An Image Segmentation Algorithm in Image Processing Based on Threshold Segmentation

Zhu, Shiping; Xia, Xi; Zhang, Qingrong; Belloulata, Kamel

doi:10.1109/sitis.2007.116

Cited by 79 publications

(37 citation statements)

References 10 publications

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“…These types of techniques are either applied directly to an image or can be combined with pre and post processing techniques. This type of technique is based on the assumption that the neighboring pixels that lies below or above a certain value or range belongs to the same class or group (are present in the same cluster) [7].…”

Section: Image Segmentationmentioning

confidence: 99%

Image Optimization using Cuckoo Search and Levy Flight Algorithms

Prashar¹,

Jain²,

Mahna³

2017

IJCA

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The paper aims at solving the issue of image noise and image distortion by providing an algorithm for producing an optimized image with optimum threshold value. It does so by firstly explaining in detail what does optimization actually mean and how optimization relates to the field of image processing and can be applied to images to make it more efficient. It then explains the different image segmentation methods and the different parts that we can obtain as a result of image segmentation. It then explains in detail about what is Cuckoo searching algorithm and how an optimal threshold value can be obtained by the application of meta-heuristic Cuckoo Search algorithm via using the famous concepts of Levy Flight optimization. It also explains about the obligate brood parasitic behavior of some cuckoo species and combines it with the Levy Flight behavior in order to obtain the best possible results. As result, we developed an algorithm using a combination of all the algorithms mentioned above as a solution to the issue of image noise, image sizing and image optimization. The algorithm so suggested does this while reducing the size of the image, without compromising on either the quality or the detailing of the image. The paper also intends to calculate the "Mean Squared Error" and "PSNR" value of the squared error image in order to compare the performance of our algorithm with the earlier proposed methods. In the end, the paper also dwells into some of the applications of Image Optimizations in different fields of technology like biometric finger print scanning, steganography and visual cryptography to name a few.

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Section: Image Segmentationmentioning

confidence: 99%

Image Optimization using Cuckoo Search and Levy Flight Algorithms

Prashar¹,

Jain²,

Mahna³

2017

IJCA

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“…Shiping Zhu [36] proposed a new threshold based edge detection and image segmentation algorithm. They calculate the threshold of each pixel in the image on the basis of its neighboring pixels.…”

Section: E Threshold Based Image Segmentationmentioning

confidence: 99%

A Survey: Image Segmentation Techniques

Khan¹

2014

IJET

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Abstract-Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. This paper presents a literature review of basic image segmentation techniques from last five years. Recent research in each of image segmentation technique is presented in this paper.

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“…Expectation step: Calculate the expected value of the log likelihood function, with respect to the conditional distribution of z given x under the current estimate of the parameters θ (t) is given by equation (7): (7) Maximization step: Find the parameter which maximizes the quantity by using equation (8): (8) The EM Algorithm [9] is preferred because it minimizes the signal to noise ratio when compared to other algorithms. The EM algorithm is obtained by alternating the expectation step with the maximization step, and iterate until convergence.…”

Section: Em Algorithmmentioning

confidence: 99%

“…The EM algorithm is applied on 2D Ultrasonic image of uterus and tested. The Gabor function [7] has been recognized by its multiresolution properties and the precision of locating the texture features in the spatial domain. It consists of four steps.…”

Section: Introductionmentioning

confidence: 99%

Uterus Image Segmentation Using Multi-feature EM Algorithm Based on Gabor Filter

Malarkhodi¹,

Banu²

2010

2010 International Conference on Advances in Recent Technologies in Communication and Computing

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In this work the image is segmented effectively based on texture feature by reducing the noise. For effective image segmentation Expectation-Maximization (EM) algorithm based on Gabor filter is used. The EM algorithm is applied on 2D Ultrasonic image of uterus and tested. The Gabor function has been recognized by its multiresolution properties and the precision of locating the texture features in the spatial domain. The approach consists of two steps: finding an effective and discriminative set of texture features using Gabor filters and EM based image segmentation. The work is simulated using MAT LAB (Matrix laboratory).

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An Image Segmentation Algorithm in Image Processing Based on Threshold Segmentation

Cited by 79 publications

References 10 publications

Image Optimization using Cuckoo Search and Levy Flight Algorithms

Image Optimization using Cuckoo Search and Levy Flight Algorithms

A Survey: Image Segmentation Techniques

Uterus Image Segmentation Using Multi-feature EM Algorithm Based on Gabor Filter

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