SUDIP KUMAR ADHIKARI
@cgec.org.in
Assistant Professor of Department of Computer Science & Engineering
COOCH BEHAR GOVERNMENT ENGINEERING COLLEGE
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
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Sudip Kumar Adhikari, Prasenjit Dey, Sourav De, and Shouvik Paul
Elsevier
Nabanita Mahata, Sayan Kahali, Sudip Kumar Adhikari, and Jamuna Kanta Sing
Elsevier BV
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.
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.
Sayan Kahali, Sudip Kumar Adhikari, and Jamuna Kanta Sing
Elsevier BV
Sayan Kahali, Sudip Kumar Adhikari, and Jamuna Kanta Sing
Springer Singapore
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.
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.
Jamuna Kanta Sing, Sudip Kumar Adhikari, and Sayan Kahali
IEEE
Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, Mita Nasipuri, and Punam Kumar Saha
Springer Science and Business Media LLC
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.
Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, and Mita Nasipuri
Elsevier BV
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.
Sudip Kumar Adhikari, Jamuna Kanta Sing, Dipak Kumar Basu, and Mita Nasipuri
Springer India
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.