SUDIP KUMAR ADHIKARI

@kgec.edu.in

Assistant Professor of Department of Computer Science & Engineering
KALYANI GOVERNMENT ENGINEERING COLLEGE, KALYANI



                       

https://researchid.co/sudipadhikari

Dr. Sudip Kumar Adhikari passed B. Tech in Computer Science & Engineering from Vidyasagar
University in 2002. He obtained the M.E. and Ph.D. degree in Computer Science & Engineering from Jadavpur University. He had more than 19 years of teaching experiences. He had published nearly eighteen research papers in reputed International Journals and Conferences. His research interest includes Medical image processing, Pattern recognition, Artificial intelligence, Soft Computing. He is a senior member of IEEE and member of Institute of Engineers. He is currently an assistant professor in Computer Science & Engineering Department of Cooch Behar Government Engineering College, Cooch Behar.

EDUCATION

B.Tech, M.E., PhD

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Computer Interaction

19

Scopus Publications

480

Scholar Citations

9

Scholar h-index

8

Scholar i10-index

Scopus Publications

  • Cocoa-Net: Performance Analysis on Classification of Cocoa Beans Using Structural Image Feature
    Chandrajit Pal, Samikshan Das, Amitava Akuli, Sudip Kumar Adhikari, and Aniruddha Dey
    Slovenian Association Informatika

  • Analysis of System Response, Energy Savings, and Fault Detection in a Weather and Traffic-Adaptive Smart Lighting System
    Tanumay Halder, Arnab Gain, Sudip Kumar Adhikari, and Biswanath Roy
    Seventh Sense Research Group Journals

  • Medical image analysis using swarm intelligence: A survey
    Sudip Kumar Adhikari, Prasenjit Dey, Sourav De, and Shouvik Paul
    Elsevier

  • Applications of Internet of Things and Machine Learning Technologies in Healthcare
    Prasenjit Dey, Sudip Kumar Adhikari, Sourav De, and Rudranath Banerjee
    Chapman and Hall/CRC

  • Internet of Things-Based Machine Learning in Healthcare: Technology and Applications
    Prasenjit Dey, Sudip Kumar Adhikari, Sourav De, and Indrajit Kar
    Chapman and Hall/CRC


  • Convolution of 3D Gaussian surfaces for volumetric intensity inhomogeneity estimation and correction in 3D brain MR image data
    Sayan Kahali, Sudip Kumar Adhikari, and Jamuna Kanta Sing
    Institution of Engineering and Technology (IET)
    Magnetic resonance (MR) imaging technique has become indispensable in image‐guided diagnosis and clinical research. However, present MR image acquisition leads to a slow varying intensity inhomogeneity (IIH) in MR image data. This study presents a novel technique based on convolution of three‐dimensional (3D) Gaussian surfaces, which is denoted as ‘Co3DGS’, for volumetric IIH estimation and correction for 3D brain MR image data. A 3D Gaussian surface is approximated using local voxel gradients on each tissue volume corresponding to grey matter, white matter and cerebrospinal fluid of the 3D brain MR image data and then convolved to partially estimate the IIH, which is subsequently removed from the image data. The above processes are repeated until there is no such significant change in the voxel gradients. The Co3DGS technique has been tested on both synthetic and in‐vivo human 3D brain MR image data of different pulse sequences. The empirical results both in qualitatively and quantitatively, which include coefficient of joint variation, index of variation, index of joint variation, index of class separability and root mean square error, collectively demonstrate that the Co3DGS efficiently estimates and removes the IIH from the 3D brain MR image data and stands superior to some state‐of‐the‐art methods.

  • A fuzzy clustering algorithm with local contextual information and Gaussian function for simultaneous brain MR image segmentation and intensity inhomogeneity estimation
    Nabanita Mahata, Sayan Kahali, Jamuna Kanta Sing, and Sudip Kumar Adhikari
    IEEE
    In this paper, we present a fuzzy clustering algorithm by integrating local contextual information and a Gaussian function for simultaneous brain MR image segmentation and intensity inhomogeneity estimation. For each pixel, a local contextual information is integrated due to highly correlation between the image pixels and used to define its fuzzy membership function to belong into a tissue type. Whereas, a Gaussian surface is fitted over each tissue region using the local image gradients to estimate the intensity inhomogeneity (IIH). In doing so, we have introduced global and local membership functions for each pixel. The combined IIH is iteratively removed from the image and the final segmentation result is obtained based on the global membership values. The simulation results on brain MR images show its superiority over other fuzzy-based clustering algorithms.

