Jyotsna Kumar Mandal Debika Bhattacharya    Editors
Emerging Technology in Modelling and Graphics Proceedings of IEM Graph 2018
Advances in Intelligent Systems and Computing
Volume 937
Series Editor
Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Advisory Editors
Nikhil R. Pal, Indian Statistical Institute, Kolkata, India Rafael Bello Perez, Faculty of Mathematics, Physics and Computing, Universidad Central de Las Villas, Santa Clara, Cuba Emilio S. Corchado, University of Salamanca, Salamanca, Spain Hani Hagras, School of Computer Science & Electronic Engineering, University of Essex, Colchester, UK László T. Kóczy, Department of Automation, Széchenyi István University, Gyor, Hungary Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA Chin-Teng Lin, Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan Jie Lu, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia Patricia Melin, Graduate Program of Computer Science, Tijuana Institute of Technology, Tijuana, Mexico Nadia Nedjah, Department of Electronics Engineering, University of Rio de Janeiro, Rio de Janeiro, Brazil Ngoc Thanh Nguyen, Faculty of Computer Science and Management, Wrocaw University of Technology, Wrocaw, Poland Jun Wang, Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
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Emerging Technology in Modelling and Graphics Proceedings of IEM Graph 2018
123
Editors Jyotsna Kumar Mandal Department of Computer Science and Engineering University of Kalyani Kalyani, West Bengal, India
Debika Bhattacharya Department of Computer Science and Engineering Institute of Engineering and Management Kolkata, West Bengal, India
ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing ISBN 978-981-13-7402-9 ISBN 978-981-13-7403-6 (eBook) https://doi.org/10.1007/978-981-13-7403-6
© Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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The 1st International Conference IEMGraph 2018—International Conference on Emerging Technology in Modelling and Graphics was held on 6–7 September 2018 in Kolkata, India. IEMGraph 2018 was an international and interdisciplinary con- ference covering research and development in the field of emerging technologies in intelligent systems and computing. It addressed all trending research topics and the latest research in emerging technology in modelling and graphics including image processing and analysis, image segmentation, digital geometry for computer imaging, image and security, biometrics, video processing, medical imaging, and virtual and augmented reality.
More than 250 pre-registered authors submitted their work in this conference. IEMGraph 2018 finally accepted and hosted 70 papers after a double-blind peer review process. The conference technical committee with the contribution of competent and expert reviewers decided about the acceptance of the submitted papers.
One of the primary objectives of IEMGraph 2018 was the investigation of information-based technological change and its adaptation in different industries and academic world. The conference tried to bridge the gap between industry demand and academic supply in research fields to address that demand. This annual event was addressed jointly to academics and practitioners and provided a forum for a number of perspectives based on either theoretical analyses or empirical case studies that foster the exchange of ideas. The conference offered a number of sessions under its patronage that was a valuable resource for scholars and practi- tioners, and also the conference workshop was organized to give hands-on expe- rience to students in image processing, graphical modelling, and AI-related fields.
We would like to thank all participated in any way in the IEMGraph 2018 and team members of organizing committee and other committees for organizing the conference successfully. Also, thanks to the contributors of this volume for con- tributing their articles for publication. We express our sincere gratitude to the famous publication house Springer for their communication sponsorship and the
v
co-organizer “Indian Space Research Organisation (ISRO)”, India, for their tech- nical sponsorship, and to the members of the technical committee who provided a significant contribution to the review of papers and all members of the organizing committee for their help, support, and spirit participation before, during, and after the conference.
This volume will be a state-of-the-art material for researchers, engineers, and students.
