Abstract
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.
Highlights
Magnetic resonance imaging (MRI) of the brain is often used to monitor tumor response to treatment process
6 Experimental results the performance of the proposed prior-informationguided FCM (PIGFCM) is evaluated for the segmentation of normal and pathological brain MRI volumes
As there are publically available standard benchmark datasets of normal synthetic and real human brain MRI volumes with known ground truth, our first series of experiments are directed to the automatic segmentation of normal brain tissues
Summary
Magnetic resonance imaging (MRI) of the brain is often used to monitor tumor response to treatment process. It inherently offers a soft classification model, which is consistent with the partial volume effect observed in MR images and eliminates the need for explicit modeling of mixed classes (which is required - for example - by segmentation methods based on the finite Gaussian mixture [5]). Another key advantage of the fuzzy approach is that it can segment several tissues at the same time. The exact formulae to obtain the memberships and class centers can be derived
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