  • A two-stage fuzzy multi-objective framework for segmentation of 3D MRI brain image data
    Sayan Kahali, Sudip Kumar Adhikari, and Jamuna Kanta Sing
    Elsevier BV

  • 3D MRI brain image segmentation: A two-stage framework
    Sayan Kahali, Sudip Kumar Adhikari, and Jamuna Kanta Sing
    Springer Singapore

  • On estimation of bias field in MRI images: polynomial vs Gaussian surface fitting method
    Sayan Kahali, Sudip Kumar Adhikari, and Jamuna Kanta Sing
    Wiley
    Surface fitting is one of the well‐known retrospective methods for bias field estimation from magnetic resonance imaging (MRI) images. Bias field in MRI images is primarily caused because of radio frequency–coil nonuniformity, improper image acquisition process, patient movement, and so on. The bias field can be characterized by any slow variant and smooth function because of its slow variant nature. In this paper, we present a comparative study between polynomial and Gaussian surface fitting methods. In particular, we have used both the second‐ and third‐order polynomial functions to estimate the bias field. In this study, we approximate the bias field in two different ways. In the first method, the surfaces are fitted on the anatomical tissue regions individually and then fused to estimate the bias field. Conversely, in the second method, we have done the same over the entire image region. We have tested on three volumes of simulated and one volume of real‐patient MRI brain images and validated the results by both the qualitative and quantitative analyses. The quantitative analyses are presented in standard deviation and coefficient of joint variation. The analysis of the simulation results show that the Gaussian surface fitting method yields better results in both the cases, where the surface fitting is done on entire image and individual tissue regions.

  • Bias field estimation and segmentation of MRI images using a Spatial Fuzzy C-means algorithm
    Sudip Kumar Adhikari, Jamuna Kanta Sing, and Dipak Kumar Basu
    IEEE
    Magnetic resonance imaging (MRI) images suffer from intensity inhomogeneity or bias field causes due to smooth intensity variations of the same tissue across the image region. This paper presents a new method called Bias Estimated Spatial Fuzzy C-means (BESFCM) algorithm for intensity inhomogeneity estimation and segmentation of MRI images at the same time. First, we formulate a new local fuzzy membership function that includes a probability function of a pixel considering its spatial neighbourhood information. Then, we introduce a new clustering center and weighted joint membership functions using the local and global membership values. Finally, MRI images are segmented and bias field is estimated by formulating an objective function using the new cluster centers and joint membership functions. The simulation results show that the resulting BESFCM algorithm estimates intensity inhomogeneity and improves the segmentation results as compared to other FCM-based clustering algorithms.

  • On estimation of bias field in MRI images
    Jamuna Kanta Sing, Sudip Kumar Adhikari, and Sayan Kahali
    IEEE

  • A nonparametric method for intensity inhomogeneity correction in MRI brain images by fusion of Gaussian surfaces
    Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, Mita Nasipuri, and Punam Kumar Saha
    Springer Science and Business Media LLC