Kalyani, India Jyotsna Kumar Mandal Kolkata, India Debika Bhattacharya
vi Preface
An Automated Segmentation Approach from Colored Retinal Images for Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Suchismita Goswami, Sushmita Goswami, Shubhasri Roy, Shreejita Mukherjee and Nilanjana Dutta Roy
Epileptic Seizure Recognition Using Deep Neural Network . . . . . . . . . . 21 Anubhav Guha, Soham Ghosh, Ayushi Roy and Sankhadeep Chatterjee
Graph-Based Supervised Feature Selection Using Correlation Exponential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Gulshan Kumar, Gitesh Jain, Mrityunjoy Panday, Amit Kumar Das and Saptarsi Goswami
A Sentiment-Based Hotel Review Summarization . . . . . . . . . . . . . . . . . . 39 Debraj Ghosh
Heuristic Approach for Finding Threshold Value in Image Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Sandip Mal and Ashiwani Kumar
An Educational Chatbot for Answering Queries . . . . . . . . . . . . . . . . . . 55 Sharob Sinha, Shyanka Basak, Yajushi Dey and Anupam Mondal
A Supervised Approach to Analyse and Simplify Micro-texts . . . . . . . . 61 Vaibhav Chaturvedi, Arunangshu Pramanik, Sheersendu Ghosh, Priyanka Bhadury and Anupam Mondal
vii
A Short Review on Different Clustering Techniques and Their Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Attri Ghosal, Arunima Nandy, Amit Kumar Das, Saptarsi Goswami and Mrityunjoy Panday
An Efficient Descriptor for Gait Recognition Using Spatio-Temporal Cues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Sanjay Kumar Gupta, Gaurav Mahesh Sultaniya and Pratik Chattopadhyay
Supervised Classification Algorithms in Machine Learning: A Survey and Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Pratap Chandra Sen, Mahimarnab Hajra and Mitadru Ghosh
Breast Cancer Diagnosis Using Image Processing and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Subham Sadhukhan, Nityasree Upadhyay and Prerana Chakraborty
Using Convolutions and Image Processing Techniques to Segment Lungs from CT Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Souvik Ghosh, Sayan Sil, Rohan Mark Gomes and Monalisa Dey
Analyzing Code-Switching Rules for English–Hindi Code-Mixed Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Sainik Kumar Mahata, Sushnat Makhija, Ayushi Agnihotri and Dipankar Das
Detection of Pulsars Using an Artificial Neural Network . . . . . . . . . . . . 147 Rajarshi Lahiri, Souvik Dey, Soumit Roy and Soumyadip Nag
Modification of Existing Face Images Based on Textual Description Through Local Geometrical Transformation . . . . . . . . . . . . 159 Mrinmoyi Pal, Subhajit Ghosh and Rajnika Sarkar
A Neural Network Framework to Generate Caption from Images . . . . . 171 Ayan Ghosh, Debarati Dutta and Tiyasa Moitra
Remote Sensing and Advanced Encryption Standard Using 256-Bit Key . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Sumiran Naman, Sayari Bhattacharyya and Tufan Saha
Grasp-Pose Prediction for Hand-Held Objects . . . . . . . . . . . . . . . . . . . . 191 Abhirup Das, Ayon Chattopadhyay, Firdosh Alia and Juhi Kumari
A Multi-level Polygonal Approximation-Based Shape Encoding Framework for Automated Shape Retrieval . . . . . . . . . . . . . . . . . . . . . . 203 Sourav Saha, Soumi Bhunia, Laboni Nayak, Rebeka Bhattacharyya and Priya Ranjan Sinha Mahapatra
viii Contents
A Hand Gesture Recognition Model Using Fuzzy Directional Encoding of Polygonal Approximation . . . . . . . . . . . . . . . . . . . . . . . . . . 217 Sourav Saha, Soma Das, Shubham Debnath and Sayantan Banik
Signature-Based Data Reporting in Wireless Sensor Networks . . . . . . . 231 Monika Bhalla and Ajay Sharma
Wine Quality Analysis Using Machine Learning . . . . . . . . . . . . . . . . . . 239 Bipul Shaw, Ankur Kumar Suman and Biswarup Chakraborty
Generalized Smart Traffic Regulation Framework with Dynamic Adaptation and Prediction Logic Using Computer Vision . . . . . . . . . . . 249 Vishal Narnolia, Uddipto Jana, Soham Chattopadhyay and Shramana Roy
A Short Review on Applications of Big Data Analytics . . . . . . . . . . . . . 265 Ranajit Roy, Ankur Paul, Priya Bhimjyani, Nibhash Dey, Debankan Ganguly, Amit Kumar Das and Suman Saha
A Survey of Music Recommendation Systems with a Proposed Music Recommendation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 Dip Paul and Subhradeep Kundu
Interactive Systems for Fashion Clothing Recommendation . . . . . . . . . . 287 Himani Sachdeva and Shreelekha Pandey
Embedded Implementation of Early Started Hybrid Denoising Technique for Medical Images with Optimized Loop . . . . . . . . . . . . . . . 295 Khakon Das, Mausumi Maitra, Minakshi Banerjee and Punit Sharma
Stress Profile Analysis in n-FinFET Devices . . . . . . . . . . . . . . . . . . . . . . 309 T. P. Dash, S. Das, S. Dey, J. Jena and C. K. Maiti
An Annotation System to Annotate Healthcare Information from Tweets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Nixon Dutta, Anupam Mondal and Pritam Paul
Broad Neural Network for Change Detection in Aerial Images . . . . . . . 327 Shailesh Shrivastava, Alakh Aggarwal and Pratik Chattopadhyay
User-Item-Based Hybrid Recommendation System by Employing Mahout Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 Sutanu Paul and Dipankar Das
