Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction

In this paper, we propose an improvement method for image segmentation using the fuzzy c-means clustering algorithm (FCM). This algorithm is widely experimented in the field of image segmentation with very successful results. In this work, we suggest further improving these results by acting at thre...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Digital signal processing Ročník 23; číslo 5; s. 1390 - 1400
Hlavní autori: Benaichouche, A.N., Oulhadj, H., Siarry, P.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 01.09.2013
Elsevier
Predmet:
Algorithms
Clustering
Computer Science
Fuzzy
Fuzzy c-means (FCM) algorithms
Fuzzy logic
Fuzzy set theory
Image segmentation
Local spatial information
Mahalanobis distance
Operations Research
Particle swarm optimization (PSO)
Post-segmentation
Segmentation
Post-segmentation
Image segmentation
Fuzzy c-means (FCM) algorithms
Particle swarm optimization (PSO)
Mahalanobis distance
Local spatial information
ISSN:1051-2004, 1095-4333
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:In this paper, we propose an improvement method for image segmentation using the fuzzy c-means clustering algorithm (FCM). This algorithm is widely experimented in the field of image segmentation with very successful results. In this work, we suggest further improving these results by acting at three different levels. The first is related to the fuzzy c-means algorithm itself by improving the initialization step using a metaheuristic optimization. The second level is concerned with the integration of the spatial gray-level information of the image in the clustering segmentation process and the use of Mahalanobis distance to reduce the influence of the geometrical shape of the different classes. The final level corresponds to refining the segmentation results by correcting the errors of clustering by reallocating the potentially misclassified pixels. The proposed method, named improved spatial fuzzy c-means IFCMS, was evaluated on several test images including both synthetic images and simulated brain MRI images from the McConnell Brain Imaging Center (BrainWeb) database. This method is compared to the most used FCM-based algorithms of the literature. The results demonstrate the efficiency of the ideas presented.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2013.07.005