Optimization-Based Image Segmentation by Genetic Algorithms

Chabrier, Sébastien; Rosenberger, Christophe; Emile, Bruno; Laurent, Hélène

doi:10.1155/2008/842029

Cited by 33 publications

(26 citation statements)

References 25 publications

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“…where u + and u -are obtained by solving Equations (5) and (6). In the PS model, two coupled equations must be solved to obtain u + and u -before each iteration, and the computational cost is very expensive.…”

Section: (Ps) Modelmentioning

confidence: 99%

“…The segmentation goal is to separate image domain into a collection of distinct regions, upon which other high-level tasks such as objects recognition and tracking can be further performed. Due to the presence of noise, complex background, low intensity contrast with weak edges, and intensity inhomogeneity [1], image segmentation is still a difficult problem in practical applications, especially for traditional segmentation methods [2][3][4][5][6][7][8]. Traditional methods, like Canny edge detection [7], are simple and fast, but they always need further edge linking operation to produce continuous object boundaries [9].…”

Section: Introductionmentioning

confidence: 99%

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Image segmentation algorithm by piecewise smooth approximation

Wang

2012

J Image Video Proc

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We propose a novel image segmentation algorithm using piecewise smooth (PS) approximation to image. The proposed algorithm is inspired by four well-known active contour models, i.e., Chan and Vese' piecewise constant (PC)/smooth models, the region-scalable fitting model, and the local image fitting model. The four models share the same algorithm structure to find a PC/smooth approximation to the original image; the main difference is how to define the energy functional to be minimized and the PC/smooth function. In this article, pursuing the same idea we introduce different energy functional and PS function to search for the optimal PS approximation of the original image. The initial function with our model can be chosen as a constant function, which implies that the proposed algorithm is robust to initialization or even free of manual initialization. Experiments show that the proposed algorithm is very appropriate for a wider range of images, including images with intensity inhomogeneity and infrared ship images with low contrast and complex background.

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Section: (Ps) Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image segmentation algorithm by piecewise smooth approximation

Wang

2012

J Image Video Proc

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“…Reference [20] summarized the study of various techniques of brain tumor from MR images. A general scheme to segment images by a genetic algorithm is presented in [21].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of MRI Brain Tumor Segmentation Technique

Lakra¹

2015

IJCA

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Magnetic Resonance Imaging (MRI) is a powerful visualization tool that permits to acquire images of internal anatomy of human body in a secure and non-invasive manner. The important task in the diagnosis of brain tumor is to determine the exact location, orientation and area of the abnormal tissues. This paper presents a performance analysis of image segmentation techniques, viz., Genetic algorithm, KMeans Clustering and Fuzzy C-Means clustering for detection of brain tumor from brain MRI images. The performance evaluation of these techniques is carried out on the real time database on the basis of error percentage compared to ground truth. General TermsSegmentation algorithms, Brain tumor Keywords MRI brain tumor, segmentation, Genetic algorithm, K-means clustering and Fuzzy C-means clustering.

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“…The limitation of the proposed method is that it classifies the images upto three classes. Chabrier et al [4] have proposed an optimization based image segmentation by using GA. The criterion is optimized by using genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Images Containing Multiple Intensity Levels using Genetic Algorithms

Phulpagar¹,

Bichkar²

2015

IJCA

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Image segmentation technique is the process of separating the foreground objects of different intensities from the background. Several authors have proposed different methods for segmentation of images into two classes, each one having different quality of segmentation. Yu et al [1]. used a GA approach to segmentation of 2-D images into two classes. We have extended this method to segment the images into multiple classes or multiple intensity levels (Four, Eight, Twelve and Sixteen greay Levels). The proposed GA-based approach gives us good results for original synthetic images and noisy images containing rectangular, elliptical and irregular-objects using morphological operations upto sixteen classes. The results obtained give 81% to 100% pixel classification accuracy for different types of noise (Gaussian, Speckle, salt and pepper and Poisson) and high noise levels (SNR ranging between 2.18 dB to 6.97 dB). The segmentation results obtained by using proposed GA-based method are good as compared to standard image segmentation FCM method with the increasing noise density of salt and pepper and Gaussian noises.

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Optimization-Based Image Segmentation by Genetic Algorithms

Cited by 33 publications

References 25 publications

Image segmentation algorithm by piecewise smooth approximation

Image segmentation algorithm by piecewise smooth approximation

A Comparative Analysis of MRI Brain Tumor Segmentation Technique

Segmentation of Images Containing Multiple Intensity Levels using Genetic Algorithms

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