Mousumi Gupta

@smu.edu.in

Associate Professor
sikkim manipal institute of technology



              

https://researchid.co/mousumi.g

RESEARCH INTERESTS

Pattern recognition using Digital Image Processing approach .

34

Scopus Publications

Scopus Publications

  • Tracing the COVID-19 spread pattern in India through a GIS-based spatio-temporal analysis of interconnected clusters
    Mousumi Gupta, Arpan Sharma, Dhruva Kumar Sharma, Madhab Nirola, Prasanna Dhungel, Ashok Patel, Harpreet Singh, and Amlan Gupta

    Springer Science and Business Media LLC
    AbstractSpatiotemporal analysis is a critical tool for understanding COVID-19 spread. This study examines the pattern of spatial distribution of COVID-19 cases across India, based on data provided by the Indian Council of Medical Research (ICMR). The research investigates temporal patterns during the first, second, and third waves in India for an informed policy response in case of any present or future pandemics. Given the colossal size of the dataset encompassing the entire nation’s data during the pandemic, a time-bound convenience sampling approach was employed. This approach was carefully designed to ensure a representative sample from advancing timeframes to observe time-based patterns in data. Data were captured from March 2020 to December 2022, with a 5-day interval considered for downloading the data. We employ robust spatial analysis techniques, including the Moran’s I index for spatial correlation assessment and the Getis Ord Gi* statistic for cluster identification. It was observed that positive COVID-19 cases in India showed a positive auto-correlation from May 2020 till December 2022. Moran’s I index values ranged from 0.11 to 0.39. It signifies a strong trend over the last 3 years with $$r^2$$ r 2 of 0.74 on order 3 polynomial regression. It is expected that high-risk zones can have a higher number of cases in future COVID-19 waves. Monthly clusters of positive cases were mapped through ArcGIS software. Through cluster maps, high-risk zones were identified namely Kerala, Maharashtra, New Delhi, Tamil Nadu, and Gujarat. The observation is: high-risk zones mostly fall near coastal areas and hotter climatic zones, contrary to the cold Himalayan region with Montanne climate zone. Our aggregate analysis of 3 years of COVID-19 cases suggests significant patterns of interconnectedness between the Indian Railway network, climatic zones, and geographical location with COVID-19 spread. This study thereby underscores the vital role of spatiotemporal analysis in predicting and managing future COVID-19 waves as well as future pandemics for an informed policy response.

  • Revolutionizing Colon Histopathology Glandular Segmentation Using an Ensemble Network With Watershed Algorithm
    Bijoyeta Roy, Mousumi Gupta, and Bidyut Krishna Goswami

    Wiley
    ABSTRACTColorectal adenocarcinoma, the most prevalent form of colon cancer, originates in the glandular structures of the intestines, presenting histopathological abnormalities in affected tissues. Accurate gland segmentation is crucial for identifying these potentially fatal abnormalities. While recent methodologies have shown success in segmenting glands in benign tissues, their efficacy diminishes when applied to malignant tissue segmentation. This study aims to develop a robust learning algorithm using a convolutional neural network (CNN) to segment glandular structures in colon histology images. The methodology employs a CNN based on the U‐Net architecture, augmented by a weighted ensemble network that integrates DenseNet 169, Inception V3, and Efficientnet B3 as backbone models. Additionally, the segmented gland boundaries are refined using the watershed algorithm. Evaluation on the Warwick‐QU dataset demonstrates promising results for the ensemble model, by achieving an F1 score of 0.928 and 0.913, object dice coefficient of 0.923 and 0.911, and Hausdorff distances of 38.97 and 33.76 on test sets A and B, respectively. These results are compared with outcomes from the GlaS challenge (MICCAI 2015) and existing research findings. Furthermore, our model is validated with a publicly available dataset named LC25000, and visual inspection reveals promising results, further validating the efficacy of our approach. The proposed ensemble methodology underscores the advantages of amalgamating diverse models, highlighting the potential of ensemble techniques to enhance segmentation tasks beyond individual model capabilities.



  • Digital colposcopy image analysis techniques requirements and their role in clinical diagnosis: a systematic review
    Parimala Tamang, Mousumi Gupta, and Annet Thatal

    Informa UK Limited
    INTRODUCTION Colposcopy is a medical procedure for detecting cervical lesions. Access to devices required for colposcopy procedures is limited in low- and middle-income countries. However, various existing digital imaging techniques based on artificial intelligence offer solutions to analyze colposcopy images and address accessibility challenges. METHODS We systematically searched PubMed, National Library of Medicine, and Crossref, which met our inclusion criteria for our study. Various methods and research gaps are addressed, including how variability in images and sample size affect the accuracy of the methods. The quality and risk of each study were assessed following the QUADAS-2 guidelines. RESULTS Development of image analysis and compression algorithms, and their efficiency are analyzed. Most of the studied algorithms have attained specificity, sensitivity, and accuracy which range from 86% to 95%, 75%-100%, and 100%, respectively, and these results were validated by the clinician to analyze the images quickly and thus minimize biases among the clinicians. CONCLUSION This systematic review provides a comprehensive study on colposcopy image analysis stages and the advantages of utilizing digital imaging techniques to enhance image analysis and diagnostic procedures and ensure prompt consultations. Furthermore, compression techniques can be applied to send medical images over media for further analysis among periphery hospitals.

