Novel modified fuzzy c-means algorithm with applications

Kang, Jiayin; Lee, Min; Luan, Qingxian; Li, Xiao; Liu, Jinzhu

doi:10.1016/j.dsp.2007.11.005

Cited by 89 publications

(48 citation statements)

References 11 publications

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“…Bunların yerine görüntüde bir miktar kayıp ile yüksek oranda sıkıştırma daha çok tercih edilmektedir. Bu tekniklerden en popüler olanları LBG ile vektör nicemleme (Linde et al, 1980;Gray, 1984;Lin and Tai, 1998;Patane and Russo, 2001;Tsai et al, 2009;Pan et al, 2011, Ku et al, 2014, Khan et al, 2015, Bulanık C-Ortalamalar (BCO) (Dunn, 1973;Bezdek et al, 1984;Ya-zhong et al, 2011;Kang et al, 2009), K-Ortalamalar (KO) (Lloyd, 1982;Bagirov et al, 2011;Bai et al, 2013;Tzortzis and Likas, 2014) algoritmalarıdır. Klasik LBG algoritması iyi bir başlangıç kod vektör listesi hesaplanıp algoritma başlangıcında kullanılarak geliştirilmiştir (Patane, 2001).…”

Section: Introductionunclassified

A New Spliting LBG Algorithm for Codebook Generation in Image Compression

Koçyiğit¹,

Nil²,

Kılıç³

2017

jist

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ÖZET: Linde-Buzo-Gray (LBG) algoritması, görüntü sıkıştırmada Vektör Nicemleme (VN) tekniği için kullanılan, performansı büyük oranda başlangıç kod vektör listesine bağlı, kararlı, yerel optimum sonuç veren bir tekniktir. Bölme tabanlı LBG algoritmasında görüntüyü oluşturan vektörlerin ortalaması başlangıç olarak seçilir. Merkezler 2 n kez ikiye bölünüp güncellenerek yerel optimum kod listesi oluşturulur. Önerilen yeni teknikte (YLBG), LBG algoritması geliştirilmiş ve algoritma içerisindeki bölme işlemi tüm bölgelere uygulanmayıp sadece hatası en fazla olan bölge tespit edilip o bölgenin merkezi ikiye bölünerek merkez artırması sağlanmıştır. Böylece mevcut standart LBG'den farklı olarak merkezler teker teker artırılıp kod vektör listesinin performansı global olarak artırılmıştır. Bu çalışmada önerilen yeni teknik standart görüntülere uygulanmış, K-Ortalamalar (KO), LBG ve Bulanık C-Ortalamalar (BCO) ile karşılaştırılmış, ortalama karesel hata(OKH) ölçütüne göre üstün olduğu görülmüştür.Anahtar Kelimeler: Bulanık C-ortalamalar , K-ortalamalar, LBG, vektör nicemleme ABSTRACT: Linde-Buzo-Gray (LBG) algorithm is used in image processing for Vector Quantization (VQ). LBG technique is robust, performs locally best but depends on the initial codebook. In the splitting based VQ, the first center is defined as average of all vectors. The rest of 2 n centres are calculated by splitting and updated procedure. In the proposed new technique (NLBG) the LBG is improved and insted of splitting all centres into two new areas, the worst area that has highest mean square error splitted and updated into to new areas. Therefore, the number of codevectors is increased one by one apart from the classical LBG. Consequently, the performance of the codebook is increased globally. In this paper, the new technique is applied to the standard images, compared to the FCM(Fuzzy C-Means), K-Means (K -Ortalamalar) and LBG. As a result, it is seen that the proposed new technique performs better according to the criteria of MSE.

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Section: Introductionunclassified

A New Spliting LBG Algorithm for Codebook Generation in Image Compression

Koçyiğit¹,

Nil²,

Kılıç³

2017

jist

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“…To improve the performance of FCM, Caldairou et al (2011) used a non-local FCM method to segment brain MR images [21]. New forms of membership functions and objective functions including neighbor information were also proposed to increase the performance of FCM [22][23][24][25][26]. In these studies, membership functions were modified to have a weight function to include non-local pixel information from adjacent pixels, and the objective function was modified to include a non-local regularization term [21].…”

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

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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).

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“…Kang et al [30] improved FCM with adaptive weighted average filter. Ahmed et al [12] modified the objective function of FCM to allow the labeling of a pixel to be influenced by the labels in its immediate neighborhood.…”

Section: Related Workmentioning

confidence: 99%

“…The datasets also have the skull as part of the imaged volume, so it is important to remove it in a separate pre-process. This is achieved using the Brain suite [30] automated software package for skull removing. Then the proposed approach is applied on the volumes of the datasets to segment each into the five classes (WM, GM, CSF, BG, and tumor).…”

Section: Real Normal Mri Segmentationmentioning

confidence: 99%

Tumor segmentation in brain MRI using a fuzzy approach with class center priors

El-Melegy

Mokhtar

2014

J Image Video Proc

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This paper proposes a new fuzzy approach for the automatic segmentation of normal and pathological brain magnetic resonance imaging (MRI) volumetric datasets. The proposed approach reformulates the popular fuzzy c-means (FCM) algorithm to take into account any available information about the class center. The uncertainty in this information is also modeled. This information serves to regularize the clusters produced by the FCM algorithm thus boosting its performance under noisy and unexpected data acquisition conditions. In addition, it also speeds up the convergence process of the algorithm. Experiments using simulated and real, both normal and pathological, MRI volumes of the human brain show that the proposed approach has considerable better segmentation accuracy, robustness against noise, and faster response compared with several well-known fuzzy and non-fuzzy techniques reported in the literature.

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Novel modified fuzzy c-means algorithm with applications

Cited by 89 publications

References 11 publications

A New Spliting LBG Algorithm for Codebook Generation in Image Compression

A New Spliting LBG Algorithm for Codebook Generation in Image Compression

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

Tumor segmentation in brain MRI using a fuzzy approach with class center priors

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