Medical Image Fusion Based on Ripplet Transform Type-I

Das, Susmita; Chowdhury, Manish; Kundu, and Malay Kumar

doi:10.2528/pierb11040601

Cited by 52 publications

(23 citation statements)

References 12 publications

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“…In case of image denoising application, RT performs better than CVT and DWT. RT produces high quality fused images, when applied in the medical image fusion domain [21]. All these experiments show that RT based image coding is suitable for representing texture or edges in images.…”

Section: Ripplet Transform Type-i (Rt)mentioning

confidence: 86%

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Das¹,

Chowdhury²,

Kundu³

2013

PIER

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110

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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.

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Section: Ripplet Transform Type-i (Rt)mentioning

confidence: 86%

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Das¹,

Chowdhury²,

Kundu³

2013

PIER

Self Cite

110

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“…Recently, to overcome these problems, many improved IF/MIF methods based on multiscale geometric analysis (MGA) tools (like curvelet, contourlet, ripplet etc.) are proposed [9], [10]. However, measuring the importance/contribution of individual source image in the fused image, and finding effective way of combining them is still an open problem.…”

Section: Introductionmentioning

confidence: 99%

A Neuro-Fuzzy Approach for Medical Image Fusion

Das

Kundu

2013

IEEE Trans. Biomed. Eng.

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144

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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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“…There are studies utilizing image fusion and multi-resolution analysis methods together [15][16][17].…”

Section: Image Fusion and Fusion Rulesmentioning

confidence: 99%

Fusion and ANN based classification of liver focal lesions using phases in magnetic resonance imaging

Ozturk

Ceylan

2015

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

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Detecting and diagnosing the liver focal lesions have vital importance in planning the treatments of the patients. While there is no need to apply any treatment for benign lesions, medical treatments or surgical operations are necessary in case of existence of malign lesions. Pre-contrast, arterial, portal venous and delayed venous phases in magnetic resonance imaging help to make clear diagnosis through their different contrast material holding properties. In this study, magnetic resonance images belonging to 60 patients are classified as benign/malign by using multi-resolution analysis methods and artificial neural networks. In proposed system, the magnetic resonance images taken from four different phases for each patient are merged with three multi-resolution analyses based on fusion rules and classified by using artificial neural networks. The accuracy rate of the study is reached to 90%. Index Terms -Multi-resolution analysis methods, fusion, liver classification, ANN.

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Medical Image Fusion Based on Ripplet Transform Type-I

Cited by 52 publications

References 12 publications

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

Brain MR Image Classification Using Multiscale Geometric Analysis of Ripplet

A Neuro-Fuzzy Approach for Medical Image Fusion

Fusion and ANN based classification of liver focal lesions using phases in magnetic resonance imaging

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