Gray Matter Segmentation and Delineation from Positron Emission Tomography (PET) Image . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 Abhishek Bal, Minakshi Banerjee, Punit Sharma and Mausumi Maitra
A Joint Image Compression–Encryption Algorithm Based on SPIHT Coding and 3D Chaotic Map . . . . . . . . . . . . . . . . . . . . . . . . . 373 Ramkrishna Paira
Contents ix
Improved Multi-feature Computer Vision for Video Surveillance . . . . . 383 Ashutosh Upadhyay and Jeevanandam Jotheeswaran
Topic Modeling for Text Classification . . . . . . . . . . . . . . . . . . . . . . . . . . 395 Pinaki Prasad Guha Neogi, Amit Kumar Das, Saptarsi Goswami and Joy Mustafi
Low Frequency Noise Analysis in Strained-Si Devices . . . . . . . . . . . . . . 409 Sanghamitra Das, Tara Prasanna Dash and Chinmay Kumar Maiti
Employing Cross-genre Unstructured Texts to Extract Entities in Adapting Sister Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419 Promita Maitra and Dipankar Das
Relation Extraction from Cross-Genre Unstructured Text . . . . . . . . . . . 433 Promita Maitra and Dipankar Das
An Efficient Algorithm for Detecting and Measure the Properties of Pothole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 Amitava Choudhury, Rohit Ramchandani, Mohammad Shamoon, Ankit Khare and Keshav Kaushik
Quantitative Interpretation of Cryptographic Algorithms . . . . . . . . . . . 459 Aditi Jha and Shilpi Sharma
Ransomware Attack: India Issues Red Alert . . . . . . . . . . . . . . . . . . . . . 471 Simran Sabharwal and Shilpi Sharma
An Automated Dual Threshold Band-Based Approach for Malaria Parasite Segmentation from Thick Blood Smear . . . . . . . . . . . . . . . . . . 485 Debapriya Paul, Nilanjan Daw, Nilanjana Dutta Roy and Arindam Biswas
A Secured Biometric-Based Authentication Scheme in IoT-Based Patient Monitoring System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501 Sushanta Sengupta
Automatic Facial Expression Recognition Using Geometrical Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519 Tanmoy Banerjee, Sayantan De, Sampriti Das, Susmit Sarkar and Spandan Swarnakar
A Review on Different Image De-hazing Methods . . . . . . . . . . . . . . . . . 533 Sweta Shaw, Rajarshi Gupta and Somshubhra Roy
An OverView of Different Image Algorithms and Filtering Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 Sumit Prakash, Abhas Somya and Ayush Kumar Rai
Design of a Quantum One-Way Trapdoor Function . . . . . . . . . . . . . . . 547 Partha Sarathi Goswami and Tamal Chakraborty
x Contents
Relation Estimation of Packets Dropped by Wormhole Attack to Packets Sent Using Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . 557 Sayan Majumder and Debika Bhattacharyya
A Review on Agricultural Advancement Based on Computer Vision and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 567 Abriti Paul, Sourav Ghosh, Amit Kumar Das, Saptarsi Goswami, Sruti Das Choudhury and Soumya Sen
Transformation of Supply Chain Provenance Using Blockchain—A Short Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 Sekhar Kumar Roy, Runa Ganguli and Saptarsi Goswami
A Framework for Predicting and Identifying Radicalization and Civil Unrest Oriented Threats from WhatsApp Group . . . . . . . . . . 595 Koushik Deb, Souptik Paul and Kaustav Das
Flower Pollination Algorithm-Based FIR Filter Design for Image Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607 Supriya Dhabal, Srija Chakraborty and Prosenjit Sikdar
Image Enhancement Using Differential Evolution Based Whale Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 619 Supriya Dhabal and Dip Kumar Saha
Advanced Portable Exoskeleton with Self-healing Technology Assisted by AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629 Piyush Keshari and Santanu Koley
Crosstalk Minimization as a High-Performance Factor in Three-Layer Channel Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645 Sumanta Chakraborty
A Human Intention Detector—An Application of Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659 Megha Dutta, Shayan Mondal, Sanjay Chakraborty and Arpan Chakraborty
Survey on Applications of Machine Learning in the Field of Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667 Himanshu Shekhar, Sujoy Seal, Saket Kedia and Amartya Guha
Automatic Speech Recognition Based on Clustering Technique . . . . . . . 679 Saswati Debnath and Pinki Roy
A Study of Interrelation Between Ratings and User Reviews in Light of Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 689 Pritam Mondal, Amlan Ghosh, Abhirup Sinha and Saptarsi Goswami
Contents xi
A Study on Spatiotemporal Topical Analysis of Twitter Data . . . . . . . . 699 Lalmohan Dutta, Giridhar Maji and Soumya Sen
A Secure Steganography Scheme Using LFSR . . . . . . . . . . . . . . . . . . . . 713 Debalina Ghosh, Arup Kumar Chattopadhyay, Koustav Chanda and Amitava Nag
Crowd Behavior Analysis and Alert System Using Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721 Sayan Dutta, Sayan Burman, Agnip Mazumdar and Nilanjana Dutta Roy
Review Article on Magnetic Resonance Imaging . . . . . . . . . . . . . . . . . . 731 Shatadru Majumdar, Rashmita Roy, Madhurima Sen and Mahima Chakraborty
An Intelligent Traffic Light Control System Based on Density of Traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741 Kriti Dangi, Manish Singh Kushwaha and Rajitha Bakthula
