Robust noise region-based active contour model via local similarity factor for image segmentation

Niu, Sijie; Chen, Qiang; Sisternes, Luís de; Ji, Zexuan; Zhou, Ze; Rubin, Daniel L.

doi:10.1016/j.patcog.2016.07.022

Cited by 202 publications

(101 citation statements)

References 36 publications

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“…The number of white pixels represented the area of the water region, revealing the amount of water in the image. The black pixels represented the other regions. Evaluation of image processing performance: In this step, a region‐based method (Niu et al ., ) was employed to evaluate the performance of the automatic image processing. Differences were measured between the locations and sizes of the segmented regions.…”

Section: Methodsmentioning

confidence: 99%

Using automatic image processing to analyze visual artifacts created by students in scientific argumentation

Pei¹,

Xing²,

Lee³

2019

Brit J Educational Tech

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Science classes should support students' development of scientific argumentation. While previous studies have analyzed argumentative texts, they have overlooked the ways in which other types of representations, including images, affect the production of such texts. In addition, studies into the use of visual images in science education have offered mostly qualitative analyses. To fill these gaps in the research, this study used techniques of automated image processing to extract relevant information from student‐generated visual artifacts. Specifically, it used a series of image‐processing algorithms to automatically extract and quantify features of images created by students to serve as evidence in support of scientific arguments. Using various statistical analyses, we identified the relationships between the extracted features and the students' performance levels in constructing scientific arguments. The results revealed that the presence of water in a student's image correlated significantly with that student's claim and explanation scores and that the amount of water present in a student's image correlated significantly with that student's claim score, but not with their explanation score. These results indicate that automatic image processing can successfully identify image features that affect students' performance in scientific argumentation. Using this analysis as an example, we discuss implications for incorporating automated image processing into further research into scientific argumentation and the development of automated feedback.

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Section: Methodsmentioning

confidence: 99%

Using automatic image processing to analyze visual artifacts created by students in scientific argumentation

Pei¹,

Xing²,

Lee³

2019

Brit J Educational Tech

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“…For this purpose, various types of clustering and region prediction algorithms such as, level set method, 6 Active Contour Model (ACM), [7][8][9] fuzzy clustering 10 and k-means clustering 11,12 algorithms are proposed in the traditional works. Among these methods, then level set model performs the intensity and texturebased image segmentation processes by identifying the retinal optic disc in the image.…”

Section: Problem Identi¯cationmentioning

confidence: 99%

NDC-IVM: An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

Balakrishnan

2017

J. Innov. Opt. Health Sci.

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Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure (IOP), which damages the vision of eyes. So, detecting and classifying Glaucoma is an important and demanding task in recent days. For this purpose, some of the clustering and segmentation techniques are proposed in the existing works. But, it has some drawbacks that include ine±cient, inaccurate and estimates only the a®ected area. In order to solve these issues, a Neighboring Di®erential Clustering (NDC) -Intensity Variation Masking (IVM) are proposed in this paper. The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc. This work includes three stages such as, preprocessing, clustering and segmentation. At¯rst, the given retinal image is preprocessed by using the Gaussian Mask Updated (GMU) model for eliminating the noise and improving the quality of the image. Then, the cluster is formed by extracting the threshold and patterns with the help of NDC technique. In the segmentation stage, the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method. Here, the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identi¯cation. In experiments, the results of both existing and proposed techniques are evaluated in terms of sensitivity, speci¯city, accuracy, Hausdor® distance, Jaccard and dice metrics.

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“…In this paper, we propose a method for segmenting the wavefront of cortical spreading depressions (CSDs) in two-photon calcium images of mouse brains. This method is inspired by the region-based, noise-robust method proposed by Nui et al [9] and converts a level set method to a threshold-based method that is essentially based on fast marching [10]. The primary motivation of the proposed method is to conserve the shape of the segmentation boundary between iterations, preventing the final segmentation from being overly sensitive to the discontinuities in the CSD wavefront that occur due to noise and occlusion.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Cortical Spreading Depression Wavefronts Through Local Similarity Metric

Sluzewski

Tvrdík

Acton

2019

2019 IEEE International Conference on Image Processing (ICIP)

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In this paper, we present a novel region-based segmentation method for cortical spreading depressions in 2-photon microscopy images. Fluorescent microscopy has become an important tool in neuroscience, but segmentation approaches are challenged by the opaque properties and structures of brain tissue. These challenges are made more extreme when segmenting events such as cortical spreading depressions, where low signal-to-noise ratios and intensity inhomogeneity dominate images. The method we propose uses a local intensity similarity measure that takes advantage of normalized Euclidean and geodesic distance maps of the image. This method provides a smooth segmentation boundary which is robust to the noise and inhomogeneity within cortical spreading depression images. Experimental results yielded a DICE index of 0.9859, an increase of 6% over the current state-of-the-art, and a reduction of root mean square error by 79.9%.

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Robust noise region-based active contour model via local similarity factor for image segmentation

Cited by 202 publications

References 36 publications

Using automatic image processing to analyze visual artifacts created by students in scientific argumentation

Using automatic image processing to analyze visual artifacts created by students in scientific argumentation

NDC-IVM: An automatic segmentation of optic disc and cup region from medical images for glaucoma detection

Segmentation of Cortical Spreading Depression Wavefronts Through Local Similarity Metric

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