  • A modified fuzzy C-means algorithm using scale control spatial information for MRI image segmentation in the presence of noise
    Jamuna Kanta Sing, Sudip Kumar Adhikari, and Dipak Kumar Basu
    Wiley
    AbstractThe fuzzy C‐means (FCM) algorithm does not fully utilize the spatial information for image segmentation and is sensitive to the presence of noise and intensity inhomogeneity in magnetic resonance imaging (MRI) images. The underlying reason is that using a single fuzzy membership function the FCM algorithm cannot properly represent pattern associations to all clusters. In this paper, we present a modified FCM (mFCM) algorithm by incorporating scale control spatial information for segmentation of MRI images in the presence of high levels of noise and intensity inhomogeneity. The algorithm utilizes scale controlled spatial information from the neighbourhood of each pixel under consideration in the form of a probability function. Using this probability function, a local membership function is introduced for each pixel. Finally, new clustering centre and weighted joint membership functions are introduced based on the local membership and global membership functions. The resulting mFCM algorithm is robust to the noise and intensity inhomogeneity in MRI image data and thereby improves the segmentation results. The experimental results on a synthetic image, four volumes of simulated and one volume of real‐patient MRI brain images show that the mFCM algorithm outperforms k‐means, FCM and some other recently proposed FCM‐based algorithms for image segmentation in terms of qualitative and quantitative studies such as cluster validity functions, segmentation accuracy and tissue segmentation accuracy. Copyright © 2015 John Wiley & Sons, Ltd.

  • Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images
    Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, and Mita Nasipuri
    Elsevier BV

  • A spatial fuzzy C-means algorithm with application to MRI image segmentation
    Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, and Mita Nasipuri
    IEEE
    The standard fuzzy C-means (FCM) algorithm does not fully utilize the spatial information for image segmentation and is sensitive to noise especially in the presence of intensity inhomogeneity in magnetic resonance imaging (MRI) images. The underlying reason is that a single fuzzy membership function in FCM algorithm cannot properly represent pattern associations to all clusters. In this paper, we present a spatial fuzzy C-means (SpFCM) algorithm for the segmentation of MRI images. The algorithm utilizes spatial information from the neighbourhood of each pixel under consideration and is realized by defining a probability function. A new membership function is introduced using this spatial information to generate local membership values for each pixel. Finally, new clustering centers and weighted joint membership functions are presented based on the local and global membership functions. The resulting SpFCM algorithm solves the problem of sensitivity to noise and intensity inhomogeneity in MRI data and thereby improves the segmentation results. The experimental results on several simulated and real-patient MRI brain images show that the SpFCM algorithm has superior performance on image segmentation when compared to some FCM-based algorithms.

  • Conditional spatial fuzzy C-means clustering algorithm with application in MRI image segmentation
    Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, and Mita Nasipuri
    Springer India

  • Segmentation of MRI brain images by incorporating intensity inhomogeneity and spatial information using probabilistic fuzzy c-means clustering algorithm
    Sudip Kumar Adhikari, J. K. Sing, D. K. Basu, M. Nasipuri, and P. K. Saha
    IEEE
    Segmentation of magnetic resonance imaging (MRI) brain images is an important task to analyze tissue structures of a human brain. Due to improper image acquisition systems, MRI images are generally corrupted by intensity inhomogeneity (IIH) or intensity nonuniformity (INU). Conventional methods try to segment MRI images using only spatial information about the distribution of pixel intensities and are highly sensitive to noise and the IIH or INU. This paper presents a method to segment MRI brain images by considering the INU and spatial information using fuzzy C-means (FCM) clustering algorithm. Firstly, the INU of MRI brain image is corrected using fusion of Gaussian surfaces. The individual Gaussian surface is estimated independently over the different homogeneous regions by considering its center as the center of mass of the respective homogeneous region. Secondly, the IIH corrected image is segmented using probabilistic FCM algorithm, which considers spatial features of image pixels. The experiments using 3D synthetic phantoms and real-patient MRI brain images reveal that the proposed method performs satisfactorily.

RECENT SCHOLAR PUBLICATIONS

  • Programmable Optically Variable Resistors: Automating the Design and Measurement of Transistor Biasing Circuits
    S Ray, A Acharyya, A Sarkar, P Das, R Das, T Halder, A Maji, ...
    Journal of Circuits, Systems and Computers 2025

  • Cocoa-Net: Performance Analysis on Classification of Cocoa Beans Using Structural Image Feature
    C Pal, S Das, A Akuli, SK Adhikari, A Dey
    Informatica 48 (12) 2024

  • Analysis of System Response, Energy Savings, and Fault Detection in a Weather and Traffic-Adaptive Smart Lighting System
    T Halder, A Gain, SK Adhikari, B Roy
    SSRG International Journal of Electrical and Electronics Engineering 11 (6 2024

