Region growing and region merging image segmentation

Ikonomatakis, N.; Plataniotis, Konstantinos N.; Zervakis, M.; Venetsanopoulos, A.N.

doi:10.1109/icdsp.1997.628077

Cited by 34 publications

(18 citation statements)

References 6 publications

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“…Assuming that the gravitational mass is equal to the inertia mass, the mass M i (t) is updated by (10), (11), (12) and (14):…”

Section: Gravitational Search Algorithmmentioning

confidence: 99%

“…According to reviews presented in previous evaluation studies, there are many different methods and techniques used for image segmentation. They are classified into the following categories: region-based methods [12,16,42], edge-based methods [18,25,35], methods based on the combination of region and edge [15], mathematical morphology-based methods [50,55], fuzzy theory-based methods [29,46], neural network-based methods [3, 5-7, 14, 22, 36, 41, 45, 48, 49, 53], and support vector machine-based methods [47,51,56].…”

Section: Introductionmentioning

confidence: 99%

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A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping

Han

Xiong

2015

Appl Intell

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This paper proposes a novel image segmentation method based on BP neural network, which is optimized by an enhanced Gravitational Search Algorithm (GSA). GSA is a novel heuristic optimization algorithm based on the law of gravity and mass interactions. It has been proven that the GSA has good ability to search for the global optimum, but it suffers from the premature convergence due to the rapid reduction of diversity. This work introduces a cat chaotic mapping into the steps of population initialization and iterative stage of the original GSA, which forms a new algorithm called CCMGSA. Then the CCMGSA is employed to optimize BP neural networks, which forms a combination method called CCMGSA-BP and we use it for image segmentation. To verify the efficiency of this method, the visual and performance experiments are done. The visual results using our proposed method are compared with those using other segmentation methods including an improved k-means clustering algorithm (I-K-means), a hybrid region merging method (H-Region-merging), and manual segmentation. The comparison results show that the proposed method can get good segmentation results on grayscale images with specific characteristics. And we compare the performance of our proposed method with those of IGSA-BP, CLPSO-BP and RGA-BP for image segmentation. The results indicate that the CCMGSA-BP shows

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“…Assuming that the gravitational mass is equal to the inertia mass, the mass M i (t) is updated by (10), (11), (12) and (14):…”

Section: Gravitational Search Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping

Han

Xiong

2015

Appl Intell

View full text Add to dashboard Cite

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“…The thresholding approaches are based on the assumption that clusters in the histogram correspond to either background or objects of interest that can be extracted by separating these clusters [1][2][3][4]. The boundary-based methods are based on the assumption that the pixel properties, including intensity, color, and texture, should change abruptly between different regions [5][6][7], whereas the region-based methods assume that neighboring pixels within the same region have similar pixel properties [8][9][10]. The clusteringbased methods are based on the statistic models [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Color image segmentation using nonparametric mixture models with multivariate orthogonal polynomials

Liu

Song

Chen

et al. 2011

Neural Comput & Applic

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To solve the problem of over-reliance on a priori assumptions of the parametric methods for finite mixture models and the problem that monic Chebyshev orthogonal polynomials can only process the gray images, a segmentation method of mixture models of multivariate Chebyshev orthogonal polynomials for color image was proposed in this paper. First, the multivariate Chebyshev orthogonal polynomials are derived by the Fourier analysis and tensor product theory, and the nonparametric mixture models of multivariate orthogonal polynomials are proposed. And the mean integrated squared error is used to estimate the smoothing parameter for each model. Second, to resolve the problem of the estimation of the number of density mixture components, the stochastic nonparametric expectation maximum algorithm is used to estimate the orthogonal polynomial coefficient and weight of each model. This method does not require any prior assumptions on the models, and it can effectively overcome the problem of model mismatch. Experimental performance on real benchmark images shows that the proposed method performs well in a wide variety of empirical situations.

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“…The only requirement is to make sure that the structure is disconnected from all segmented structures. The selection was performed using a region growing tool, by first placing a seed in the structure of interest and letting the algorithm segment all voxels belonging to the structure [19]. The result of the binary segmentation is often postprocessed using morphological operations, which are described in the following section.…”

Section: Segmentation By Thresholdingmentioning

confidence: 99%

Image Analysis and Visualization of the Human Mastoid Air Cell System

Cros¹

2015

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Legend: CO cochlea, EC ear canal, FN facial nerve, MACS mastoid air cell system, MC micro-channels, OSS ossicles (manubrium of the incus), TM tympanic membrane, TS trabecular spaces, TYM tympanum (middle ear). Image Analysis and Visualization of the Human Mastoid Air Cell System © 2015 Olivier Cros Department of Biomedical EngineeringLinköping University, Sweden ISBN 978-91-7685-941-4 ISSN 0280-7971 LIU-TEK-LIC-1975:N AbstractFrom an engineering background, it is often believed that the human anatomy has already been fully described. Radiology has greatly contributed to understand the inside of the human body without surgical intervention. Despite great advances in clinical CT scanning, image quality is still related to a limited amount X-ray exposure for the patient safety. This limitation prevents fine anatomical structures to be visible and, more importantly, to be detected. Where such modality is of great advantage for screening patients, extracting parameters like surface area and volume implies the bone structure to be large enough in relation to the scan resolution.The mastoid, located in the temporal bone, houses an air cell system whose cells have a variation in size that can go far below current conventional clinical CT scanner resolution. Therefore, the mastoid air cell system is only partially represented on a CT scan. Any statistical analysis will be biased towards air cells of smaller size. To allow a complete representation of the mastoid air cell system, a micro-CT scanner is more adequate. Micro-CT scanning uses approximately the same amount of X-rays but for a much longer exposure time compared to what is normally allowed for patients. Human temporal bone specimens are therefore necessary when using such scanning method. Where the conventional clinical CT scanner lacks level of minutes details, micro-CT scanning provides an overwhelming amount of fine details.Prior to any image analysis of medical data, visualization of the data is often needed to learn how to extract the structures of interest for further processing. Visualization of micro-CT scans is of no exception. Due to the high resolution nature of the data, visualization of such data not only requires modern and powerful computers, but also necessitates a tremendous amount of time to adjust the hiding of irrelevant structures, to find the correct orientation, while emphasising the structure of interest. Once the quality of the data has been assessed, and a strategy for the image processing has been decided, the image processing can start, to in turn extract metrics such as the surface area or volume and draw statistics from it. The temporal bone being one of the most complex in the human body, visualization of micro-CT scanning of this bone awakens the curiosity of the experimenter, especially with the correct visualization settings.This thesis first presents a statistical analysis determining the surface area to volume ratio of the mastoid air cell system of human temporal bone, from micro-CT scanning using methods previously appl...

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Region growing and region merging image segmentation

Cited by 34 publications

References 6 publications

A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping

A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping

Color image segmentation using nonparametric mixture models with multivariate orthogonal polynomials

Image Analysis and Visualization of the Human Mastoid Air Cell System

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