Scheduling in Cloud Computing Environment using Metaheuristic Techniques: A Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 753 Harvinder Singh, Sanjay Tyagi and Pardeep Kumar
Crown Detection and Counting Using Satellite Images . . . . . . . . . . . . . 765 Rebeka Bhattacharyya and Avijit Bhattacharyya
Hand Segmentation from Complex Background for Gesture Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775 Soumi Paul, Arpan Bhattacharyya, Ayatullah Faruk Mollah, Subhadip Basu and Mita Nasipuri
A Study on Content Selection and Cost-Effectiveness of Cognitive E-Learning in Distance Education of Rural Areas . . . . . . . . . . . . . . . . . 783 Anindita Chatterjee, Kushal Ghosh and Biswajoy Chatterjee
An Automatic Method for Bifurcation Angle Calculation in Retinal Fundus Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 787 Suchismita Goswami and Sushmita Goswami
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 797
About the Editors
Jyotsna Kumar Mandal, M.Sc.(Ph.), JU, M.Tech.(CS), CU, Ph.D.(Eng.), JU Professor CSE, is former Dean of the FETM, and was KU for two consecutive terms. He has nearly 30 years of teaching and research experience, and has com- pleted four AICTE and one state government project. He is a life member of the CSI, CRSI, a member of the ACM, and fellow of the IETE. He was also honorary vice chairman and chairman of the CSI. He has delivered over 100 lectures and organized more than 25 national and international conferences. He currently serves as an editorial board member and corresponding editor for the Proceedings of Science Direct, IEEE and other conferences, as well as guest editor of the MST Journal. He has published more than 400 research articles and six books.
Dr. Debika Bhattacharya obtained her B.Tech. and M.Tech. in Radiophysics and Electronics from Calcutta University, and her Ph.D. in Electronics and Telecom- munication from Jadavpur University. She is currently Dean (of Academics) at the Institute of Engineering andManagement. She hasmore than 22 years of teaching and research experience, has publishedmany papers in prominent journals and conference proceedings, and has completed 2AICTE and 1DST project. In 2018 she received the Venus International Foundation’s “Distinguished Leader” award in engineering.
xiii
Arushi Agarwal, Pratibha Chaudhary, Rana Majumdar, Sunil Kumar Chowdhary and Abhishek Srivastava
Abstract Noise pollution is escalating at an alarming rate as a one of the critical outcomes of urbanization. This led to harmful effect on the health of human being as it can cause annoyance, hypertension, heart disease, and sleep disturbances. Despite all measures to control noise pollution that have been taken in Mumbai so far, those are prone to vulnerabilities. The differences in these vulnerability-inducing causes arise a need for an effective analysis. Themotive of this paper is to have datamining to come to aid to create a model that provides the heterogeneity of the data by grouping similar objects together to find the noise pollution regions in the Mumbai state with respect to different factors.
Keywords Data mining · Cluster · Noise pollution · Root nodes
1 Introduction
From past few years, industries have been extended and numbers of transport vehi- cles on the roads have been increased due to rapid urbanization. These are the sources of environmental noise pollution. Noise pollution led to harmful effect on the phys- iological and physical health of human being. Noise pollution can cause annoyance,
A. Agarwal · P. Chaudhary · R. Majumdar (B) · S. K. Chowdhary · A. Srivastava Amity School of Engineering and Technology, Amity University, Noida, Uttar Pradesh, India e-mail: rana.majumdarwb@gmail.com
A. Agarwal e-mail: arushiagwl@gmail.com
P. Chaudhary e-mail: pratibha.chaudhary4991@gmail.com
A. Srivastava e-mail: asrivastava8@amity.edu
© Springer Nature Singapore Pte Ltd. 2020 J. K. Mandal and D. Bhattacharya (eds.), Emerging Technology in Modelling and Graphics, Advances in Intelligent Systems and Computing 937, https://doi.org/10.1007/978-981-13-7403-6_1
1
hypertension, heart disease, and sleep disturbances.As per the report byWHO(World Health Organization), several cities had exceeded the acceptable level of noise pol- lution. In India, in 2011, Central Pollution Control Board (CPCB) [1] has been set up for Real-time National Ambient Noise Monitoring Network that includes nine cities (Delhi, Lucknow, Chennai,Mumbai, NaviMumbai, Thane, Kolkata, Hyderabad, and Bangalore). According to the statistical report generated by CPCB [1], Mumbai is on the top that exceeded maximum limits of prescribed noise level. Particularly, amid Ganeshotsav, the highest levels of noise are recorded on this day.
According to WHO standard [2], the community noise should not be more than 30 A-weighted decibels (dB (A), dB is used to measure noise) for sleeping at night and not be more than 35 dB (A) in classrooms for studying. The night noise should not be more than 40 dB (A) of annual average (light) of bedrooms to avoid adverse effects on health from night noise.
The main objective of this paper is to study the several regions of Mumbai against the contributing causes and draw conclusion in order to control noise pollution in the state. In this research work, we perform clustering techniques to group regions and identify causes of health risks in regions.