  • Internet of Things-Based Machine Learning in Healthcare Technology and Applications
    P Dey, SK Adhikari, S De, I Kar
    Internet of Things-Based Machine Learning in Healthcare: Technology and 2024

  • Applications of Internet of Things and Machine Learning Technologies in Healthcare
    P Dey, SK Adhikari, S De, R Banerjee
    Internet of Things-Based Machine Learning in Healthcare Technology and 2024

  • Medical image analysis using swarm intelligence: A survey
    SK Adhikari, P Dey, S De, S Paul
    Recent Trends in Swarm Intelligence Enabled Research for Engineering 2024

  • Performance Analysis of Healthcare Information in Big Data NoSql Platform
    SS Mondal, S Mondal, SK Adhikari
    Doctoral Symposium on Intelligence Enabled Research, 235-247 2022

  • Quality Analysis of the Ganges River Water Utilizing Machine Learning Technologies
    P Dey, SK Adhikari, A Gain, S Koner
    Doctoral Symposium on Intelligence Enabled Research, 11-20 2022

  • Applications of big data in various fields: a survey
    SS Mondal, S Mondal, SK Adhikari
    Doctoral Symposium on Intelligence Enabled Research, 221-233 2022

  • Enhanced seismic base isolation using inertial amplifiers. Structures 33: 1340–1353
    S Chowdhury, A Banerjee, S Adhikari
     2021

  • Local contextual information and Gaussian function induced fuzzy clustering algorithm for brain MR image segmentation and intensity inhomogeneity estimation
    N Mahata, S Kahali, SK Adhikari, JK Sing
    Applied Soft Computing 68, 586-596 2018

  • Convolution of 3D Gaussian surfaces for volumetric intensity inhomogeneity estimation and correction in 3D brain MR image data
    S Kahali, SK Adhikari, JK Sing
    IET Computer Vision 12 (3), 288-297 2018

  • A fuzzy clustering algorithm with local contextual information and Gaussian function for simultaneous brain MR image segmentation and intensity inhomogeneity estimation
    N Mahata, S Kahali, JK Sing, SK Adhikari
    2017 2nd International conference on man and machine interfacing (MAMI), 1-6 2017

  • A two-stage fuzzy multi-objective framework for segmentation of 3D MRI brain image data
    S Kahali, SK Adhikari, JK Sing
    Applied Soft Computing 60, 312-327 2017

  • 3D MRI brain image segmentation: A two-stage framework
    S Kahali, SK Adhikari, JK Sing
    Computational Intelligence, Communications, and Business Analytics: First 2017

  • On estimation of bias field in MRI images: polynomial vs Gaussian surface fitting method
    S Kahali, SK Adhikari, JK Sing
    Journal of Chemometrics 30 (10), 602-620 2016

  • Bias field estimation and segmentation of MRI images using a Spatial Fuzzy C-means algorithm
    SK Adhikari, JK Sing, DK Basu
    2016 2nd International Conference on Control, Instrumentation, Energy 2016

  • On estimation of bias field in MRI images
    JK Sing, SK Adhikari, S Kahali
    2015 IEEE International Conference on Computer Graphics, Vision and 2015

  • Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images
    SK Adhikari, JK Sing, DK Basu, M Nasipuri
    Applied soft computing 34, 758-769 2015

  • A spatial fuzzy C-means algorithm with application to MRI image segmentation
    SK Adhikari, JK Sing, DK Basu, M Nasipuri
    2015 Eighth International Conference on Advances in Pattern Recognition 2015

MOST CITED SCHOLAR PUBLICATIONS

  • Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images
    SK Adhikari, JK Sing, DK Basu, M Nasipuri
    Applied soft computing 34, 758-769 2015
    Citations: 218

  • Local contextual information and Gaussian function induced fuzzy clustering algorithm for brain MR image segmentation and intensity inhomogeneity estimation
    N Mahata, S Kahali, SK Adhikari, JK Sing
    Applied Soft Computing 68, 586-596 2018
    Citations: 64

  • A modified fuzzy C‐means algorithm using scale control spatial information for MRI image segmentation in the presence of noise
    JK Sing, SK Adhikari, DK Basu
    Journal of Chemometrics 29 (9), 492-505 2015
    Citations: 42

