Maximum likelihood thresholding based on population mixture models

Kurita, Takio; Otsu, Nobuyuki; Abdelmalek, Nabih N.

doi:10.1016/0031-3203(92)90024-d

Cited by 246 publications

(110 citation statements)

References 7 publications

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“…Some hybrid methods have also been proposed [15]. Most of these algorithms rely either on statistical methods (for example Bayes classifier, maximum likelihood [7,18,19] and moment preservation [37]), or on signal processing (for example maximization of the entropy of the image [1,17], minimization of the variance between the object and the background [27] and the Hadamard transform [3]). Other approaches are based on edge detection algorithms [5,14,15,28,39], on fuzzy classification [6] or on multi scale [34].…”

Section: Related Workmentioning

confidence: 99%

Document Binarization Based on Connected Operators

Naegel¹,

Wendling²

2009

2009 10th International Conference on Document Analysis and Recognition

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Section: Related Workmentioning

confidence: 99%

Document Binarization Based on Connected Operators

Naegel¹,

Wendling²

2009

2009 10th International Conference on Document Analysis and Recognition

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“…Otsu's method from the 70's is the most used thresholding technique in image segmentation [11,12]. It searches for a global threshold value that separates classes in the result.…”

Section: Introductionmentioning

confidence: 99%

“…There are many approaches in the image segmentation methods that are well discussed in the literature [13, 16, 17, 18, and 19]. The author in [13] has classified the thresholding methods mainly into six different categories as segmentation based on histogram [11,12], Clustering based segmentation techniques [20,21,22,23,24,25], entropy based segmentation [26 ,27, 28], methods that extract the threshold value based on the features [29,30], Based on the object attribute methods, using higher order probability distribution [15]. Histogram, clustering and entropy based segmentation methods are the foremost custom behind thresholding methods.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Fuzzy Thresholding for Segmentation of Images

Kumari¹,

Enireddy²

2017

IJCA

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Segmentation is a process of dividing an image into multiple regions making it easier for the analysis. It is also an important step in Image processing. It also has number of applications which includes Medical field to analyze a disease, scientific fields including, engineering and technology, face recognition and object. Many algorithms and techniques have been developed for the image segmentation. To remove distinctive areas from a picture an immediate and straight forward method is using thresholding. It looks for a worldwide esteem that boosts the partition between yield classes. Noisy or uneven illuminated images pose a challenge for segmentation. The utilization of solitary hard limit esteem is absolutely the wellspring of imperative division mistakes in numerous situations like boisterous images or uneven illumination. In this paper a multiregion thresholding technique is displayed to overcome the normal downsides of thresholding strategies when images are debased with artifacts and commotion. Pixels from the images are related to various yield centroids by means of fuzzy membership function, evading any underlying hard choice. To make this strategy robust to noise and artifacts it utilizes spatial data through a local aggregation step where the participation level of every pixel is adjusted by neighborhood data that considers the enrollments of the encompassing pixels. The results are then compared with the existing techniques and results obtained are satisfactory.

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“…For instance, Otsu's between-class variance (BCV) method chooses the optimal thresholds by maximizing the variance between classes with an exhaustive search [11,13]. Many variations of Otsu method have been proposed in the past.…”

Section: Introductionmentioning

confidence: 99%

Thresholding techniques for segmentation of atherosclerotic plaque and lumen areas in vascular arteries

Markiewicz¹,

Dziekiewicz²,

Osowski³

et al. 2015

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. The paper develops the automatic methods of segmentation of the blood vessel area in the images of the multi-slice computed tomography, allowing to separate the lumen from the atherosclerotic plaque areas. The solution is based on the application of different implementations of thresholding, including between class variance in a bimodal mode, Gaussian mixture modeling, clustering technique, polynomial and multilayer perceptron approximations. These methods are compared with many examples of arteries of different percentage of the plaque occupancy in the iliac and femoral arteries. The numerical results of segmentation have been verified by the medical experts and prove its usefulness in medical practice. The presented system can find application in an automatic evaluation of the atherosclerosis progression/regression of patients on the basis of sequence of Computed Tomography slice images.

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Maximum likelihood thresholding based on population mixture models

Cited by 246 publications

References 7 publications

Document Binarization Based on Connected Operators

Document Binarization Based on Connected Operators

Implementation of Fuzzy Thresholding for Segmentation of Images

Thresholding techniques for segmentation of atherosclerotic plaque and lumen areas in vascular arteries

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