TY - GEN
T1 - Level set segmentation using non-negative matrix factorization of brain MRI images
AU - Dera, Dimah
AU - Bouaynaya, Nidhal
AU - Fathallah-Shaykh, Hassan M.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/16
Y1 - 2015/12/16
N2 - This paper presents a new level set method for image segmentation by integrating the level set formulation and the non-negative matrix factorization (NMF). The proposed model characterizes the histogram of the image by dividing the image into blocks and computing the histograms of the blocks as nonnegative combinations of basic histograms. This is achieved by using the NMF algorithm. The basic histograms form a clustering of the image into distinct regions. Our model also takes into account the intensity inhomogeneity or the bias field that usually corrupts medical images. In a level set formulation, this clustering criterion defines an energy in terms of the level set functions that represent a partition of the image domain. The image segmentation is achieved by minimizing this energy with respect to the level set functions and the bias field. Our method is compared, using synthetic and real images, to other state-of-the-art level set approaches that are based on localized clustering and local Gaussian distribution fitting. It is shown that the proposed approach is more robust to noise in the image and intensity inhomogeneity. These advantages stem from the fact that the proposed model i) depends on the distribution of pixels intensities (the histogram) rather than the direct intensity values and ii) does not introduce additional model parameters to be simultaneously estimated with the bias field and the level set functions.
AB - This paper presents a new level set method for image segmentation by integrating the level set formulation and the non-negative matrix factorization (NMF). The proposed model characterizes the histogram of the image by dividing the image into blocks and computing the histograms of the blocks as nonnegative combinations of basic histograms. This is achieved by using the NMF algorithm. The basic histograms form a clustering of the image into distinct regions. Our model also takes into account the intensity inhomogeneity or the bias field that usually corrupts medical images. In a level set formulation, this clustering criterion defines an energy in terms of the level set functions that represent a partition of the image domain. The image segmentation is achieved by minimizing this energy with respect to the level set functions and the bias field. Our method is compared, using synthetic and real images, to other state-of-the-art level set approaches that are based on localized clustering and local Gaussian distribution fitting. It is shown that the proposed approach is more robust to noise in the image and intensity inhomogeneity. These advantages stem from the fact that the proposed model i) depends on the distribution of pixels intensities (the histogram) rather than the direct intensity values and ii) does not introduce additional model parameters to be simultaneously estimated with the bias field and the level set functions.
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U2 - 10.1109/BIBM.2015.7359711
DO - 10.1109/BIBM.2015.7359711
M3 - Conference contribution
AN - SCOPUS:84962367730
T3 - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
SP - 382
EP - 387
BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
A2 - Schapranow, lng. Matthieu
A2 - Zhou, Jiayu
A2 - Hu, Xiaohua Tony
A2 - Ma, Bin
A2 - Rajasekaran, Sanguthevar
A2 - Miyano, Satoru
A2 - Yoo, Illhoi
A2 - Pierce, Brian
A2 - Shehu, Amarda
A2 - Gombar, Vijay K.
A2 - Chen, Brian
A2 - Pai, Vinay
A2 - Huan, Jun
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Y2 - 9 November 2015 through 12 November 2015
ER -