Mahalanobis distance based on fuzzy clustering algorithm for image segmentation

Zhao, Xuemei; Li, Yu; Zhao, Quanhua

doi:10.1016/j.dsp.2015.04.009

Cited by 67 publications

(37 citation statements)

References 26 publications

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“…The Mahalanobis distance in statistics [8] is a generalized, unitless and scale invariant similarity measure that takes correlations in the dataset into account. It is popularly used for anomaly detection and clustering [24,25]. Our goal is to define a similarity measure to compare how close any two cryo-EM images are, given the CTF-affected, noisy observations for a pair of images from possibly different defocus groups, say Y i and Y j in eq.…”

Section: Anisotropic Affinitymentioning

confidence: 99%

Mahalanobis distance for class averaging of cryo-EM images

Bhamre

Zhao

Singer

2017

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

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Single particle reconstruction (SPR) from cryo-electron microscopy (EM) is a technique in which the 3D structure of a molecule needs to be determined from its contrast transfer function (CTF) affected, noisy 2D projection images taken at unknown viewing directions. One of the main challenges in cryo-EM is the typically low signal to noise ratio (SNR) of the acquired images. 2D classification of images, followed by class averaging, improves the SNR of the resulting averages, and is used for selecting particles from micrographs and for inspecting the particle images. We introduce a new affinity measure, akin to the Mahalanobis distance, to compare cryo-EM images belonging to different defocus groups. The new similarity measure is employed to detect similar images, thereby leading to an improved algorithm for class averaging. We evaluate the performance of the proposed class averaging procedure on synthetic datasets, obtaining state of the art classification.

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Section: Anisotropic Affinitymentioning

confidence: 99%

Mahalanobis distance for class averaging of cryo-EM images

Bhamre

Zhao

Singer

2017

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

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“…The second method is a variation of the first one. It uses Mahalanobis distances (FCmahal) rather than Euclidean distances [13]. The third method is a classical hierarchical clustering approach that groups samples over a variety of distances by building a binary tree (BiTree).…”

Section: B Experimental Studymentioning

confidence: 99%

Similarity based hierarchical clustering of physiological parameters for the identification of health states - a feasibility study

Schrumpf

Bausch

Stürm

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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This paper introduces a new unsupervised method for the clustering of physiological data into health states based on their similarity. We propose an iterative hierarchical clustering approach that combines health states according to a similarity constraint to new arbitrary health states. We applied our method to experimental data in which the physical strain of subjects was systematically varied. We derived health states based on parameters extracted from ECG data. The occurrence of health states shows a high temporal correlation to the experimental phases of the physical exercise. We compared our method to other clustering algorithms and found a significantly higher accuracy with respect to the identification of health states.

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“…Remote sensing image segmentation is a process of partitioning image domain into several meaningful regions and serves as a bridge between remote sensing image and high-level information processing [1,2]. Remote sensing image is a type of data that records spectral information of the earth's surface from a spaceborne sensor.…”

Section: Introductionmentioning

confidence: 99%

Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree

Lin

Liu

2020

Remote Sensing

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With finer spatial scale, high-resolution images provide complex, spatial, and massive information on the earth's surface, which brings new challenges to remote sensing segmentation methods. In view of these challenges, finding a more effective segmentation model and parallel processing method is crucial to improve the segmentation accuracy and process efficiency of large-scale high-resolution images. To this end, this study proposed a minimum spanning tree (MST) model integrated into a regional-based parallel segmentation method. First, an image was decomposed into several blocks by regular tessellation. The corresponding homogeneous regions were obtained using the minimum heterogeneity rule (MHR) partitioning technique in a multicore parallel processing mode, and the initial segmentation results were obtained by the parallel block merging method. On this basis, a regionalized fuzzy c-means (FCM) method based on master-slave parallel mode was proposed to achieve fast and optimal segmentation. The proposed segmentation approach was tested on high-resolution images. The results from the qualitative assessment, quantitative evaluation, and parallel analysis verified the feasibility and validity of the proposed method.Remote Sens. 2020, 12, 783 2 of 29 high-resolution images. The abundant spatial and geometric information, which determines the spatial and geometric models, must be taken into account in building the segmentation model.Having massive data makes data the decomposition-based parallel algorithm a realistic choice to solve the complexity of computing time, and this approach has become one of the most effective ways to expand and optimize existing segmentation methods to address the massive remote sensing image processing requirements [11]. Many researchers have proposed various parallel image segmentation methods for large-scale high-resolution images [12][13][14]. For example, Xing et al. [15] proposed a parallel remote sensing image segmentation method combined with decomposition/merging mode and k-means algorithms based on geospatial cyberinfrastructure (GCI). Under this mode, the large-scale image is decomposed into several blocks, which are parallel-divided into regions. The merging process restores the block segmentation outcome to the whole image segmentation result and considers the merging of boundary region between blocks, which solves the problem of over-segmentation caused by decomposition. However, the k-means segmentation algorithm only considers spectral information and not the spatial and geometric information, resulting in difficulties in solving the complexity of high-resolution images.As a branch of mathematics, graph theory uses the graph as the primary study object and has the ability to describe the internal relations of vertex sets [16][17][18][19]. The remote sensing image representation model is built by mapping the coordinates and spectral information of the pixels into vertices. The adjacency relationship of the pixels is regarded as the connected edges between the vertices, whi...

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Mahalanobis distance based on fuzzy clustering algorithm for image segmentation

Cited by 67 publications

References 26 publications

Mahalanobis distance for class averaging of cryo-EM images

Mahalanobis distance for class averaging of cryo-EM images

Similarity based hierarchical clustering of physiological parameters for the identification of health states - a feasibility study

Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree

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