2 Literature Survey
The regular exposure to increased sound levels that may have adverse effects on the lives of humans or other living beings is called noise pollution. According to WHO study [1], sound levels should not be more than 70 db. Then, it cannot harm the living organisms. Exposure for 8 h and more to constant noise higher than 85 dB may be calamitous.
Noise free is thought to be a fundamental, essential of human well-being and prosperity. However, noise pollution is becoming a calamity to prosperity around the globe (WHO 2011). The study of noise pollution reveals that the effects of noise pollution are also hazardous as compare to other types of pollution, not only in Mumbai regions, but across the world. Many strategies have been introduced to monitor noise pollution in smart cities for different noise quality levels. The analysis also concluded that many measures have taken to reduce the effects and causes of noise pollution. There are many different techniques, along with data mining that can be used to monitor and analyze the noise pollution data. Table 1 summarizes some of the research studies where various techniques are used to understand its implication in noise pollution.
Clustering helps in the formation of data sets of similar type of group structures, thus dividing and arranging the data into subclasses. In this paper, the clustering technique used is expectation-maximization (EM) clustering using Gaussian mix- ture models (GMM). The data points are assumed to be in multivariate Gaussian distribution surface. Mean and standard deviation are the two important parameters describing the shape of the clusters. A single cluster is assigned an each Gaussian
Identification of Noise Pollution Prone Regions in Mumbai … 3
Table 1 Techniques used S. No. Author Research Technique
1. Panu Maijala (2018) [5]
Monitoring noise sources
3 Problem Conceptualizations
Mumbai region is one among the top five cities in India which have higher impact on lives due to the adverse effects created by the noise pollution. A certain high level of noise is also a deadly cause affecting the lives of people living in Mum- bai. Many noise-monitoring schemas and noise-controlling plans have been directed by the authorities, but the implementation of the tasks hadn’t created much differ- ence to resolve the issue of noise pollution. We hardly find any silent zones in the Mumbai region which are safe away from the hands of noise pollution, as Central Pollution Control Board (CPCB) has also revealed that areas around hospitals, edu- cational intuitions, courts, etc. didn’t fall in the silence zones within the range of 100 m. In this paper, we have proposed a process which can be adopted for the analysis and identification of the highly prone noise pollution-affected regions in Mumbai using data mining approach of clustering techniques called expectation- maximization (EM) clustering using Gaussian mixture models (GMM), and also a decision tree can be made which shows the classification of the major causes of the noise pollution in the Mumbai region.
4 Proposed Methodology Model
The proposed methodology model is depicted in Fig. 1, and the flow of execution is from top to bottom. The steps involved are described in the following flowchart.
4 A. Agarwal et al.
Fig. 1 Flowchart of applied techniques
4.1 Gathering of the Data from CPCB Official Website
The sample of noise monitoring data set of Mumbai for the year 2014 is collected from [3]. The data set consists of different regions of Mumbai affected by noise pollution in day time as well as night time. Figure 2 represents the chosen data set.
4.2 Treating and Cleaning of the Missing Values in Data Set
This is preprocessing step which involves the steps to remove the missing values (i.e., noisy values) from the data set, which in turn reduces the set of data (cleaning of data). Max–min normalization is also calculated, which converts the data in the normalized form for each year and combines the data into the Excel sheet.
4.3 Using Clustering Technique (Expectation-Maximization Clustering Using Gaussian Mixture Models)
The clustering technique to group similar object used is expectation-maximization clustering using Gaussian mixture models that is described in Fig. 3.
The data mining approach we proposed in this paper, used to form the clusters of the affected regions of Mumbai due to noise pollution, is expectation-maximization
Identification of Noise Pollution Prone Regions in Mumbai … 5
Fig. 2 The sample data set for several regions of Mumbai
Fig. 3 Steps of clustering technique
6 A. Agarwal et al.
(EM) clustering using Gaussian mixture models (GMM). The symbols used in the equations are described in the table. The steps involved are as follows:
1. Estimating Parameters (mean and covariance): Using the multivariate Gaussian distribution, we estimate the parameters (mean and covariance) of a distribution. In this step, the clusters of data are predicted to have an independent Gaussian distribution, each having their ownmean and covariance matrices. The estimated parameters are as:
N (x |μ,Σ) = 1
} (1)
) (2)
2. Expectation-Maximization: It is an iterative technique, which includes expec- tation and maximization step (explained in next steps). The expectation- maximization algorithm is used for the generation that maximizes the proba- bility of output data of means and variance, for the distributional parameters of multi-mode data. The techniques involve the probabilistic models.
