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In this paper, we present a novel modified Fuzzy C-means algorithm with symmetry information to reduce the effect of noise in brain tissue segmentation in magnetic resonance image (MRI). We integrate brain's bilateral symmetry into the conventional Fuzzy C-means (FCM) as an additional term. In experiments, some synthetic images, and both simulated and real brain images were used to investigate the robustness of the method against noise. Finally, the method was compared with the conventional FCM algorithm. Results show the viability of the approach and the preliminary investigation appears promising.

This paper presents a new modified fuzzy c-means (FCM) clustering algorithm that exploits bilateral symmetry information in image data. With the assumption of pixels that are located symmetrically tend to have similar intensity values; we compute the degree of symmetry for each pixel with respect to a global symmetry axis of the image. This information is integrated into the objective function of the standard FCM algorithm. Experimental results show the effectiveness of the approach. The method was further improved using neighbourhood information, and was compared with conventional fuzzy c-means algorithms.

In neuroimage analysis, the automatic identification of symmetry plane has various applications. Despite the considerable amount of research, this remains an open problem. Most of the existing work based on image intensity is either sensitive to strong noise or not applicable to different imaging modalities. This paper presents a novel approach for identifying symmetry plane in three-dimensional brain magnetic resonance (MR) images based on the concepts of fractal dimension and lacunarity analysis which characterizes the complexity and homogeneity of an object. Experimental results, evaluation, and comparison with two other state-of-the-art techniques show the accuracy and the robustness of our method.

Symmetry plane identification is a critical initial step in brain image analysis, either done manually or automatically. Automatic extraction of the symmetry plane can provide an initial estimate for brain image registration, pathology assessment and disease diagnosis. This paper presents a novel technique for extracting the mid plane from volumetric magnetic resonance images (MRI). It is a theoretically simple approach that utilizes anatomical and radiological properties. The method is straight-forward to implement without the need for any prior segmentation. The efficacy of the proposed method was tested on brain MRI while investigating the robustness against rotation, intensity non-uniformity, noise and pathology. The method was compared with a cross-correlation based technique and the results show the viability of the approach.

In brain image analysis, the automatic identification of symmetry plane has various applications. This paper presents a new method that uses the concept of fractal dimension as a quantitative measure for identifying symmetry plane in three-dimensional (3D) brain magnetic resonance (MR) images. The method was tested on various 3D MRI datasets. Robust and accurate results were obtained in our experiments.