  • A two-stage fuzzy multi-objective framework for segmentation of 3D MRI brain image data
    S Kahali, SK Adhikari, JK Sing
    Applied Soft Computing 60, 312-327 2017
    Citations: 35

  • A spatial fuzzy C-means algorithm with application to MRI image segmentation
    SK Adhikari, JK Sing, DK Basu, M Nasipuri
    2015 Eighth International Conference on Advances in Pattern Recognition 2015
    Citations: 29

  • A nonparametric method for intensity inhomogeneity correction in MRI brain images by fusion of Gaussian surfaces
    SK Adhikari, JK Sing, DK Basu, M Nasipuri, PK Saha
    Signal, Image and Video Processing 9 (8), 1945-1954 2015
    Citations: 18

  • On estimation of bias field in MRI images: polynomial vs Gaussian surface fitting method
    S Kahali, SK Adhikari, JK Sing
    Journal of Chemometrics 30 (10), 602-620 2016
    Citations: 17

  • Segmentation of MRI brain images by incorporating intensity inhomogeneity and spatial information using probabilistic fuzzy c-means clustering algorithm
    SK Adhikari, JK Sing, DK Basu, M Nasipuri, PK Saha
    2012 International Conference on Communications, Devices and Intelligent 2012
    Citations: 13

  • Conditional spatial fuzzy c-means clustering algorithm with application in MRI image segmentation
    SK Adhikari, JK Sing, DK Basu, M Nasipuri
    Information Systems Design and Intelligent Applications: Proceedings of 2015
    Citations: 9

  • On estimation of bias field in MRI images
    JK Sing, SK Adhikari, S Kahali
    2015 IEEE International Conference on Computer Graphics, Vision and 2015
    Citations: 8

  • Convolution of 3D Gaussian surfaces for volumetric intensity inhomogeneity estimation and correction in 3D brain MR image data
    S Kahali, SK Adhikari, JK Sing
    IET Computer Vision 12 (3), 288-297 2018
    Citations: 6

  • Enhanced seismic base isolation using inertial amplifiers. Structures 33: 1340–1353
    S Chowdhury, A Banerjee, S Adhikari
     2021
    Citations: 5

  • Bias field estimation and segmentation of MRI images using a Spatial Fuzzy C-means algorithm
    SK Adhikari, JK Sing, DK Basu
    2016 2nd International Conference on Control, Instrumentation, Energy 2016
    Citations: 4

  • Applications of big data in various fields: a survey
    SS Mondal, S Mondal, SK Adhikari
    Doctoral Symposium on Intelligence Enabled Research, 221-233 2022
    Citations: 3

  • 3D MRI brain image segmentation: A two-stage framework
    S Kahali, SK Adhikari, JK Sing
    Computational Intelligence, Communications, and Business Analytics: First 2017
    Citations: 3

  • Internet of Things-Based Machine Learning in Healthcare Technology and Applications
    P Dey, SK Adhikari, S De, I Kar
    Internet of Things-Based Machine Learning in Healthcare: Technology and 2024
    Citations: 2

  • Applications of Internet of Things and Machine Learning Technologies in Healthcare
    P Dey, SK Adhikari, S De, R Banerjee
    Internet of Things-Based Machine Learning in Healthcare Technology and 2024
    Citations: et of Things and Machine Learning Technologies in Healthcare

  • Cocoa-Net: Performance Analysis on Classification of Cocoa Beans Using Structural Image Feature
    C Pal, S Das, A Akuli, SK Adhikari, A Dey
    Informatica 48 (12) 2024
    Citations: 1

  • Medical image analysis using swarm intelligence: A survey
    SK Adhikari, P Dey, S De, S Paul
    Recent Trends in Swarm Intelligence Enabled Research for Engineering 2024
    Citations: 1

  • Performance Analysis of Healthcare Information in Big Data NoSql Platform
    SS Mondal, S Mondal, SK Adhikari
    Doctoral Symposium on Intelligence Enabled Research, 235-247 2022
    Citations: 1