A survey on image segmentation

Fu, King-Sun; Mui, J. K.

doi:10.1016/0031-3203(81)90028-5

Cited by 1,030 publications

(360 citation statements)

References 52 publications

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“…Choice of threshold selection method is the central step in the design of any adaptive thresholding algorithm, and many approaches have been put forward as suitable for use in the binarisation of line drawing images [1][2][3][4][5][6][7][8][9]. Most work from a grey level histogram of some image area, assuming the grey level distribution to comprise two normally distributed components; one centred on plain (white) paper and the other on inked (black) regions.…”

Section: Threshold Selection For Line Drawing Interpretationmentioning

confidence: 99%

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Thresholding Images of Line Drawings with Hysteresis

Pridmore

2002

Lecture Notes in Computer Science

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Abstract. John Canny's two-level thresholding with hysteresis is now a de facto standard in edge detection. The method consistently outperforms single threshold techniques and is simple to use, but relies on edge detection operators' ability to produce thin input data. To date, thresholding with hysteresis has only been applicable to thick data such as line drawings by top-down systems using a priori knowledge of image content to specify the pixel tracks to be considered. We present, and discuss within the context of line drawing interpretation, a morphological implementation of thresholding with hysteresis that requires only simple thresholding and idempotent dilation and which is applicable to thick data. Initial experiments with the technique are described. A more complete evaluation and formal comparison of the performance of the proposed algorithm with alternative line drawing binarisation methods is underway and will be the subject of a future report.

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Section: Threshold Selection For Line Drawing Interpretationmentioning

confidence: 99%

“…Many thresholding schemes have been proposed [1][2][3][4][5][6], Static, adaptive thresholding methods are the most commonly used in line drawing interpretation. A threshold value is defined as a function of (only) the grey level distribution of a local image region.…”

Section: Introductionmentioning

confidence: 99%

Thresholding Images of Line Drawings with Hysteresis

Pridmore

2002

Lecture Notes in Computer Science

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“…Image segmentation is one of the most important steps in the analysis of preprocessed patient image data and can be helpful in diagnosis, treatment planning, and treatment delivery, among other applications [1]- [3]. It is the process of labeling each pixel in a medical image dataset to indicate its tissue type or anatomical structure.…”

Section: Introductionmentioning

confidence: 99%

“…There are several proposed approaches in the literature for image segmentation and extraction of objects (tumor, vessel, bone, etc.). Segmentation techniques can be categorized into four classes: the threshold-based, edge or boundary-based, region-based and model-based techniques [1], [4]- [6]. The threshold technique is the most intuitive.…”

Section: Introductionmentioning

confidence: 99%

Medical Image Segmentation Using Level Set Method with a New Hybrid Speed Function Based on Boundary and Region Segmentation

Park

Cho

2012

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents a new hybrid speed function needed to perform image segmentation within the level-set framework. The proposed speed function uses both the boundary and region information of objects to achieve robust and accurate segmentation results. This speed function provides a general form that incorporates the robust alignment term as a part of the driving force for the proper edge direction of an active contour, an active region term derived from the region partition scheme, and the smoothing term for regularization. First, we use an external force for active contours as the Gradient Vector Flow field. This is computed as the diffusion of gradient vectors of a gray level edge map derived from an image. Second, we partition the image domain by progressively fitting statistical models to the intensity of each region. Here we adopt two Gaussian distributions to model the intensity distribution of the inside and outside of the evolving curve partitioning the image domain. Third, we use the active contour model that has the computation of geodesics or minimal distance curves, which allows stable boundary detection when the model's gradients suffer from large variations including gaps or noise. Finally, we test the accuracy and robustness of the proposed method for various medical images. Experimental results show that our method can properly segment low contrast, complex images. key words: image segmentation, geometric deformable model, level set method, gradient vector flow

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“…In the last few decades, many researchers have focused their work on algorithms and techniques that look for the main regions that compose an image [2][3][4]. This means that the state-of-the-art on image segmentation involves large amounts of methodologies and also many taxonomies on this topic [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised colour image segmentation by low-level perceptual grouping

Martínez-Usó

Pla

García-Sevilla

2011

Pattern Anal Applic

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This paper proposes a new unsupervised approach for colour image segmentation. A hierarchy of image partitions is created on the basis of a function that merges spatially connected regions according to primary perceptual criteria. Likewise, a global function that measures the goodness of each defined partition is used to choose the best low-level perceptual grouping in the hierarchy. Contributions also include a comparative study with five unsupervised colour image segmentation techniques. These techniques have been frequently used as a reference in other comparisons. The results obtained by each method have been systematically evaluated using four well-known unsupervised measures for judging the segmentation quality. Our methodology has globally shown the best performance, obtaining better results in three out of four of these segmentation quality measures. Experiments will also show that our proposal finds low-level perceptual solutions that are highly correlated with the ones provided by humans.

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A survey on image segmentation

Cited by 1,030 publications

References 52 publications

Thresholding Images of Line Drawings with Hysteresis

Thresholding Images of Line Drawings with Hysteresis

Medical Image Segmentation Using Level Set Method with a New Hybrid Speed Function Based on Boundary and Region Segmentation

Unsupervised colour image segmentation by low-level perceptual grouping

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