▪ Abstract Image segmentation plays a crucial role in many medical-imaging applications, by
automating or facilitating the delineation of anatomical structures and other regions of …

Accurate segmentation of medical images is a key step in contouring during radiotherapy
planning. Computed topography (CT) and Magnetic resonance (MR) imaging are the most …

We present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI)
data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity …

Medical image acquisition devices provide a vast amount of anatomical and functional
information, which facilitate and improve diagnosis and patient treatment, especially when …

We describe a sequence of low-level operations to isolate and classify brain tissue within T1-
weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue …

An algorithm is presented for the fuzzy segmentation of two-dimensional (2-D) and three-
dimensional (3-D) multispectral magnetic resonance (MR) images that have been corrupted …

Magnetic resonance imaging (MRI) is a sophisticated and versatile medical imaging
modality. The inverse FFT has served the MR community very well as the conventional …

This paper presents a survey of latest image segmentation techniques using fuzzy
clustering. Fuzzy C-Means (FCM) Clustering is the most wide spread clustering approach for …

An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the
segmentation of three-dimensional (3-D) magnetic resonance (MR) images. The input …

Magnetic resonance imaging is a popular and powerful non-invasive imaging technique.
Automated analysis has become mandatory to efficiently cope with the large amount of data …