Intensity inhomogeneity compensation and segmentation of MR brain images using hybrid c-means clustering models

A Feature Ranking and Selection Algorithm for Brain Tumor Segmentation in Multi-Spectral Magnetic Resonance Image Data

Accuracy is the most important quality marker in medical image segmentation. However, when the task is to handle large volumes of data, the relevance of processing speed rises. In machine learning solutions the optimization of the feature set can significantly reduce the computational load. This paper presents a method for feature selection and applies it in the context of a brain tumor detection and segmentation problem in multi-spectral magnetic resonance image data. Starting from an initial set of 104 features involved in an existing ensemble learning solution that employs binary decision trees, a reduced set of features is obtained using a iterative algorithm based on a composite criterion. In each iteration, features are ranked according to the frequency of usage and the correctness of the decisions to which they contribute. Lowest ranked features are iteratively eliminated as long as the segmentation accuracy is not damaged. The final reduced set of 13 features provide the same accuracy in the whole tumor segmentation process as the initial one, but three times faster.

A hybrid method based on fuzzy clustering and local region-based level set for segmentation of inhomogeneous medical images

medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation. To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering. Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method. Also, the controlling parameters of level set are directly computed from fuzzy clustering result. This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy. So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images. Proposed method was tested on several medical images from different modalities for performance evaluation. Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods.

A new kernel-based fuzzy level set method for automated segmentation of medical images in the presence of intensity inhomogeneity

Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.

Efficient inhomogeneity compensation using fuzzy c-means clustering models

Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into classification or clustering algorithms, they generally have difficulties when INU reaches high amplitudes and usually suffer from high computational load. This study reformulates the design of c-means clustering based INU compensation techniques by identifying and separating those globally working computationally costly operations that can be applied to gray intensity levels instead of individual pixels. The theoretical assumptions are demonstrated using the fuzzy c-means algorithm, but the proposed modification is compatible with a various range of c-means clustering based INU compensation and MR image segmentation algorithms. Experiments carried out using synthetic phantoms and real MR images indicate that the proposed approach produces practically the same segmentation accuracy as the conventional formulation, but 20-30 times faster.