  • 3D reconstruction of light microscopic images and its significance for better clinical decisions: A systematic review emphasizing gastric histopathology
    Bijoyeta Roy, Mousumi Gupta, and Bidyut Krishna Goswami

    Institute of Electrical and Electronics Engineers (IEEE)


  • An Enhanced Security in Medical Image Encryption Using Dynamic Chaotic Fuzzy Based Technique
    Snehashish Bhattacharjee, Mousumi Gupta, and Biswajoy Chatterjee

    EJournal Publishing
    As IoT and cloud computing have grown in popularity, medical images are now often transmitted between devices or accessed directly from the cloud. With this, the security is always a concern as these images are prone to many types of attack. We have proposed a proven method that is efficient in terms of security, time complexity, and integrity in order to be cloud-friendly so that it may be launched into the cloud and made accessible to users at any time. The goal of the work is to create a dynamic key that, depending on fuzzy values, alters the reproduction rate parameters with each repetition. By applying the last chaotic value created from the previous iteration, the fuzzy triangular membership function has been used in this manner to generate the reproduction rate parameter. The uniqueness and major benefit of the suggested strategy are that it can increase the security of the algorithm that makes use of a chaotic map and a static key. The method has been put forth when designing algorithms so that it should not only demonstrate security against different attacks but also provide efficiency towards computational complexity. The technique has been tested against a set of images and an existing algorithm using a variety of security metrics, including the correlation coefficient, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and entropy. It has been determined from the comparative analysis that the proposed approach can make the existing algorithm more secure.


  • Thickness and Mass of East Rathong Glacier (ERG), West Sikkim, India, Estimated Using GIS
    Arpan Sharma, Mousumi Gupta, Narpati Sharma, and Santanu Gupta

    Horizon Research Publishing Co., Ltd.

  • An Enhanced Security in Medical Image Encryption Based on Multi-level Chaotic DNA Diffusion
    Mousumi Gupta, Snehashish Bhattacharjee, and Biswajoy Chatterjee

    EJournal Publishing
    A novel medical image encryption technique has been proposed based on the features of DNA encodingdecoding in combination with Logistic map approach. The approach is proven for encryption of highly sensitive medical images with 100 percent integrity or negligible data loss. Testing is done on both high and low-resolution images. Proposed encryption technique consists of two levels of diffusion using the actual structure of the DNA. In the first level of diffusion process, we have used DNA encoding and decoding operations to generate DNA sequence of each pixel. The originality of the work is to use a long DNA structure stored in a text file stored on both sender and receiver’s end to improve the performance of the proposed method. In this initial level of diffusion, DNA sequences are generated for each pixe-land in each of the DNA sequence. Index values are obtained by employing a search operation on the DNA structure. This index values are further modified and ready to be used for next diffusion process. In the second level diffusion, a highly chaotic logistic map is iterated to generate sequences and is employed to extract the chaotic values to form the cipher images. The correlation coefficient analysis, Histogram analysis, Entropy analysis, NPCR, and UACI exhibit significant results. Therefore; the proposed technique can play an important role in the security of low-resolution medical images as well as other visible highly sensitive images.

  • Time Efficient Image Encryption-Decryption for Visible and COVID-19 X-ray Images Using Modified Chaos-Based Logistic Map
    Snehashish Bhattacharjee, Mousumi Gupta, and Biswajoy Chatterjee

    Springer Science and Business Media LLC

  • Fractal Dimension-Based Infection Detection in Chest X-ray Images
    Sujata Ghatak, Satyajit Chakraborti, Mousumi Gupta, Soumi Dutta, Soumen Kumar Pati, and Abhishek Bhattacharya

    Springer Science and Business Media LLC


  • Automated Nuclei Analysis from Digital Histopathology
    Bijoyeta Roy, Pratima Sarkar, and Mousumi Gupta

