Automatic medical image segmentation based on EPGV-Snake

Bakir, Houda; Charfi, M.

doi:10.1109/ssd.2009.4956799

Cited by 7 publications

(2 citation statements)

References 10 publications

(8 reference statements)

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“…Various approaches have been proposed for SAR sea ice segmentation, such as image thresholding [2], data clustering [3], watershed and its variations [4,10], edge-based techniques [5], feature fusion and learning [6], region growing [1,[7][8][9] and kernel graph-cut [11]. For threshold-based approaches, local/global thresholds are derived from histogram analysis for image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Effective SAR sea ice image segmentation and touch floe separation using a combined multi-stage approach

Ren

Hwang

Murray

et al. 2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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This version is available at https://strathprints.strath.ac.uk/54441/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. ABSTRACT Accurate sea-ice segmentation from satellite synthetic aperture radar (SAR) images plays an important role for understanding the interactions between sea-ice, ocean and atmosphere in the Arctic. Processing sea-ice SAR images are challenging due to poor spatial resolution and severe speckle noise. In this paper, we present a multi-stage method for the sea-ice SAR image segmentation, which includes edge-preserved filtering for preprocessing, k-means clustering for segmentation and conditional morphology filtering for post-processing. As such, the effect of noise has been suppressed and the undersegmented regions are successfully corrected. EFFECTIVE SAR SEA ICE IMAGE SEGMENTATION AND TOUCH FLOE SEPARATION USING A COMBINED MULTI-STAGE APPROACH

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Section: Introductionmentioning

confidence: 99%

Effective SAR sea ice image segmentation and touch floe separation using a combined multi-stage approach

Ren

Hwang

Murray

et al. 2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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“…Different segmentation algorithms such as thresholding [2], clustering [3], watershed [4], edge detection-based methods [5], feature fusion [6] and region growing techniques [1] [7] [8] [9] have been used in past research works for feature extraction. These segmentation techniques have their strengths but also have their drawbacks.…”

Section: Introductionmentioning

confidence: 99%

SAR Sea Ice Image Segmentation Using Watershed with Intensity-Based Region Merging

Ijitona

Ren

Hwang

2014

2014 IEEE International Conference on Computer and Information Technology

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A new approach for Synthetic Aperture Radar (SAR) based sea ice image segmentation for the retrieval of floe size distribution (FSD) is proposed. This method consists of three stages. The first stage involves the pre-processing of the SAR image to reduce the speckle noise in the image by median filtering. The second stage involves an initial segmentation of the image using the Watershed transform. The third stage involves a region merging process based on the difference function of the mean intensity of adjacent regions. Adjacent regions are defined by the region adjacency graph based on a minimum distance of 1 pixel between two adjacent regions. A threshold value is usually set for the merging process, if the difference in mean intensity of adjacent regions is less than the threshold, the adjacent regions will be merged. Experimental results have shown the efficacy of the proposed method in effective segmentation of SAR ice images.

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A study on Rényi entropy and Shannon entropy of image segmentation based on finite multivariate skew t distribution mixture model

2021

Math Methods in App Sciences

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Image segmentation technology has been widely used in various business and social fields. In recent years, more and more scholars have studied the theories in this field. Many models and methods have good effects in image segmentation. However, as people's demand for image is getting higher and higher, people often face images with complex structure and multimode, which makes us need to study and analyze the theory of image segmentation more deeply. In this paper, we study the Rényi entropy and Shannon entropy of finite multivariate skew t mixture distribution (this distribution was proposed based on Sahu and Branco (2003; https://doi.org/10.2307/3316064), and it has better properties and wider application range than the traditional skew t distribution). In addition to the specific calculation results of the two kinds of entropy, we use Hölder inequality and polynomial theorem to obtain the upper bound and lower bound of the two kinds of entropy of finite multivariate skew t mixture distribution.

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Automatic medical image segmentation based on EPGV-Snake

Cited by 7 publications

References 10 publications

Effective SAR sea ice image segmentation and touch floe separation using a combined multi-stage approach

Effective SAR sea ice image segmentation and touch floe separation using a combined multi-stage approach

SAR Sea Ice Image Segmentation Using Watershed with Intensity-Based Region Merging

A study on Rényi entropy and Shannon entropy of image segmentation based on finite multivariate skew t distribution mixture model

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