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Journal ArticleDOI

A threshold selection method from gray level histograms

01 Jan 1979-IEEE Transactions on Systems, Man, and Cybernetics (IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS)-Vol. 9, Iss: 1, pp 62-66
About: This article is published in IEEE Transactions on Systems, Man, and Cybernetics.The article was published on 1979-01-01. It has received 37017 citations till now.
Citations
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Journal ArticleDOI
TL;DR: The authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations.
Abstract: The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogram-based model, the FM has an intrinsic limitation-no spatial information is taken into account. This causes the FM model to work only on well-defined images with low levels of noise; unfortunately, this is often not the the case due to artifacts such as partial volume effect and bias field distortion. Under these conditions, FM model-based methods produce unreliable results. Here, the authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown that the FM model is a degenerate version of the HMRF model. The advantage of the HMRF model derives from the way in which the spatial information is encoded through the mutual influences of neighboring sites. Although MRF modeling has been employed in MR image segmentation by other researchers, most reported methods are limited to using MRF as a general prior in an FM model-based approach. To fit the HMRF model, an EM algorithm is used. The authors show that by incorporating both the HMRF model and the EM algorithm into a HMRF-EM framework, an accurate and robust segmentation can be achieved. More importantly, the HMRF-EM framework can easily be combined with other techniques. As an example, the authors show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a three-dimensional fully automated approach for brain MR image segmentation.

6,335 citations


Cites methods from "A threshold selection method from g..."

  • ...According to this, we carry out initial estimation using a discriminant measurebased thresholding method proposed by Otsu [ 21 ]....

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Journal ArticleDOI
TL;DR: A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present, and is applied at an early stage in an automated data analysis, before a tissue model is available.
Abstract: A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available. Described as nonparametric nonuniform intensity normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. The performance of this method is evaluated using both real and simulated MR data.

4,613 citations


Cites methods from "A threshold selection method from g..."

  • ...Since the accuracy of this segmentation is not critical, the foreground is determined using a simple threshold chosen automatically by analyzing the histogram of the volume [20]....

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Journal ArticleDOI
TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
Abstract: We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)

4,543 citations


Cites background or methods from "A threshold selection method from g..."

  • ...The Otsu method still remains one of the most re enced thresholding methods....

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  • ...Mean square clustering is used in [46] while fuzzy clustering ideas have been applied in [30], [47]....

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  • ...J. Z. Liu and W. Q. Li, ‘‘The automatic thresholding of gray-lev pictures via two-dimensional Otsu method,’’Acta Automatica Sin.19, 101–105~1993!. 51....

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  • ...In a similar study, thresho ing based on isodata clustering is given in Velasco.48 Some limitations of the Otsu method are discussed in Lee a Park.49 Liu and Li50 generalized it to a 2-...

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  • ...For example, Shape–Sezan and Cluster–Otsu, refer, respectively, to the shape-based thresholding method in ctronic Imaging / January 2004 / Vol. 13(1) e , f - - duced in a paper by Sezan and to the clustering-ba thresholding method first proposed by Otsu....

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Journal ArticleDOI
29 Jun 2007-Cell
TL;DR: The transcriptional landscape of the four human HOX loci is characterized at five base pair resolution in 11 anatomic sites and 231 HOX ncRNAs are identified that extend known transcribed regions by more than 30 kilobases, suggesting transcription of ncRNA may demarcate chromosomal domains of gene silencing at a distance.

4,003 citations


Cites methods from "A threshold selection method from g..."

  • ...We addressed this challenge by adapting a signal processing algorithm used in computer vision termed Otsu’s method (Otsu, 1979)....

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Journal ArticleDOI
TL;DR: Two methods of entropic thresholding proposed by Pun (Signal Process.,2, 1980, 223–237;Comput.16, 1981, 210–239) have been carefully and critically examined and a new method with a sound theoretical foundation is proposed.
Abstract: Two methods of entropic thresholding proposed by Pun (Signal Process.,2, 1980, 223–237;Comput. Graphics Image Process.16, 1981, 210–239) have been carefully and critically examined. A new method with a sound theoretical foundation is proposed. Examples are given on a number of real and artifically generated histograms.

