A Review on Graph Based Segmentation

Camilus, K. Santle; Govindan, V. K.

doi:10.5815/ijigsp.2012.05.01

Cited by 54 publications

(26 citation statements)

References 115 publications

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“…Graph based model [5] Min-cut/max-flow, Normalized cut, Grab cut, Random Walk Here we present a framework for transforming scale-space features constructed on color planes to generate a nucleus probability image such that gray level value of a pixel is proportionate to the probability that the pixel belongs to nuclear region. This probability plane enhances speed and robustness of the nucleus localization and segmentation stages as it transforms the non-linear features at multiple scales into prediction of presence of nucleus at particular pixel.…”

Section: Category Subcategorymentioning

confidence: 99%

Learning scale-space representation of nucleus for accurate localization and segmentation of epithelial squamous nuclei in cervical smears

Karri

Garud

Sheet

et al. 2014

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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Computer vision systems are being introduced in pre-screening of cervical cytopathology slides to identify samples that require study by cytopathologists. These systems work on the principle of imaging and analysis of cytology features in general and nuclear features in particular. Thus accurate localization and segmentation of the nuclei is crucial for the systems. Though several methods have been conceptualized, developed and employed to achieve the tasks of localization and segmentation of nuclei in cytology images, most fail to localize nuclei with opened up chromatin. This paper presents a machine learning approach based framework for accurate localization and segmentation of nuclei. The approach uses the random forest model to learn complete scale-space representation of the nuclear chromatin distribution in green and color saturation channels. Based on the multi scale features of an unknown image this model can predict an image such that gray level value of a pixel is proportionate to the probability that the pixel belongs to nuclear region. This predicted image then can be used for accurate localization and segmentation of the nuclei by random walks approach. Accuracy of the system has been tested on a publicly available dataset images and was found to be approximately 97%.

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Section: Category Subcategorymentioning

confidence: 99%

Learning scale-space representation of nucleus for accurate localization and segmentation of epithelial squamous nuclei in cervical smears

Karri

Garud

Sheet

et al. 2014

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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“…District developing is a way to deal with an image segmentation in which neighboring pixels are analyzed and joined to a locale class of no edges are identified. Area developing methodology is the inverse of the split and unions approach [20]: a) An underlying arrangement of little territories is interactively converged by similitude requirements. b) Start by picking a self-assertive seed pixel and contrast it and neighboring pixels.…”

Section: Region Growingmentioning

confidence: 99%

Computational Approach to Image Segmentation Analysis

Gourav¹,

Sharma²,

Singh³

2017

IJMECS

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Abstract-Image division refers to the way toward dividing an advanced picture into various portions. Image division says to a parcel of an image into various divisions that are homogeneous or comparable. The objective of the division is to Simplify or potentially changes the portrayal of an image into something that is more important and simpler to dissect. Advancement of precise image division different image division strategies is utilized to take care of a particular issue. The motivation behind this survey is to give an overview of various image division methods. These methods are sorted into four sorts: an) Edge based division b) Threshold Segmentation c) Clustering-based division D) Region-based division. This survey tended to different image division methods, their correlation and presents the issues identified with those procedures.

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“…Within this context, computational methods that make use of graphs as a basic element of study have played a key role in getting innovative solutions in various fields of knowledge, in particular in problem areas of computer vision and information visualization. Recent examples of applications that employ graph analysis in their processing pipelines are easily found in the literature such as: segmentation and classification of images via large-scale graphs [1,2], rearrangement and removal of overlaps in visual layouts, visualization and high-dimensional data clustering [3,4], among others. Thus, the modern theory of graphs is seen today as an indispensable tool to explore, analyze, and process large volumes of information, especially when it comes to digital images and high-dimensional data visualization, in view of its strong theoretical and mathematical support [5].…”

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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A Review on Graph Based Segmentation

Cited by 54 publications

References 115 publications

Learning scale-space representation of nucleus for accurate localization and segmentation of epithelial squamous nuclei in cervical smears

Learning scale-space representation of nucleus for accurate localization and segmentation of epithelial squamous nuclei in cervical smears

Computational Approach to Image Segmentation Analysis

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

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