3. Expectation Step: In this step, we assign each point to a cluster. Using the esti- mated mean and covariance, we can predict and calculate the probability of each data point belonging to each cluster.We can calculate the fixed, data-independent parameters. Thus, for already calculated parameters we can calculate the approx- imation values of latent variable. The formula for depicting the responsibility (calculating data point for each cluster) in expectation step is:
ric = πcN (xi |μc, σc)∑ j∈[0,k} π j N
( xi |μ j , σ j
) (3)
Suppose, if we have 80 data points with a mixture of 4 Gaussians, we need to calculate 320 numbers (matrix is of 80*4).
4. Maximization Step: Now in this step, we works on the improvement of guess of each curve mean, standard deviation, and weighting factor. The parameters that are known in above expectation step are fully determined, and maximization of parameters is done. This step updates the distributional class parameters based on the new parameters.
NewMean, μnew c = 1
Nc
Fig. 4 Example of Gaussian mixture models [4]
Weighting factor, πc = Nc
4.4 Analyzing of High-Prone Noise Regions in Mumbai by Clusters
After applying the technique discussed above, the clusters of the regions are formed according to the rate of noise pollution in the particular area. Now, the analysis of data is done to estimate the major causes of noise pollution in the clusters formed areas.
4.5 Classification Using Decision Tree
Decision tree is a tree-like structure consisting of root nodes, along with leaf and intermediate nodes. The sample data of the particular year are collected, along with the daytime and nighttime. The major and dominant reasons are identified with the help of classification methods which are responsible for the occurrence of noise pollution in the regions in certain year. The data set is classified on the basis of cluster analysis. The criterion used to make decision tree is gain ratio, which is calculated as (Table 2):
Gain Ratio(A) = Gain(A)/Split Info(A)
Table 2 Symbol table with its description
Symbols and description
Covariance of Gaussian distribution
5 Conclusion and Future Work
In this paper, the proposed model which shows how cluster analysis helps to deter- mine the noise pollution prone regions of Mumbai. The decision tree is used to label the clusters which are classified to conclude the dominant factors responsible for the noise pollution. The advantages of used clustering technique are as follows:
• The choosing of component distribution is flexible. • Availability of “soft” classification (EMM is sometimes considered as soft clus- tering technique).
• Density estimation for each cluster is obtained.
In future, we can extend this work by implementing proposed methodology using the data mining tools. In addition to this, the study of the data sets to determine the most-vulnerable situation that occurred due to the impact of noise pollution in Mumbai and its impact on the population of Mumbai.
References
1. http://cpcb.nic.in 2. http://www.euro.who.int/en/health-topics/environment-and-health/noise/data-and-statistics 3. http://cpcb.nic.in/ 4. https://ibug.doc.ic.ac.uk/media/uploads/documents/expectation_maximization-1.pdf 5. P. Maijala, Z. Shuyang, T. Heittola, T. Virtanen, Environmental noise monitoring using source
classification in sensors. Appl. Acoust. 129, 258–267 (2018) 6. N. Maisonneuve, M. Stevens, B. Ochab, Participatory noise pollution monitoring using mobile
phones. Inf. Polity 15(1, 2), 51–71 (2010) 7. http://ethesis.nitrkl.ac.in/5590/1/E-49.pdf
Suchismita Goswami, Sushmita Goswami, Shubhasri Roy, Shreejita Mukherjee and Nilanjana Dutta Roy
Abstract Segmentation fromcolored retina images plays a vital role in stable feature extraction for image registration and detection in many ocular diseases. In this study, the authors will look at the segmentation of the blood vessels from fundus images which will further help in preparation of digital template. Here, images are passed through the preprocessing stages and then some of the morphological operators for thresholding are applied on the images for segmentation. Finally, noise removal and binary conversion complete the segmentation method. Then, a number count on blood vessels around the optic disk is done as a feature for further processing. The authors will ensure whether the segmentation accuracy, based on comparison with a ground truth, can serve as a reliable platform for image registration and ocular disease detection. Experiments are done on the images of DRIVE and VARIA databases with an average accuracy of 97.20 and 96.45%, respectively, for segmentation, and a comparative study has also been shown with the existing works.