3,551 citations

References
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Journal ArticleDOI
TL;DR: A system with three basic components: 1) a sensor which rigorously and effectively scans microscopic fields and converts the optical information into digital form; 2) human analysts who contribute heuristics, devise image processing methods and 3) quantitative methods which fully utilize measurements of light intensity of optical density.
Abstract: Automation of the acquisition and interpretation of data in microscopy has been a focus of biomedical research for almost a decade. In spite of many serious mechanical perception of microscopic fields with a reliability that would inspire routine application still eludes us. Many facets of the problem appear to be well within the grasp of presentday technology. Thus, available histochemical techniques make it possible to prepare biological materials so that morphological integrity is preserved, key constituents are stained stoichiometrically, and the specimens are favorably dispersed for effective imaging one by one. Scanning microscopes now have the requisite sensitivity, resolution, and stability to sample such objects and make photometric measurements over a wide range of magnifications and wavelengths within the visible and near-visible spectrum. Furthermore, modern large capacity, high speed data facilities at last provide the ability to manipulate the hitherto unmanageable quantities of optical information contained within all but the simplest images. With the basic materials for achieving automation via mensuration finally a t hand, attention has been turned toward generating and evaluating methods for extracting meaning from quantitative optical information. Definitive concepts for image structuring and image characterization have yet to be realized, to be given satisfactory operational definitions, and to be assembled within a machine-oriented perceptual framework.6 Criteria for effective and efficient discrimination and interpretation of images must be evolved. It would be a serious mistake to presuppose that mechanical perception must mimic the human’s perceptual apparatus in organizing images as complexes of picture eIements. Likewise i t would be serious to ignore traditional descriptive morphology and established taxonomies. In steering a middle course, the exploration of many complementary approaches and the introduction of numeric methods which fully utilize measurements of light intensity of optical density seemed to us to hold the most promise for augmenting and explicating the existing, largely verbal tradition of microscopic morphology. To realize these objectives, we have designed a system with three basic components: 1) a sensor which rigorously and effectively scans microscopic fields and converts the optical information into digital form; 2) human analysts who contribute heuristics, devise image processing methods and en-

731 citations

Journal ArticleDOI
C.K. Chow1, T. Kaneko1
TL;DR: Experimental results on cardioangiograms are presented to successfully demonstrate the feasibility of the threshold method to detect boundaries in radiographic images, which is insensitive to shading or gradually varying interference.

450 citations


"A threshold selection method from g..." refers background in this paper

  • ...It is also noted that cr2 is based on the second-order statistics (class variances), while (T2 is based on the (4) first-order statistics (class means)....

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  • ...The class means (4) and (5) serve as estimates of the mean levels of the classes in the original gray-level picture....

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Journal ArticleDOI
TL;DR: A method of inverse probability is obtained which permits maximum likelihood estimates to be made of the true data-surveillance object association and the maximum likelihood estimator is given in a form that lends itself to sequential computations performed in real time as the data arrives.
Abstract: This paper contains a theoretical analysis of the data association problem of a common type of surveillance system. By a method of inverse probability, the optimal data processor is obtained which permits maximum likelihood estimates to be made of the true data-surveillance object association. The maximum likelihood estimator is given in a form that lends itself to sequential computations performed in real time as the data arrives. Examples of the use of this estimator make clear the precise mathematical meaning of such terms as tentative, confirmed, and established data tracks, and the concept of search areas. The analytical technique is of general use in a variety of surveillance situations. Computer implementations are possible.

243 citations

Journal ArticleDOI
TL;DR: A method of handling cases in which the peaks are very unequal in size and the valley is broad is described, in which points that lie on or near the edges of objects are determined.
Abstract: Threshold selection for picture segmentation is relatively easy when the frequency distribution of gray levels in the picture is strongly bimodal, with the two peaks comparable insize and separated by a deep valley. This report describes a method of handling cases in which the peaks are very unequal in size and the valley is broad. A Laplacian operation is applied to the picture to determine points that lie on or near the edges of objects. Threshold selection becomes easier when the frequency distribution of gray levels of these points is used.

208 citations


"A threshold selection method from g..." refers methods in this paper

  • ...They are, for example, the valley sharpening technique [2], which restricts the histogram to the pixels with large absolute values of derivative (Laplacian or gradient), and the difference histogram method [3], which selects the threshold at the gray level with the maximal amount of difference....

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Journal ArticleDOI
TL;DR: A recursive branching algorithm for multiple-object discrimination and tracking consists of a bank of parallel filters of the Kalman form, each of which estimates a trajectory associated with a certain selected measurement sequence.
Abstract: A recursive branching algorithm for multiple-object discrimination and tracking consists of a bank of parallel filters of the Kalman form, each of which estimates a trajectory associated with a certain selected measurement sequence. The measurement sequences processed by the algorithm are restricted to a tractable number by combining similar trajectory estimates, by excluding unlikely measurement/state associations, and by deleting unlikely trajectory estimates. The measurement sequence selection is accomplished by threshold tests based on the innovations sequence and state estimates of each filter. Numerical experiments performed using the algorithm illustrate how the accuracy of the a priori state estimates and trajectory model influences the selectivity of the algorithm.

123 citations


"A threshold selection method from g..." refers methods in this paper

  • ...For example, the histogram is approximated in the least square sense by a sum of Gaussian distributions, and statistical decision procedures are applied [4]....

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