Adaptive fuzzy moving K-means clustering algorithm for image segmentation

Isa, Nor Ashidi Mat; Salamah, Samy A.; Ngah, Umi Kalthum

doi:10.1109/tce.2009.5373781

Cited by 112 publications

(14 citation statements)

References 19 publications

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“…Quantitative analysis is a numerically oriented procedure to figure out the performance of algorithms without any human error. The Mean Square Error (MSE) [14,15] is significant metric to validate the quality of image. It measures the square error between pixels of the original and the resultant images.…”

Section: Experimental Results Qualitative Analysismentioning

confidence: 99%

Multiple Feature Fuzzy c-means Clustering Algorithm for Segmentation of Microarray Images

Harikiran

Lakshmi

Kumar

2015

IJECE

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<p>Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation and intensity extraction are the three important steps in microarray image analysis. Clustering algorithms have been used for microarray image segmentation with an advantage that they are not restricted to a particular shape and size for the spots. Instead of using single feature clustering algorithm, this paper presents multiple feature clustering algorithm with three features for each pixel such as pixel intensity, distance from the center of the spot and median of surrounding pixels. In all the traditional clustering algorithms, number of clusters and initial centroids are randomly selected and often specified by the user. In this paper, a new algorithm based on empirical mode decomposition algorithm for the histogram of the input image will generate the number of clusters and initial centroids required for clustering. It overcomes the shortage of random initialization in traditional clustering and achieves high computational speed by reducing the number of iterations. The experimental results show that multiple feature Fuzzy C-means has segmented the microarray image more accurately than other algorithms.</p>

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Section: Experimental Results Qualitative Analysismentioning

confidence: 99%

Multiple Feature Fuzzy c-means Clustering Algorithm for Segmentation of Microarray Images

Harikiran

Lakshmi

Kumar

2015

IJECE

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“…The disadvantage of the traditional k-means algorithm is that the best number of clusters is hard to choose. Moreover, it is susceptible to outliers [38,39]. However, in actual line loss work, unqualified data may be produced in the process of collecting, transferring, and storing data.…”

Section: Distribution Network Classification Based On Dbscanmentioning

confidence: 99%

Research on Predicting Line Loss Rate in Low Voltage Distribution Network Based on Gradient Boosting Decision Tree

Yao

Zhu

et al. 2019

Energies

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Line loss rate plays an essential role in evaluating the economic operation of power systems. However, in a low voltage (LV) distribution network, calculating line loss rate has become more cumbersome due to poor configuration of the measuring and detecting device, the difficulty in collecting operational data, and the excessive number of components and nodes. Most previous studies mainly focused on the approaches to calculate or predict line loss rate, but rarely involve the evaluation of the prediction results. In this paper, we propose an approach based on a gradient boosting decision tree (GBDT), to predict line loss rate. GBDT inherits the advantages of both statistical models and AI approaches, and can identify the complex and nonlinear relationship while computing the relative importance among variables. An empirical study on a data set in a city demonstrates that our proposed approach performs well in predicting line loss rate, given a large number of unlabeled examples. Experiments and analysis also confirmed the effectiveness of our proposed approach in anomaly detection and practical project management.

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“…The segmentation result looks appealing, but the search algorithm is over‐complicated, and when the grey difference between the target and background is not obvious, it frequently leads to error‐segmentation problem. K ‐means clustering algorithm is a typical distance, given unsupervised real‐time clustering algorithm [19]. The principle is simple, but it is time‐consuming and has low adaptability.…”

Section: Experiments Analysismentioning

confidence: 99%

Adaptive image segmentation algorithm under the constraint of edge posterior probability

Gong

Lan

Yang

et al. 2017

IET Computer Vision

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Image segmentation is an important step in image processing, but contemporary segmentation algorithms have problems such as poor anti-noise performance, over-segmentation, and imprecise results. To solve these problems, the authors proposed an adaptive image segmentation algorithm under the constraint of edge posterior probability. This algorithm first resolves the problem of over-segmentation by improving the watershed algorithm. Then, the algorithm automatically decides whether to adopt the edge threshold segmentation resulting from the watershed algorithm based on the proposed edge posterior probability model. Experiments showed that the proposed algorithm has excellent anti-noise performance, highly precise segmentation result, and are useful in effectively segmenting low-contrast images.

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Adaptive fuzzy moving K-means clustering algorithm for image segmentation

Cited by 112 publications

References 19 publications

Multiple Feature Fuzzy c-means Clustering Algorithm for Segmentation of Microarray Images

Multiple Feature Fuzzy c-means Clustering Algorithm for Segmentation of Microarray Images

Research on Predicting Line Loss Rate in Low Voltage Distribution Network Based on Gradient Boosting Decision Tree

Adaptive image segmentation algorithm under the constraint of edge posterior probability

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