Image Segmentation Fusion Using General Ensemble Clustering Methods

Franek, Lucas; Abdala, Daniel Duarte; Vega-Pons, Sandro; Jiang, Xiaoyi

doi:10.1007/978-3-642-19282-1_30

Cited by 38 publications

(40 citation statements)

References 18 publications

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“…Then, the segmentation procedure is run for all the M parameter settings and the generalized median of the M segmentation results is computed. The rationale here is that in line with the ensemble paradigm, the median result tends to be a good one within the explored parameter subspace, as already successfully demonstrated for 2D contour detection [4] and region segmentation [21,22].…”

Section: Motivationmentioning

confidence: 74%

“…In our case an ensemble of K surfaces is averaged to achieve an "optimal" segmentation. This principle has been successfully validated for 2D contour detection [4] and image segmentation [21,22] before. In the current work this ensemble approach is demonstrated for parameter space exploration in the context of 3D surface segmentation.…”

Section: Discussionmentioning

confidence: 98%

“…The median concept has been concretized to many domains including vectors [8], tensors [9], rotations [10], strings [11], graphs [12][13][14], clusterings [15,16], tree-like shapes [17], time series [18], ranking (permutation) [19], images (for atlas construction) [20], and image segmentations [21,22]. In [4] the 2D median contour problem is investigated.…”

Section: Generalized Median Conceptmentioning

confidence: 99%

See 2 more Smart Citations

Exact solution to median surface problem using 3D graph search and application to parameter space exploration

Jiang²,

Zheng

et al. 2015

Pattern Recognition

Self Cite

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Section: Motivationmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 98%

Section: Generalized Median Conceptmentioning

confidence: 99%

See 1 more Smart Citation

Exact solution to median surface problem using 3D graph search and application to parameter space exploration

Jiang²,

Zheng

et al. 2015

Pattern Recognition

Self Cite

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“…Ensemble methods are effective methods that are used in different areas of image processing such as segmentation and classification [22,23]. Although an individual method can be the best method for the dataset A, but it can be the worst method for the dataset B.…”

Section: Background Of Ensemble Methodsmentioning

confidence: 99%

Supervised segmentation of MRI brain images using combination of multiple classifiers

Ahmadvand

Sharififar

Daliri

2015

Australas Phys Eng Sci Med

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Segmentation of different tissues is one of the initial and most critical tasks in different aspects of medical image processing. Manual segmentation of brain images resulted from magnetic resonance imaging is time consuming, so automatic image segmentation is widely used in this area. Ensemble based algorithms are very reliable and generalized methods for classification. In this paper, a supervised method named dynamic classifier selection-dynamic local training local tanimoto index, which is a member of combination of multiple classifiers (CMCs) methods is proposed. The proposed method uses dynamic local training sets instead of a full statics one and also it change the classifier rank criterion properly for brain tissue classification. Selection policy for combining the different decisions is implemented here and the K-nearest neighbor algorithm is used to find the best local classifier. Experimental results show that the proposed method can classify the real datasets of the internet brain segmentation repository better than all single classifiers in ensemble and produces significantly improvement on other CMCs methods.

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“…Following a machine learning vision, some methods handled segmentation fusion via clustering ensemble [1]; a comparative study can be found in [6]. It was also proposed to interpret the information gathered at each pixel in the various segmentation maps as feature vectors then involved in optimization procedures.…”

Section: Segmentation Fusionmentioning

confidence: 99%

Multi-image Segmentation: A Collaborative Approach Based on Binary Partition Trees

Randrianasoa

Kurtz

Desjardin

et al. 2015

Lecture Notes in Computer Science

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Abstract. Image segmentation is generally performed in a "one image, one algorithm" paradigm. However, it is sometimes required to consider several images of a same scene, or to carry out several (or several occurrences of a same) algorithm(s) to fully capture relevant information. To solve the induced segmentation fusion issues, various strategies have been already investigated for allowing a consensus between several segmentation outputs. This article proposes a contribution to segmentation fusion, with a specific focus on the "n images" part of the paradigm. Its main originality is to act on the segmentation research space, i.e., to work at an earlier stage than standard segmentation fusion approaches. To this end, an algorithmic framework is developed to build a binary partition tree in a collaborative fashion, from several images, thus allowing to obtain a unified hierarchical segmentation space. This framework is, in particular, designed to embed consensus policies inherited from the machine learning domain. Application examples proposed in remote sensing emphasise the potential usefulness of our approach for satellite image processing.

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Image Segmentation Fusion Using General Ensemble Clustering Methods

Cited by 38 publications

References 18 publications

Exact solution to median surface problem using 3D graph search and application to parameter space exploration

Exact solution to median surface problem using 3D graph search and application to parameter space exploration

Supervised segmentation of MRI brain images using combination of multiple classifiers

Multi-image Segmentation: A Collaborative Approach Based on Binary Partition Trees

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