You are viewing a javascript disabled version of the site. Please enable Javascript for this site to function properly.
Go to headerGo to navigationGo to searchGo to contentsGo to footer
In content section. Select this link to jump to navigation

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

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).

References

[1] 

Petitjean C., and Dacher J.-N., A review of segmentation methods in short axis cardiac MR images. Medical Image Analysis 15, 169 (2011).

[2] 

Li B.N., , Chui C.K., , Chang S., and Ong S.H., Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation. Computers in Biology and Medicine 41, 1 (2011).

[3] 

Egger J., , Kapur T., , Fedorov A., , Pieper S., , Miller J.V., , Veeraraghavan H., , Freisleben B., , Golby A.J., , Nimsky C., and Kikinis R., GBM volumetry using the 3D Slicer medical image computing platform. Scientific Reports 3, 1364 (2013).

[4] 

Hu Y., , Caruthers S.D., , Low D.A., , Parikh P.J., and Mutic S., Respiratory Amplitude Guided 4-Dimensional Magnetic Resonance Imaging. Int J Radiat Oncol 86, 198 (2013).

[5] 

Lagendijk J.J.W., , Raaymakers B.W., , Van den Berg C.A.T., , Moerland M.A., , Philippens M.E., and van Vulpen M., MR guidance in radiotherapy. Phys Med Biol 59, R349 (2014).

[6] 

Mutic S., , Parikh P.J., , Bradley J.D., , Hallahan D.E., , Hu Y., , Kashani R., , Kawrakow I., , Li H., , Michalski J.M., , Olsen J.R., , Robinson C.G., , Rodriguez V., , Santanam L., , Tanderup K., , Victoria J., , Wooten H.O., , Yang D., , Zoberi I., , Green O.L., and Dempsey J.F., The Dawn of a New Era: First Ever MR-IGRT Treatments - Initial Experiences and Future Implications. Int J Radiat Oncol 90, S94 (2014).

[7] 

Thomson D., , Boylan C., , Liptrot T., , Aitkenhead A., , Lee L., , Yap B., , Sykes A., , Rowbottom C., and Slevin N., Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk. Radiat. Oncol. 9, 12 (2014).

[8] 

Bezdek J.C., , Hall L.O., and Clarke L.P., Review of MR Image Segmentation Techniques Using Pattern-Recognition. Med Phys 20, 1033 (1993).

[9] 

Bezdek J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers (1981).

[10] 

Bezdek J.C., , Ehrlich R., and Full W., FCM: The fuzzy C-means clustering algorithm. Computers & Geosciences 10, 191 (1984).

[11] 

Feng Y., , Bayly P.V., , Huang J., , Robinson C., , Du D., , Mutic S., , Shimony J.S., , Leuthardt E.C., , Okamoto R.J., and Hu Y., A simulation study to investigate the potential of using Magnetic Resonance Elastography (MRE) to differentiate recurrent tumor and radiation necrosis. Medical Physics 40, 540 (2013).

[12] 

Wang X.Y., and Bu J.A., A fast and robust image segmentation using FCM with spatial information. Digit Signal Process 20, 1173 (2010).

[13] 

Jain A.K., , Murty M.N., and Flynn P.J., Data clustering: a review. ACM Computing Surveys 31, 264 (1999).

[14] 

Pham D.L., Spatial models for fuzzy clustering. Comput Vis Image Und 84, 285 (2001).

[15] 

Kannan S.R., , Ramathilagam S., , Devi R., and Sathya A., Robust kernel FCM in segmentation of breast medical images. Expert Systems with Applications 38, 4382 (2011).

[16] 

Zhang C., , Wang P., and Liu C., Green Communications and Networks, edited by Y. Yang and M. Ma, Springer Netherlands (2012), pp. 219-226.

[17] 

Noreen N., , Hayat K., and Madani S.A., MRI segmentation through wavelets and fuzzy C-means. World Applied Sciences Journal 13, 34 (2011).

[18] 

Keller B., , Nathan D., , Wang Y., , Zheng Y., , Gee J., , Conant E., and Kontos D., Adaptive Multi-cluster Fuzzy C-Means Segmentation of Breast Parenchymal Tissue in Digital Mammography. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2011, edited by G. Fichtinger, A. Martel and T. Peters, Springer Berlin Heidelberg (2011), pp. 562-569.

[19] 

Pal N.R., , Pal K., , Keller J.M., and Bezdek J.C., A possibilistic fuzzy C-means clustering algorithm. IEEE Transactions on Fuzzy Systems 13, 517 (2005).

[20] 

Khalilia M.A., , Bezdek J., , Popescu M., and Keller J.M., Improvements to the relational fuzzy C-means clustering algorithm. Pattern Recogn 47, 3920 (2014).

[21] 

Caldairou B., , Passat N., , Habas P.A., , Studholme C., and Rousseau F., A non-local fuzzy segmentation method: Application to brain MRI. Pattern Recogn 44, 1916 (2011).

[22] 

Kang J.Y., , Min L.Q., , Luan Q.X., , Li X., and Liu J.Z., Novel modified fuzzy C-means algorithm with applications. Digit Signal Process 19, 309 (2009).

[23] 

Yang Z., , Chung F.L., and Wang S.T., Robust fuzzy clustering-based image segmentation. Appl Soft Comput 9, 80 (2009).

[24] 

Cai W.L., , Chen S.C., and Zhang D.Q., Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn 40, 825 (2007).

[25] 

Xia Y., , Feng D.G., , Wang T.J., , Zhao R.C., and Zhang Y.N., Image segmentation by clustering of spatial patterns. Pattern Recogn Lett 28, 1548 (2007).

[26] 

Ahmed M.N., , Yamany S.M., , Mohamed N., , Farag A.A., and Moriarty T., A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE T Med Imaging 21, 193 (2002).

[27] 

Cao H., , Deng H., and Wang Y., Segmentation of M-FISH images for improved classification of chromosomes with an adaptive fuzzy C-means clustering algorithm. IEEE Transactions on Fuzzy Systems 20, 1 (2012).

[28] 

Astola L., , Fuster A., and Florack L., A Riemannian scalar measure for diffusion tensor images. Pattern Recogn 44, 1885 (2011).

[29] 

Huttenlocher D.P., , Klanderman G.A., and Rucklidge W.J., Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 850 (1993).

[30] 

Runkler T.A., and Bezdek J.C., Alternating cluster estimation: A new tool for clustering and function approximation. IEEE Transactions on Fuzzy Systems 7, 377 (1999).