    IEEE
    In Histopathology analysis, an abnormal nuclear shape can be a strong parameter to detect malignancy. Similarly, by visualizing the growing amount of nuclei implies disease status such as grading of cancer. In clinical diagnosis there is a link between texture of the nucleus and disease current status. In digital pathology the most crucial & important step for computer-aided diagnostics system is automated cell nuclei segmentation. Regular studies are going on digital pathology for automatic analyzing the nuclei cell images. However, providing accurate cell nucleus segmentation rate through automation is still poor. Scientists are rigorously working on the development of computerized segmentation algorithm to provide faster diagnosis. This paper proposed a novel thresholding based unsupervised technique which is applicable to detect nuclei contours. Proposed approach is applied to seven multi organ histological tissue images. Threshold is calculated by taking into consideration the first nonzero gray level pixel value followed by finding the pixel connectivity with 8 neighborhood concept. Furthermore, the various nuclei feature including mean intensity, diameter, perimeter, centroid is computed to get better insight to cell functionality providing a support in early diagnosis to diseases. When compare with traditional adaptive thresholding this approach achieved better performance rate by 48.31 %.

  • Melanoma Cell Detection by Using K-means Clustering Segmentation and Abnormal Cell Detection Technique
    Pratima Sarkar, Bijoyeta Roy, Mousumi Gupta, and Sourav De

    Springer Nature Singapore

  • Preface
    DAVID ABIR

    Elsevier

  • Assessment of Blood Donor Information Using Kernel Density Maps: A Case Study at Gangtok District, Sikkim and Modeling of a Web Application.
    Madhab Nirola, Mousumi Gupta, and Arpan Sharma

    IEEE
    Acquiring blood donor information is a very time-consuming process and may put the patient’s health at risk. Finding blood donor data is essential for receiving the correct type of blood as quickly as possible. This study aims to develop a web application to alleviate the hassles faced by critical patients. We acquire sample data from CRH hospital in Gangtok, Sikkim. We assess data from blood donors using ArcGIS platforms. Further, we generate a region-wise heat map. We include 391 blood donor records of which 165 are A+ Ve, 94 are B+ Ve, 43 are AB+ Ve, 3 are B- Ve, and 86 were O+ Ve. Through the modeled application users can locate blood donor information near them and communicate with the blood donor by direct phone call using the assessed heatmaps. Users can also register themselves by providing information such as their personal information, blood type, etc., which will be displayed on the heatmap for future use. Through the modeled application blood banks in that area can Figure out which locations have a higher volume of a specific blood type that they require, and then set up a camp or encourage volunteers to donate blood to help.

  • Target Detection from Brain MRI and Its Classification
    Bijoyeta Roy, Mousumi Gupta, Abhishek Kumar, and Sweta

    Springer Singapore

  • Enhancement of foveolar architectural changes in gastric endoscopic biopsies
    Mousumi Gupta, Om Prakash Dhakal, and Amlan Gupta

    Elsevier

  • MRI Image Reconstruction Through Contour Interpolation
    Bijoyeta Roy, Shivank Goel, and Mousumi Gupta

    Springer International Publishing

  • Tracking Suspicious User Behavior Through Hybrid Feature Selection Technique
    Anusree Roy and Mousumi Gupta

    Springer International Publishing

  • Preface
    A.F. KONAR and E. ADALI

    Elsevier

  • Macroscopic Reconstruction for Histopathology Images: A Survey
    Bijoyeta Roy and Mousumi Gupta

    Springer Singapore
    Over the past decade, due to a dramatic increase in diseases, histopathology has become a vital part of medical science. Histopathology involves examining of tissue under a microscope to examine the manifestation of diseases. The term histology deals with the study of entire cells of the sample specimen, whereas histopathology is a technique, which deals with analyzing for microscopic changes or abnormalities in tissues that are caused as a result of diseases. With the computational advancement, tissue histopathology slides can be digitized with the help of whole slide digital scanners. Histopathology is a powerful diagnosis method to analyze the tissue structure. However, the microscopic images generally produce two-dimensional views. With the advent of new computer algorithms, 3D reconstruction from 2D histopathology images is a routine technique. However, the already developed algorithms exhibit distorted image and recur information loss, which is unacceptable for the pathologists. An accurate high-resolution 3D reconstruction for histopathology sections will lead to a better diagnosis. This paper describes the 3D visualization techniques and its applicability in histopathology.

  • A survey on: Facial emotion recognition invariant to pose, illumination and age
    Saswati Bhattacharya and Mousumi Gupta

    IEEE
    Facial Emotion Recognition (FER) gained considerable interest among scientists working in areas like medical diagnosis, forensics and communication technology. Numerous studies have already been conducted on facial emotion recognition (FER) but none seems to deal with pose, illumination and age invariance simultaneously. This paper reviews several challenges for pose, illumination and age invariance for FER. Here we also provide a survey on deep learning methodologies used in FER. It is noted that deep learning algorithms provide greater accuracy than all other algorithms previously used.