@inproceedings{38c1be38993d441284e8183b0df003c9,
title = "Unsupervised Brain Tissue Segmentation by Using Bias Correction Fuzzy C-Means and Class-Adaptive Hidden Markov Random Field Modelling",
abstract = "Unsupervised brain tissue segmentation in magnetic resonance imaging (MRI) is a key step in brain analysis, such as computer-aided surgery, clinical diagnosis, pathological analysis, surgical planning. Due to the noise and bias field in MRI, it is difficult to automatically segment brain tissue. In order to improve the segmentation accuracy, we propose an unsupervised method which combines an improved bias correction Fuzzy C-means (BCFCM) and class-adaptive hidden Markov random field Modelling (HMRF). The BCFCM segmentation result is used as the initial labelling for class-adaptive HMRF, which is utilized to refine the segmentation results. Experiments are evaluated on simulated MR images. Comparing with the ground truth, the results show that the proposed method can perform well on MR brain images with noisy MRI and bias field.",
keywords = "Bias field, Brain, MRI, Noise, Segmentation",
author = "Ziming Zeng and Chunlei Han and Liping Wang and Reyer Zwiggelaar",
year = "2013",
month = dec,
day = "6",
doi = "10.1007/978-94-007-7618-0_56",
language = "English",
isbn = "9789400776173",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Nature",
pages = "579--587",
editor = "Shaozi Li and Qun Jin and Xiaohong Jiang and Park, {James J. (Jong Hyuk)}",
booktitle = "Frontier and Future Development of Information Technology in Medicine and Education, ITME 2013",
address = "Switzerland",
}