a kind of famous method for image segmentation [3][4][5][6][7][8][9]. In earlier research, Otsu proposed minimum within-class variance criteria to select the best threshold [3].…”

Section: Introductionmentioning

confidence: 99%