Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation

ABSTRACT The robustness and speed of image classification is still a challenging task in satellite image processing. This paper introduces a novel image classification technique that uses the particle filter framework (PFF)-based optimisation technique for satellite image classification. The framework uses a template-matching algorithm, comprising fast marching algorithm (FMA) and level set method (LSM)-based segmentation which assists in creating the initial templates for comparison with other test images. The created templates are trained and used as inputs for the optimisation. The optimisation technique used in this proposed work is multikernel sparse representation (MKSR). The combined execution of FMA, LSM, PFF and MKSR approaches has resulted in a substantial reduction in processing time for various classes in a satellite image which is small when compared with Support Vector Machine (SVM) and Independent Component Discrimination Analysis (ICDA)based image classifications obtained for comparison purposes. This study aims to improve the robustness of image classification based on overall accuracy (OA) and kappa coefficient. The variation of OA with this technique, between different classes of a satellite image, is only10%, whereas that with the SVM and ICDA techniques is more than 50%.

FULLY AUTOMATIC FRAMEWORK FOR SEGMENTATION OF BRAIN MRI IMAGE / 药物分析学报

Objective To propose an automatic framework for segmentation of brain image in this paper. Methods The brain MRI image segmentation framework consists of three-step segmentation procedures. First, Non-brain structures removal by level set method. Then, the non-uniformity correction method is based on computing estimates of tissue intensity variation. Finally, it uses a statistical model based on Markov random filed for MRI brain image segmentation. The brain tissue can be classified into cerebrospinal fluid, white matter and gray matter. Results To evaluate the proposed our method, we performed two sets of experiments, one on simulated MR and another on real MR brain data. Conclusion The efficacy of the brain MRI image segmentation framework has been demonstrated by the extensive experiments. In the future, we are also planning on a large-scale clinical evaluation of this segmentation framework.