Oriented Relative Fuzzy Connectedness: Theory, Algorithms, and Applications in Image Segmentation

Bejar, Hans Harley Ccacyahuillca; Miranda, Paulo A. V.

doi:10.1109/sibgrapi.2014.38

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…Other result of our work was the design of more adaptive and flexible connectivity functions, with the use of dynamic weights, that allow better handling of images with strong inhomogeneity . The theoretical foundation proposed in this work has also allowed new achievements that were recently published, such as [Braz and Miranda 2014] and [Bejar and Miranda 2014].…”

Section: Resultsmentioning

confidence: 87%

Image Segmentation by Image Foresting Transform with Boundary Polarity and Shape Constraints

Mansilla¹,

Miranda²

2015

Anais Do XXVIII Concurso De Teses E Dissertações (CTD 2015)

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Image segmentation, such as to extract an object from a background, is very useful for medical and biological image analysis. In this work, we propose new segmentation methods for interactive segmentation of multidimensional images, based on the Image Foresting Transform (IFT), by exploiting for the first time non-smooth connectivity functions (NSCF) with a strong theoretical background. The new algorithms provide global optimum solutions according to an energy function of graph cut, subject to high-level boundary constraints (polarity and shape). Our experimental results indicate substantial improvements in accuracy in relation to other state-of-the-art methods, using medical images by allowing the customization of the segmentation to a given target object.

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Section: Resultsmentioning

confidence: 87%

Image Segmentation by Image Foresting Transform with Boundary Polarity and Shape Constraints

Mansilla¹,

Miranda²

2015

Anais Do XXVIII Concurso De Teses E Dissertações (CTD 2015)

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“…Most segmentation techniques (such as thresholding methods [4], edge-based methods [5], region-based methods [6], and hybrid methods [7], etc.) for gray images can also be extended to segmentation of color images.…”

Section: Introductionmentioning

confidence: 99%

Fusion Segmentation Method Based on Fuzzy Theory for Color Images

Zhao

Huang

Zhang

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The image segmentation method based on two-dimensional histogram segments the image according to the thresholds of the intensity of the target pixel and the average intensity of its neighborhood. This method is essentially a hard-decision method. Due to the uncertainties when labeling the pixels around the threshold, the hard-decision method can easily get the wrong segmentation result. Therefore, a fusion segmentation method based on fuzzy theory is proposed in this paper. We use membership function to model the uncertainties on each color channel of the color image. Then, we segment the color image according to the fuzzy reasoning. The experiment results show that our proposed method can get better segmentation results both on the natural scene images and optical remote sensing images compared with the traditional thresholding method. The fusion method in this paper can provide new ideas for the information extraction of optical remote sensing images and polarization SAR images.

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“…A short version of this work was published in a conference paper [31]. Here, the proposed method, named oriented relative fuzzy connectedness (ORFC), is presented in more details, including experiments involving large three-dimensional datasets, and showing the running time curves.…”

Section: Introductionmentioning

confidence: 99%

Oriented relative fuzzy connectedness: theory, algorithms, and its applications in hybrid image segmentation methods

Bejar

Miranda

2015

J Image Video Proc.

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Anatomical structures and tissues are often hard to be segmented in medical images due to their poorly defined boundaries, i.e., low contrast in relation to other nearby false boundaries. The specification of the boundary polarity can help alleviate a part of this problem. In this work, we discuss how to incorporate this property in the relative fuzzy connectedness (RFC) framework. We include a theoretical proof of the optimality of the new algorithm, named oriented relative fuzzy connectedness (ORFC), in terms of an oriented energy function subject to the seed constraints, and show its usage to devise powerful hybrid image segmentation methods. The methods are evaluated using medical images of MRI and CT of the human brain and thoracic studies.

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Oriented Relative Fuzzy Connectedness: Theory, Algorithms, and Applications in Image Segmentation

Cited by 7 publications

References 28 publications

Image Segmentation by Image Foresting Transform with Boundary Polarity and Shape Constraints

Image Segmentation by Image Foresting Transform with Boundary Polarity and Shape Constraints

Fusion Segmentation Method Based on Fuzzy Theory for Color Images

Oriented relative fuzzy connectedness: theory, algorithms, and its applications in hybrid image segmentation methods

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