Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

Fuzzy c-means algorithm (FCM) is among the most commonly used in the medical image segmentation process. Nevertheless, the traditional FCM clustering approach has been several weaknesses such as noise sensitivity and stuck in local optimum, due to FCM hasn’t able to consider the information of contextual. To solve FCM problems, this paper presented spatial information of fuzzy clustering-based mean best artificial bee colony algorithm, which is called SFCM-MeanABC. This proposed approach is used contextual information in the spatial fuzzy clustering algorithm to reduce sensitivity to noise and its used MeanABC capability of balancing between exploration and exploitation that is explore the positive and negative directions in search space to find the best solutions, which leads to avoiding stuck in a local optimum. The experiments are carried out on two kinds of brain images the Phantom MRI brain image with a different level of noise and simulated image. The performance of the SFCM-MeanABC approach shows promising results compared with SFCM-ABC and other stats of the arts

A Modified Brain MR Image Segmentation and Bias Field Estimation Model Based on Local and Global Information

Because of the poor radio frequency coil uniformity and gradient-driven eddy currents, there is much noise and intensity inhomogeneity (bias) in brain magnetic resonance (MR) image, and it severely affects the segmentation accuracy. Better segmentation results are difficult to achieve by traditional methods; therefore, in this paper, a modified brain MR image segmentation and bias field estimation model based on local and global information is proposed. We first construct local constraints including image neighborhood information in Gaussian kernel mapping space, and then the complete regularization is established by introducing nonlocal spatial information of MR image. The weighting between local and global information is automatically adjusted according to image local information. At the same time, bias field information is coupled with the model, and it makes the model reduce noise interference but also can effectively estimate the bias field information. Experimental results demonstrate that the proposed algorithm has strong robustness to noise and bias field is well corrected

Development of Texture Weighted Fuzzy C-Means Algorithm for 3D Brain MRI Segmentation

The segmentation of human brain Magnetic Resonance Image is an essential component in the computer-aided medical image processing research. Brain is one of the fields that are attracted to Magnetic Resonance Image segmentation because of its importance to human. Many algorithms have been developed over decades for brain Magnetic Resonance Image segmentation for diagnosing diseases, such as tumors, Alzheimer, and Schizophrenia. Fuzzy C-Means algorithm is one of the practical algorithms for brain Magnetic Resonance Image segmentation. However, Intensity Non- Uniformity problem in brain Magnetic Resonance Image is still challenging to existing Fuzzy C-Means algorithm. In this paper, we propose the Texture weighted Fuzzy C-Means algorithm performed with Local Binary Patterns on Three Orthogonal Planes. By incorporating texture constraints, Texture weighted Fuzzy C-Means could take into account more global image information. The proposed algorithm is divided into following stages: Volume of Interest is extracted by 3D skull stripping in the pre-processing stage. The initial Fuzzy C-Means clustering and Local Binary Patterns on Three Orthogonal Planes feature extraction are performed to extract and classify each cluster’s features. At the last stage, Fuzzy C-Means with texture constraints refines the result of initial Fuzzy C-Means. The proposed algorithm has been implemented to evaluate the performance of segmentation result with Dice’s coefficient and Tanimoto coefficient compared with the ground truth. The results show that the proposed algorithm has the better segmentation accuracy than existing Fuzzy C-Means models for brain Magnetic Resonance Image

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging