Скачать или смотреть Similarity Measure-Based Possibilistic FCM With Label Information for Brain MRI Segmentation

Abstract—Magnetic resonance imaging (MRI) is extensively
applied in clinical practice. Segmentation of the MRI brain image
is significant to the detection of brain abnormalities. However,
owing to the coexistence of intensity inhomogeneity and noise,
dividing the MRI brain image into different clusters precisely
has become an arduous task. In this paper, an improved possibilistic
fuzzy c-means (FCM) method based on a similarity
measure is proposed to improve the segmentation performance
for MRI brain images. By introducing the new similarity measure,
the proposed method is more effective for clustering the
data with nonspherical distribution. Besides that, the new similarity
measure could alleviate the “cluster-size sensitivity” problem
that most FCM-based methods suffer from. Simultaneously, the
proposed method could preserve image details as well as suppress
image noises via the use of local label information. Experiments
conducted on both synthetic and clinical images show that the
proposed method is very effective, providing mitigation to the
cluster-size sensitivity problem, resistance to noisy images, and
applicability to data with more complex distribution.
Index Terms—Label information, magnetic resonance imaging
(MRI) brain image, possibilistic fuzzy c-means (PFCM), segmentation,
similarity measure.