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Image Segmentation by Using Threshold Techniques

TL;DR: This paper attempts to undertake the study of segmentation image techniques by using five threshold methods as Mean method, P-tile method, Histogram Dependent Technique (HDT), Edge Maximization Technique (EMT) and visual Technique and they are compared with one another so as to choose the best technique for threshold segmentation techniques image.
Abstract: This paper attempts to undertake the study of segmentation image techniques by using five threshold methods as Mean method, P-tile method, Histogram Dependent Technique (HDT), Edge Maximization Technique (EMT) and visual Technique and they are compared with one another so as to choose the best technique for threshold segmentation techniques image. These techniques applied on three satellite images to choose base guesses for threshold segmentation image.
Citations
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01 Jan 2016
TL;DR: This paper provides a review on the various image segmentation techniques proposed in the literature and shows how to cluster pixels into salient image regions corresponding to individual surfaces, objects, or natural parts of objects.
Abstract: Digital image processing plays a vital role in many applications to retrieve required information from the given image in a way that it has not affect the other features of the image. Image segmentation is one of the most important tasks in image processing which is used to partition an image into several disjoint subsets such that each subset corresponds to a meaningful part of the image. The goal of image segmentation is to cluster pixels into salient image regions corresponding to individual surfaces, objects, or natural parts of objects. With the growing research on image segmentation many segmentation methods have been developed and interpreted differently towards content analysis and image understanding for different applications. Thus an organized review on image segmentation methods is essential and this paper provides a review on the various image segmentation techniques proposed in the literature.

543 citations


Cites background from "Image Segmentation by Using Thresho..."

  • ...Adaptive threshold is performed based on gray level, neighborhood and pixel coordinate’s properties of the image [1]....

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Journal ArticleDOI
TL;DR: A comparative study of the basic Block-Based image segmentation techniques is presented, which shows how these techniques have to be combined with domain knowledge in order to effectively solve an image segmentsation problem for a problem domain.

318 citations

Journal ArticleDOI
TL;DR: An improved Otsu method, named the weighted object variance (WOV), is proposed in this research to detect defects on product surfaces and provides better segmentation results.

172 citations

Journal ArticleDOI
TL;DR: This survey addressed various image segmentation techniques, evaluates them and presents the issues related to those techniques.
Abstract: Image segmentation is a mechanism used to divide an image into multiple segments. It will make image smooth and easy to evaluate. Segmentation process also helps to find region of interest in a particular image. The main goal is to make image more simple and meaningful. Existing segmentation techniques can't satisfy all type of images. This survey addressed various image segmentation techniques, evaluates them and presents the issues related to those techniques. 

163 citations


Cites methods from "Image Segmentation by Using Thresho..."

  • ...Salem Saleh Al-amri [18] has applied Mean technique, Pile technique, HDT, and EMT technique on three satellite images in order to select the best segmented image from all above techniques....

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  • ...Salem Saleh Al-amri [18] has applied Mean technique,...

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Journal ArticleDOI
TL;DR: A method for detecting the maturity levels (green, orange, and red) of fresh market tomatoes by combining the feature color value with the backpropagation neural network (BPNN) classification technique is proposed.

137 citations


Cites methods from "Image Segmentation by Using Thresho..."

  • ...In this process, the tomato sample images were segmented using the threshold segmentation algorithm previously used by Al-Amri et al. (2010) and Zhu et al. (2007, 2010)....

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References
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Journal ArticleDOI
TL;DR: A computationally efficient solution to the problem of minimum error thresholding is derived under the assumption of object and pixel grey level values being normally distributed and is applicable in multithreshold selection.

2,145 citations


Additional excerpts

  • ...Index Terms—Image segmentation, Threshold, automatic Threshold —————————— ——————————...

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Journal ArticleDOI
TL;DR: A new image thresholding method based on minimizing the measures of fuzziness of an input image and a fuzzy range is defined to find the adequate threshold value within this range.

889 citations


Additional excerpts

  • ...Index Terms—Image segmentation, Threshold, automatic Threshold —————————— ——————————...

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Journal ArticleDOI
TL;DR: The entropy-based thresholding algorithm is extended to the 2-dimensional histogram and it was found that the proposed approach performs better specially when the signal to noise ratio (SNR) is decreased.
Abstract: Automatic thresholding of the gray-level values of an image is very useful in automated analysis of morphological images, and it represents the first step in many applications in image understanding. Recently it was shown that by choosing the threshold as the value that maximizes the entropy of the 1-dimensional histogram of an image, one might be able to separate, effectively, the desired objects from the background. This approach, however, does not take into consideration the spatial correlation between the pixels in an image. Thus, the performance might degrade rapidly as the spatial interaction between pixels becomes more dominant than the gray-level values. In this case, it becomes difficult to isolate the object from the background and human interference might be required. This was observed during studies that involved images of the stomach. The objective of this report is to extend the entropy-based thresholding algorithm to the 2-dimensional histogram. In this approach, the gray-level value of each pixel as well as the average value of its immediate neighborhood is studied. Thus, the threshold is a vector and has two entries: the gray level of the pixel and the average gray level of its neighborhood. The vector that maximizes the 2-dimensional entropy is used as the 2-dimensional threshold. This method was then compared to the conventional 1-dimensional entropy-based method. Several images were synthesized and others were obtained from the hospital files that represent images of the stomach of patients. It was found that the proposed approach performs better specially when the signal to noise ratio (SNR) is decreased. Both, as expected, yielded good results when the SNR was high (more than 12 dB).

688 citations


"Image Segmentation by Using Thresho..." refers background or methods in this paper

  • ...It yields good anti-noise capabilities; however, it is obviously not applicable if the object area ratio is unknown or varies from picture to picture [6]....

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  • ...Because of the advantage of simple and easy implementation, the global threshold has been a popular technique in many years [6][7][8]....

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01 Jan 2009
TL;DR: In this article, a hybrid thresholding method that combines the P-tile method with an edge detector was proposed to assist it in the thresholding process, which successfully generates more accurate object shape extraction than the conventional methods.
Abstract: Summary The main disadvantage of traditional global thresholding techniques is that they do not have an ability to exploit information of the characteristics of target images that they threshold. In this paper, we propose a hybrid thresholding method that combines the P-tile method with an edge detector to assist it in the thresholding process. This method successfully generates more accurate object shape extraction than the conventional methods.

31 citations

Proceedings ArticleDOI
30 Jul 2007
TL;DR: Compared three thresholding results, it shows that statistic iterative method greatly improved the anti-noise capability of image segmentation and had the good result to the image of the worth and not easy to segment in full value thresholding method.
Abstract: Image segment is one of the most fundamental and difficult problems in image analysis and computer vision. The image thresholding problem is treated as an important issue in image processing, and it can not only reduce the image data, but also lay a good foundation for succedent target recognition and image understanding. Character of global thresholding segmentation and local thresholding was analyzed in image segmentation. A new thresholding statistic iterative arithmetic is presented to overcome the direct worth method in thresholding, aiming at some lighting asymmetry and the abrupt a blemish for, or bigger arithmetic figure in ratio in a variety in gray of background image. Statistics iterative thresholding segmentation, based on image gray histogram and Gauss statistics distributing, obtain the theory expression of statistics iterative method and the best worth thresholding method and steps. Aviation image was thresholding segmentation using statistic iterative arithmetic, histogram technique and adaptive method respectively. Compared three thresholding results, it shows that statistic iterative method greatly improved the anti-noise capability of image segmentation and had the good result to the image of the worth and not easy to segment in full value thresholding method.

17 citations