Keywords Segmentation · Fundus images · Blood vessels · Morphological operators
S. Goswami · S. Goswami · S. Roy · S. Mukherjee (B) · N. D. Roy Department of Computer Science and Engineering, Institute of Engineering and Management, Kolkata, India e-mail: shreejitamukherjee.m@gmail.com
S. Goswami e-mail: suchismita.g24@gmail.com
S. Goswami e-mail: sushmita.g24@gmail.com
S. Roy e-mail: shubhasrir4@gmail.com
N. D. Roy e-mail: nilanjanaduttaroy@gmail.com
© Springer Nature Singapore Pte Ltd. 2020 J. K. Mandal and D. Bhattacharya (eds.), Emerging Technology in Modelling and Graphics, Advances in Intelligent Systems and Computing 937, https://doi.org/10.1007/978-981-13-7403-6_2
9
1 Introduction
Automated analysis and segmentation from colored retinal images is a challenging task. It has a potential research impact in various areas like diagnosing of many dis- eases, person identification and security purposes due to significant advances in the field of digital image processing. Extraction of stable features from retina like bifur- cation points, bifurcation angle, location of optic disk, width of the blood vessels, etc., needs special care. Thus, proper segmentation from colored retinal images is an essential task to accomplish the same. Manual segmentation of blood vessels is a cumbersome and time-consuming process. In a research setting, with proper techni- cal support, automatic segmentation from retinal images provides better and faster solution. It also plays an important role in diagnosing many ocular diseases affect- ing retina [1, 2]. Blood vessels are indicative to many pathological changes caused due to hypertension, diabetes, arteriosclerosis, cardiovascular diseases. So obser- vation on the vascular changes sometimes improves the disease diagnosis process. Apart from the medical diagnostics, the segmentation method has another potential application in person identification. The microvascular structure of human retina is unique for each individual and usually remains same during any one’s lifetime except surgical issues. With some existing distinct features on retinal vascular struc- ture, it could be used as a secure template for identification. Many algorithms are there in literature to segment the blood vessel from colored retinal images. In this paper, a best suited segmentation approach has been proposed using some of themor- phological operators which will further help in macula detection in the future. The proposed segmentation algorithm is subdivided into three categories, preprocessing, thresholding and blood vessel segmentation and post-processing. Preprocessing, like channel conversion, enhancement and noise removal techniques, helps in improving the quality of the image as most of them suffer from poor local contrast compared to background. Thresholding and binarization help in actual segmentation, and finally, noise present on the images is removed by applying 2-D median filtering in a 3 × 3 scanning window. Later, a count on blood vessels around optic disk [3] is done from the segmented images by looking at the number of transitions on binary images. The approach proves sensitivity and specificity as 0.9632 and 0.9470, respectively, and accuracy as 97%. Due to the inadequacy of available resources, the experiments are done only on all the images of DRIVE [4] and VARIA [5] databases. A precise view of the segmentation method is described in Fig. 1.
2 Background
Segmentation of a blood vessel from the retinal images using tracking-based ap- proach is proposed in [6]. In this approach, fuzzy model of a one-dimensional vessel profile drives the segmentation process. One drawback of these approaches is that this method of segmentation depends upon the methods for identifying the seed pixel
An Automated Segmentation Approach from Colored … 11
Fig. 1 Flow diagram of the segmentation method
12 S. Goswami et al.
which is either the optic disk or the branch points which are detected subsequently. Gabor filter-based image processingmethodswere proposed for retinal vessel extrac- tion in [7–10]. For segmentation of the blood vessels in the retinal images, optimized Gabor filters with local entropy thresholding had been used in those approaches. The drawback of those segmentation methodologies is that optimized Gabor filter methods fail to detect vessel of different widths and may sometimes detect blood vessels falsely. Also, detection process fails in case of defected retinal image caused by various retinal diseases. An automated texture-based blood vessel segmentation has been proposed in the paper [11]. In this paper, they have used Fuzzy c-Means (FCM) clustering algorithm for the classification between vessels and non-vessels depending on texture properties. This algorithm is having 84.37% sensitivity and 99.19% specificity.
3 Proposed Method
The existing algorithms suffer from problem of non-uniform illumination of the background in the retinal fundus image. The proposed method shows a way to over- come the limitations and to improve the effectiveness, accuracy and computational time.
Broadly, there are three principal stages of the segmentation method, preprocess- ing, blood vessel extraction and post-processing. Preprocessing technique has been applied, to increase the accuracy in recognizing and extracting the blood vessels. Green channel conversion of RGB images initiates the process as the green channel provides the highest vessel background contrast. Next, the boundary of the green channeled images is removed and contrast-limited adaptive histogram equalization (CLAHE) is applied over it to make a uniform illumination distribution. To this, morphological bottom hat operation with disk-shaped structuring element is applied to enhance the retinal blood vessels. Finally, to correct the illumination variation in the background of retinal image obtained from the previous stage, estimation of the background illumination and the contrast distribution is applied over it.
Image binarization and segmentation of the blood vessels are two essential tasks to be performed here with an empirically generated threshold value.
The primary task in post-processing stage is 2-D median filtering and morpho- logical noise removal operations. This would remove the disconnected blood vessel and noise from the binary image and assist to accomplish the desired goal.
