A fast and robust image segmentation using FCM with spatial information

Wang, Xiangyang; Bu, Juan

doi:10.1016/j.dsp.2009.11.007

Cited by 122 publications

(56 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It can also generate local optimal solution due to poor initialization. In order to make the FCM algorithm more robust to noise and outliers for image segmentation, many modified fuzzy clustering approaches have been reported in the past [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Pedrycz [8] introduced a conditional fuzzy C-means-based clustering method guided by an auxiliary or conditional variable.…”

Section: Q2mentioning

confidence: 99%

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

Adhikari¹,

Sing

Basu

et al. 2015

Applied Soft Computing

162

View full text Add to dashboard Cite

Section: Q2mentioning

confidence: 99%

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

Adhikari¹,

Sing

Basu

et al. 2015

Applied Soft Computing

162

View full text Add to dashboard Cite

“…Clearly, a pixel ξ i in ξ no longer contains the intrinsic information x i because when I = j, S ij equals 0 in Equation (5). The algorithm proposed by Wang and Bu [38] has the same problem. It is common sense that spatial, textual and spectral information are the most important ones for human visual interpretation in remote-sensing image classification.…”

Section: Spatial Information In Fcmmentioning

confidence: 87%

An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering

Guo

Huo

2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:Interval type-2 fuzzy c-means (IT2FCM) clustering methods for remote-sensing data classification are based on interval type-2 fuzzy sets and can effectively handle uncertainty of membership grade. However, most of these methods neglect the spatial information when they are used in image clustering. The spatial information and spectral indices are useful in remote-sensing data classification. Thus, determining how to integrate them into IT2FCM to improve the quality and accuracy of the classification is very important. This paper proposes an enhanced IT2FCM* (EnIT2FCM*) algorithm by combining spatial information and spectral indices for remote-sensing data classification. First, the new comprehensive spatial information is defined as the combination of the local spatial distance and attribute distance or membership-grade distance. Then, a novel distance metric is proposed by combining this new spatial information and the selected spectral indices; these selected spectral indices are treated as another dataset in this distance metric. To test the effectiveness of the EnIT2FCM* algorithm, four typical validity indices along with the confusion matrix and kappa coefficient are used. The experimental results show that the spatial information definition proposed here is effective, and some spectral indices and their combinations improve the performance of the EnIT2FCM*. Thus, the selection of suitable spectral indices is crucial, and the combination of soil adjusted vegetation index (SAVI) and the Automated Water Extraction Index (AWEI sh ) is the best choice of spectral indices for this method.

show abstract

“…Fuzzy Cmeans (FCM) clustering, a typical unsupervised clustering method, has shown to have a robust performance [10]. It has been widely used in medical image segmentation [11][12][13]. Based on FCM, the purpose of this study is to address a general Gaussian-type noise existing in many MR images, and to explore its potential in segmenting motion MR images.…”

Section: Y Feng Et Al / a Modified Fcm For Mr Images Segmentationmentioning

confidence: 99%

A modified fuzzy C-means method for segmenting MR images using non-local information

Feng¹,

Guo²,

Zhang³

et al. 2016

THC

View full text Add to dashboard Cite

Abstract. BACKGROUND:In recent years, MR images have been increasingly used in therapeutic applications such as image-guided radiotherapy (IGRT). However, images with low contrast values and noises present challenges for image segmentation. OBJECTIVE: The objective of this study is to develop a robust method based on fuzzy C-means (FCM) method which can segment MR images polluted with Gaussian noise. METHODS: A modified FCM algorithm accommodating non-local pixel information via Hausdorff distance was developed for segmenting MR images. The membership and objective functions were modified accordingly. Segmentations with different weights of the Hausdorff distance were compared. RESULTS: Segmentation tests using synthetic and MR images showed that the proposed algorithm was better at resolving boundaries and more robust to Gaussian noise. By segmenting a sample MR image of a tumor, we further showed the capability of the method in capturing the centroid of the target region. CONCLUSIONS: The modified FCM algorithm with neighboring information can be used to segment blurry images with potential applications in segmenting motion MR images in image-guided radiotherapy (IGRT).

show abstract

A fast and robust image segmentation using FCM with spatial information

Cited by 122 publications

References 13 publications

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering

A modified fuzzy C-means method for segmenting MR images using non-local information

Contact Info

Product

Resources

About