In this article, a novel multimodal medical image fusion (MIF) method based on non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN) is presented. The proposed MIF scheme exploits the advantages of both the NSCT and the PCNN to obtain better fusion results. The source medical images are first decomposed by NSCT. The low-frequency subbands (LFSs) are fused using the 'max selection' rule. For fusing the high-frequency subbands (HFSs), a PCNN model is utilized. Modified spatial frequency in NSCT domain is input to motivate the PCNN, and coefficients in NSCT domain with large firing times are selected as coefficients of the fused image. Finally, inverse NSCT (INSCT) is applied to get the fused image. Subjective as well as objective analysis of the results and comparisons with state-of-the-art MIF techniques show the effectiveness of the proposed scheme in fusing multimodal medical images.

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Abstract-We propose an automatic and accurate technique for classifying normal and abnormal magnetic resonance (MR) images of human brain. Ripplet transform Type-I (RT), an efficient multiscale geometric analysis (MGA) tool for digital images, is used to represent the salient features of the brain MR images. The dimensionality of the image representative feature vector is reduced by principal component analysis (PCA). A computationally less expensive support vector machine (SVM), called least square-SVM (LS-SVM) is used to classify the brain MR images. Extensive experiments were carried out to evaluate the performance of the proposed system. Two benchmark MR image datasets and a new larger dataset were used in the experiments, consisting 66, 160 and 255 images, respectively. The generalization capability of the proposed technique is enhanced by 5 × 5 cross validation procedure. For all the datasets used in the experiments, the proposed system shows high classification accuracies (on an average > 99%). Experimental results and performance comparisons with state-of-the-art techniques, show that the proposed scheme is efficient in brain MR image classification.

Abstract-This paper addresses a novel approach to the multisensor, multimodal medical image fusion (MIF) problem, employing multiscale geometric analysis of non-subsampled contourlet transform and fuzzy-adaptive reduced pulse-coupled neural network (RPCNN). The linking strengths of the RPCNNs' neurons are adaptively set by modelling them as the fuzzy membership values, representing their significance in the corresponding source image. Use of RPCNN with less complex structure and having less number of parameters, leads to computational efficiency, an important requirement of point-of-care (POC) health care technologies. The proposed scheme is free from the common shortcomings of the state-of-the-art MIF techniques: contrast reduction, loss of image fine details and unwanted image degradations etc. Subjective and objective evaluations show better performance of this new approach compared to existing techniques.Index Terms-Image fusion, artificial neural network, fuzzy logic, medical imaging, image analysis.

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