Maximum Segmented Image Information Thresholding

Leung, Chi Kin Randolph; Lam, F.K.

doi:10.1006/gmip.1997.0455

Cited by 27 publications

(14 citation statements)

References 22 publications

(29 reference statements)

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“…Since the thresholds determined in (5.4) are the best possible, no threshold selection technique published in the literature [28,29] can give a performance on the scenes in E better than that of the best thresholds indicated in (5.4). Therefore, we conclude that any published (global) threshold selection method will perform inferior to the non-scale-based and the scale-based fuzzy connectedness methods described in this paper on the scenes in E.…”

Section: Quantitative Comparisonmentioning

confidence: 98%

Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation

Saha

Udupa

Odhner

2000

Computer Vision and Image Understanding

286

247

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Section: Quantitative Comparisonmentioning

confidence: 98%

Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation

Saha

Udupa

Odhner

2000

Computer Vision and Image Understanding

286

247

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“…33,35,51 Although, the thresholding method by Otsu was proposed three decades ago, it has become quite popular because of its classical theoretical foundation. Recently, the method has been implemented within the ITK library 54 and has been used in several medical imaging [8][9][10][11][12][13] and other applications.…”

Section: Iiib Experimental Plansmentioning

confidence: 99%

“…Over the past five decades, many automatic threshold selection methods have been reported in literature. [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] In late 80's, Sahoo et al 16 published a survey of optimum thresholding methods while Lee et al 17 reported results of a comparative study of several thresholding methods. Glasbey 18 published results of another comparative study involving eleven histogram-based thresholding algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Kitler and Illingworth 33 developed a thresholding method by minimizing segmentation errors defined from an information-theoretic perspective, while Dunn et al 34 used a uniform error criterion. Leung and Lam 35 developed a method that maximizes segmented image information derived using an information-theoretic approach and demonstrated that their method is better than the methods based on minimum and uniform errors. 33,34 Sahoo et al 37 developed a thresholding method using Renyi's entropy that includes both maximum entropy as well as entropic correction methods.…”

Section: Introductionmentioning

confidence: 99%

“…Also, in this paper, we present an experimental setup to quantitatively examine both accuracy and reproducibility of the new thresholding method on several medical image data sets including repeat scan multidetector CT images of cadaveric ankles specimens and compare its performance with Otsu's method 21 which has become a popular technique for automatic thresholding. Also, the performance of the new method has been compared with three other thresholding methods based on minimum error thresholding, 33 maximum segmented image information 35 and minimization of homogeneity-and uncertainty-based energy. 51 …”

Section: Introductionmentioning

confidence: 99%

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A new multi-object image thresholding method based on correlation between object class uncertainty and intensity gradient

2012

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Purpose: Image thresholding and gradient analysis have remained popular image preprocessing tools for several decades due to the simplicity and straight-forwardness of their definitions. Also, optimum selection of threshold and gradient strength values are hidden steps in many advanced medical imaging algorithms. A reliable method for threshold optimization may be a crucial step toward automation of several medical image based applications. Most automatic thresholding and gradient selection methods reported in literature primarily focus on image histograms ignoring a significant amount of information embedded in the spatial distribution of intensity values forming visible features in an image. Here, we present a new method that simultaneously optimizes both threshold and gradient values for different object interfaces in an image that is based on unification of information from both the histogram and spatial image features; also, the method works for unknown number of object regions. Methods: A new energy function is formulated by combining the object class uncertainty measure, a histogram-based feature, of each pixel with its image gradient measure, a spatial contextual feature in an image. The energy function is designed to measure the overall compliance of the theoretical premise that, in a probabilistic sense, image intensities with high class uncertainty are associated with high image gradients. Finally, it is expressed as a function of threshold and gradient parameters and optimum combinations of these parameters are sought by locating pits and valleys on the energy surface. A major strength of the algorithm lies in the fact that it does not require the number of object regions in an image to be predefined. Results: The method has been applied on several medical image datasets and it has successfully determined both threshold and gradient parameters for different object interfaces even when some of the thresholds are almost impossible to locate in the histogram. Both accuracy and reproducibility of the method have been examined on several medical image datasets including repeat scan 3D multidetector computed tomography (CT) images of cadaveric ankles specimens. Also, the new method has been qualitatively and quantitatively compared with Otsu's method along with three other algorithms based on minimum error thresholding, maximum segmented image information and minimization of homogeneity-and uncertainty-based energy and the results have demonstrated superiority of the new method. Conclusions: We have developed a new automatic threshold and gradient strength selection algorithm by combining class uncertainty and spatial image gradient features. The performance of the method has been examined in terms of accuracy and reproducibility and the results found are better as compared to several popular automatic threshold selection methods.

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Fuzzy Connected Object Delineation: Axiomatic Path Strength Definition and the Case of Multiple Seeds

Saha

Udupa

2001

Computer Vision and Image Understanding

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Maximum Segmented Image Information Thresholding

Cited by 27 publications

References 22 publications

Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation

Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation

A new multi-object image thresholding method based on correlation between object class uncertainty and intensity gradient

Fuzzy Connected Object Delineation: Axiomatic Path Strength Definition and the Case of Multiple Seeds

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