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Big Data in Medical Image Processing Big Data in Medical Image Processing R. Suganya Department of Information Technology Thiagarajar College of Engineering Madurai, Tamilnadu, India S. Rajaram Department of ECE Thiagarajar College of Engineering Madurai, Tamilnadu, India A. Sheik Abdullah Department of Information Technology Thiagarajar College of Engineering Madurai, Tamilnadu, India p, p, A SCIENCE PUBLISHERS BOOK A SCIENCE PUBLISHERS BOOK CRC Press TCaRyClo Pr r&es Fsrancis Group 6T0ay0l0o rB &ro kFernan Scoisu nGdr oPuaprkway NW, Suite 300 B60o0c0a RBarotoknen, F SLo 3u3n4d8 P7a-2r7k4w2ay NW, Suite 300 Boca Raton, FL 33487-2742 © 2017 by Taylor & Francis Group, LLC C© R22C0011 P87r beyss T ias yalno ri m&p Frriannt coifs TGaryolourp ,& L FLrCancis Group, an Informa business CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works No claim to original U.S. Government works Printed on acid-free paper VPreirnstioedn oDna taec:i d2-0f1r7e0e1 p1a9per Version Date: 2200117710011119 International Standard Book Number-13: 978-1-4987-4799-8 (Hardback) International Standard Book Number-13: 997788--11--14398857--54772949--68 (Hardback) This book contains information obtained from authentic and highly regarded sources. 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SSCuolblaajres ccsteisfl:li csL-aC-MtSioHant:e :E rLlieaCclstC.r |i c RM bCeats7toe8pr.oi7er.soD-u-5Ms 3ma taSetr8eira5ila s2l.s 0|. 1Fu8e l| cDelDlsC-- M61at6e.r0ia7l/s5. |40285--dc23 SColalasrs icfeicllast-i-oMn:a LteCrCia lTs.K |2 M90e1s o.Mpo4r7o u2s0 1m7 a|t DerDiaCls .621.31/24240284--dc23 CLLClCa sr esricefoiccroadtr iadov naa:i vlLaaCbilClea abTtlK eh2 tat9pt0 s1h: /.t/Mtlpc4csn7:/ .2/lol0cc1c.7gn o|. vlDo/2Dc0.Cg16 o60v241/22.350101/9274024460288149- -dc23 LC record available at https://lccn.loc.gov/2016042509 Visit the Taylor & Francis Web site at hVtitspit: /t/hwew Twa.ytlaoyrl o&r aFnrdanfrcains cWise.cbo smite at http://www.taylorandfrancis.com and the CRC Press Web site at hanttdp t:/h/we CwRwC.c rPcrpersess Ws.ceobm site at http://www.crcpress.com Preface This book covers the syllabus of the various courses like B.E./B.Tech (Computer Science and Engineering, Information Technology, Biomedical Engineering, Electronics and Communication Engineering), MCA, M.Tech (Computer Science and Engineering, Bio Medical Engineering), and other courses related to department of medicine offered by various Universities and Institutions. This book contains the importance of medical imaging in modern health care community. The chapters involved in this book provide solution for better diagnostic capabilities. The book provides an automated system that could retrieve images based on user’s interest to a point of providing decision support. It will help medical analysts to take an informed decisions before planning treatment and surgery. It will also be useful to researchers who are working in problems involved in medical imaging. The brief contents of this book chapter-wise are given below: Chapter 1: Provides the importance and challenges of Big Data in Medical Image Processing through Hadoop & Map reduce technique. Chapter 2: Starts with Image Pre-processing, importance of speckle in medical images, Different types of filter and methodologies. This chapter presents how to remove speckle noise present in the low modality medical images. Finally this chapter ends with discussion about metrics used for speckle reduction. Chapter 3: Contains the importance of medical image registration, mono modal registration, multi modal image registration. This chapter also covers the procedure involved in the image registration. This chapter deals with optimization techniques like various similarity measures- correlation coefficients and mutual information. Finally this chapter ends with applications of medical image registration with corresponding sample case study. Big Data in Medical Image Processing Chapter 4: This chapter begins with introduction on texture analysis and importance of dimensionality reduction. This chapter discusses different types of feature extraction for different medical imaging modalities. Chapter 5: This chapter includes an introduction on machine learning techniques and importance of supervised and unsupervised medical image classification. This chapter discusses various machine learning algorithms like Relevance feedback classifier, Binary vs. multiple SVM, Neural network, Fuzzy classifier with detailed algorithmic representation and simple pictorial representation. Finally this chapter concluded with image retrieval and case study. Features This book has very simple and practical approach to make the readers understand well. It provides how to capture big data medical images from acquisition devices and doing analysis over it. Discuss an impact of speckle (noise) present in the medical images, monitoring the various stages of diseases like cancer and tumor by doing medical image registration. It explains the impact of dimensionality reduction. Finally it acts a recommender system for medical college students for Classifying various stages involved in the diseases by using Machine learning techniques. vi Contents Preface v 1. Big Data in Medical Image Processing 1 2. Image Processing 46 3. Image Registration 71 4. Texture Feature Extraction 95 5. Image Classification and Retrieval 125 References 190 Index 199 Authors’ Biography 201 1 Big Data in Medical Image Processing 1.1 An Introduction to Big Data Big data technologies are being increasingly used for biomedical and healthcare informatics research. Large amounts of biological and clinical data have been generated and collected at an exceptional speed and scale. Recent years have witnessed an escalating volume of medical image data, and observations are being gathered and accumulated. New technologies have made the acquisition of hundreds of terabytes/petabytes of data possible, which are being made available to the medical and scientific community. For example, the new generation of sequencing technologies enables the dispensation of billions of DNA sequence data per day, and the application of electronic health records (EHRs) is documenting large amounts of patient data. Handling out these large datasets and processing them is a challenging task. Together with the new medical opportunities arising, new image and data processing algorithms are required for functioning with, and learning from, large scale medical datasets. This book aims to scrutinize recent progress in the medical imaging field, together with new opportunity stemming from increased medical data availability, as well as the specific challenges involved in Big data. “Big Data” is a key word in medical and healthcare sector for patient care. NASA researchers coined the term big data in 1967 to describe the huge amount of information being generated by supercomputers. It has evolved to include all data streaming from various sources—cell phones, mobile devices, satellites, Google, Amazon, Twitter, etc. The impact of big data is deep, and it will have