Author
Biplab Kanti Sen
Bio: Biplab Kanti Sen is an academic researcher from University of Calcutta. The author has contributed to research in topics: Image fusion & Fuzzy set. The author has an hindex of 5, co-authored 11 publications receiving 49 citations.
Papers
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TL;DR: The experimental results show the excellence of the proposed method in enhancement in terms of PSNR, MSE, Mean and Standard Deviation compared with performance of conventional MSR and other state-of-art techniques.
17 citations
TL;DR: This work presents a novel fusion technique for color PET-MRI medical images using Two-Dimensional Discrete Fourier-Karhunen–Loeve transform and singular value decomposition (SVD) in shearlet domain and uses the inverse shearlett transformation (IST) to obtain the fused image.
Abstract: The color PET-MRI medical image fusion is a growing research area in medical image processing domain. MRI imagery provides the picture of the anatomy of brain tissues without any functional informa...
12 citations
01 Nov 2015
TL;DR: This paper presents a fuzzy fusion technique for multimodal medical image fusion using type-2 fuzzy set and near set and demonstrates the superiority of the proposed technique in terms of various fusion metrics.
Abstract: This paper presents a fuzzy fusion technique for multimodal medical image fusion using type-2 fuzzy set and near set For each pixel of both source images, pixel-wise fuzzification based on histogram level is done Then, construct type-2 fuzzy membership grade from both fuzzified images to quantify the uncertainty of shape of membership function Later, fuzzy entropy, mutual information and correlation are estimated the fuzziness, correlated fuzzy information, and similarity between both source images Near fuzzy set is used to estimate the amount of nearness between two membership grades and combined best membership grade Finally, fused image is obtained by defuzzification The proposed method is simulated on a set of medical images and compared with state-of-the-art methods The experimental results demonstrates the superiority of the proposed technique in terms of various fusion metrics
8 citations
TL;DR: The experimental results on satellite images show that the proposed method has good performance and able to preserve spectral information and high spatial details simultaneously like the original source images, and has a better edge over the prevalent image fusion methods.
7 citations
TL;DR: This work proposes an image fusion method using type-2 fuzzy and near-fuzzy set model which approximately estimate fuzzy behavior of medical images using fuzzy entropy and fuzzy correlation operators and shows that the proposed framework can generate excellent fused image in terms of several quantitative fusion evaluation indexes.
Abstract: Image fusion process combines the information from multiple images of one view, to acquire an informative single image which is required for computer vision applications. In case brain imag...
5 citations
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TL;DR: A comprehensive review of all conducting intensive research survey into the pros and cons, main architecture, and extended versions of this algorithm.
216 citations
TL;DR: In this article, a multi-modality algorithm for medical image fusion based on the Adolescent Identity Search Algorithm (AISA) for the Non-Subsampled Shearlet Transform is proposed to obtain image optimization and to reduce the computational cost and time.
103 citations
TL;DR: This study presents an up-to-date review over the application of NIOAs in image enhancement domain and the key issues which are involved in the formulation of NioAs based image enhancement models are discussed here.
Abstract: In the field of image processing, there are several problems where the efficient search has to be performed in complex search domain to find an optimal solution Image enhancement which improves the quality of an image for visual analysis and/or machine understanding is one of these problems There is no unique image enhancement technique and it’s measurement criterion which satisfies all the necessity and quantitatively judge the quality of a given image respectively Thus sometimes proper image enhancement problem becomes hard and takes large computational time In order to overcome that problem, researchers formulated the image enhancement as optimization problems and solved using Nature-Inspired Optimization Algorithms (NIOAs) which starts a new era in image enhancement field This study presents an up-to-date review over the application of NIOAs in image enhancement domain The key issues which are involved in the formulation of NIOAs based image enhancement models are also discussed here
84 citations
TL;DR: A detailed literature panorama of medical image fusion is presented in this paper, where pixel-level, feature-level and decision-level fusion methods are highlighted and discussed with several approaches in each category Theories behind fusion algorithms are explored aiming to address challenges and limitations of each classes.
75 citations
TL;DR: A novel two-stage medical image fusion scheme, which is based on non-subsampled shearlet transform (NSST) and simplified pulse coupled neural networks (S-PCNNs), is proposed in the hue-saturation-value (HSV) color space, and it can fuse more information into the final images than conventional methods.
61 citations