3.1 Image Preprocessing and Segmentation
The retinal images show non-uniform illumination of the background due to the presence of vitreous humor, which is a transparent gel that fills the interior of the eye. The blood vessels show variety in their thickness and contrast. So the darker
An Automated Segmentation Approach from Colored … 13
vessels are clearly seen and easily detected. But it is difficult to extract the thin vessels having low contrast. It is easy to segment if the blood vessels appear as dark structure in the brighter background. So, difference in the contrast between the retinal blood vessels and the background is desirable. Hence, preprocessing is applied to the original retinal image to eliminate these anomalies and to prepare the image for the next steps of segmentation. Channel conversion It is necessary to convert color (RGB) images into green channel as the green channel provides the best contrast between blood vessels and background of the RGB representation. As the red channel has low contrast and the blue channel has poor dynamic range, we are focusing only on green channel, refer Fig. 2b. Boundary removal In this step, the outer border of the green component of the image is removed by suppressing the structures that have lower contrast than the surroundings and that are connected to the image border. Uniform image illumination using AHE Fundus images suffer from background intensity variation due to non-uniform illumination which further deteriorate the segmentation result. Due to this issue, background pixels sometimes have higher gray-level values than the vessel pixels. Because of the difference in gray-level in- tensity and false vessel’s appearance, the global thresholding techniques cannot be applied in this phase. Contrast-limited adaptive histogram equalization (CLAHE) is applied here to enhance the contrast of the green channel retinal image, as shown in Fig. 2c. It redistributes the light value of the image within small regions of the image instead of the entire one. The process thus enhances the contrast of every smaller region of the image. This step also helps to reduce few false detection of blood ves- sels that would otherwise decrease the performance of blood vessel segmentation method. Morphological transformation Sometimes, retinal blood vessels falsely appear as background due to poor intensity variation between the blood vessels and the image background. So to brighten the darker blood vessels in lighter background with an empirically tested disk-shaped structuring element of size 8 × 8, morphological bottom hat operation is applied here. Bottom hat filtering subtracts the input image from the result to perform a morphological closing operation on the image which is dilation followed by erosion. Dilation is an operation that grows or thickens the object in binary image. Erosion operation shrinks the object by eroding away the boundaries of regions of foreground pixels. After applying bottom hat, the changes in the result are shown in Fig. 2d. Smoothing IlluminationThe imageproduced after the bottomhat operationhas poor distribution of lightness value. To improve the distribution of intensity, againCLAHE is applied, so that the image iswell prepared for thresholding and segmentation stages; see Fig. 2e.
14 S. Goswami et al.
Fig. 2 Flow diagram of the segmentation method
3.2 Blood Vessel Segmentation
Preprocessing has enhanced the contrast of the original images. Now the images are being thresholded using Otsu’s algorithm by keeping the information below the threshold value and assigning rest of the image the same value as the threshold. Thus, cluster-based algorithm is used to perform clustering-based image thresholding and the preprocessed grayscale images are now reduced into the binary images (Fig. 2f).
3.3 Noise Removal
The clarity of the images obtained by the previous stages is not very good because of the presence of salt-and-pepper noise in it. Hence, a 2-D median filtering with 3 × 3
An Automated Segmentation Approach from Colored … 15
neighborhood size is applied to reduce the noise from the segmented image. Next, the small connected components, having lesser than 35 white pixels around it within the 8-connectivity window, are removed from the previously processed image. Desired image is shown in Fig. 2h.
3.4 Counting Vessels Around Optic Disk
Common parameters, like accuracy, specificity and sensitivity, are used to measure the strength of an algorithm and to compare the results with other segmentation algorithms. To compare the result of the proposed approach with gold standard images, count on the number of vessels is essential. Also, counting the number of blood vessels around optic disk helps in diagnosis of severe ocular disease called proliferative diabetic retinopathy (PDR). Abrupt changes in count on vessels are alarming for some unusual scenario on regular monitoring. Algorithm 1 explains the method of counting blood vessels in segmented images. Table1 shows the number of blood vessels detected by the proposed algorithm on the images of DRIVE database.
Algorithm 1 Count on blood vessels around OD 1: count ← number of blood vessels around OD 2: for within a 50 × 50 window around the optic disk do 3: repeat 4: for for each pixel along the window border do 5: if a white pixel is found then 6: repeat 7: move along the border of the window by one pixel 8: until white to black transitions occur 9: count ← count + 1 10: else 11: move along the border of the window by one pixel 12: end if 13: end for 14: until all the pixels along the four border of the specified window is traversed 15: end for
Table 1 Count on number of blood vessels around OD
Image Count Image Count Image Count
Image 1 14 Image 6 12 Image 11 10
Image 2 12 Image 7 16 Image 12 12
Image 3 16 Image 8 16 Image 13 14
Image 4 13 Image 9 14 Image 14 13
Image 5 10 Image 10 13 Image 15 12
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4 Results and Performance Evaluation
A complete segmentation method, along with the intermediate steps, is shown in Fig. 2. To show the proficiency of the proposed algorithm, gold standard images which are manual segmented images from DRIVE [4] database have been used. Specificity, sensitivity and accuracy are calculated as measuring parameters to com- pare the results of the proposed algorithm with the other existing algorithms.
Sensitivity = TP
(TP + TN + FP + FN) (3)
where TP is correctly detected positive values; TN is correctly detected negative values; FP is feature is negative, but detected as positive; and FN is feature is positive, but cannot detect (Figs. 3 and 4).
Please refer Table2 for the performance measurement of the proposed algorithm. Table3 compares the performance of the proposed algorithm with others.