Mr. Lalit Kumar Saini Profile

Lalit Kumar Saini

at Manipal Univ Jaipur

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Profile Summary

Lalit Kumar Saini is a research scholar at Manipal University Jaipur, Jaipur (India). He received his BE and M.Tech degrees in Computer Engineering from University of Rajasthan and RTU, Kota respectively. His current research interests include digital image fusion,sparse representation,dictionary learning, and compressed sensing algorithms.

Publications (1)

SPIE Journal Paper | 26 May 2022

Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm

Lalit Kumar Saini, Pratistha Mathur

JMI, Vol. 9, Issue 05, 052403, (May 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.5.052403

KEYWORDS: Image fusion, Associative arrays, Medical imaging, Image processing, Tumors, Magnetic resonance imaging, Positron emission tomography, Brain, Neuroimaging, Computed tomography

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Purpose: The technique of fusing or integrating medical images collected from single and various modalities is known as medical image fusion. This is done to improve the quality of the images and combine information from several medical images. The whole procedure aids medical practitioners in gaining correct information from single images. Image fusion is one of the fastest-growing research topics in the medical imaging field. Sparse modeling is a popular signal representation technique used for image fusion with different dictionary learning approaches. We propose a medical image fusion with sparse representation (SR) and block total least-square (BLOTLESS) update dictionary learning. Approach: The domain of dictionary learning is the most significant research domain related to SR. An efficient dictionary increases the effectiveness of sparse modeling. Due to SR being an ongoing interesting research area, the medical image fusion process is done with a modified image fusion framework with recently developed BLOTLESS update dictionary learning. Results: The experimental results are compared for the image fusion process using other state-of-the-art dictionary learning algorithms, such as simultaneous codeword optimization, method of optimal directions, and K-singular value decomposition. The effectiveness of the algorithm is evaluated based on image fusion quantitative parameters. Results show that the BLOTLESS update dictionary algorithm is a promising modification for the sparse-based image fusion with its applicability in the fusion of images related to different diseases. Conclusions: The experiments and results show that the dictionary learning algorithm plays an important role in the sparse-based image fusion general framework. The fusion results also show that the proposed improved image fusion framework for medical images is promising compared with frameworks with other dictionary learning algorithms. As an application, it is also used as a tool for the fusion of different modularities of images related to brain tumor and glioma.

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