Recent advancements in the technology of medical imaging, such as CT and MRI scanners, are making it possible to create more detailed 3D and 4D images. These powerful images require vast amounts of digital data to help with the diagnosis of the patient. Artificial intelligence (AI) must play a vital role in supporting with the analysis of this medical imaging data, but it will only be viable as long as healthcare professionals and AI interact to embrace deep thinking platforms such as automation in the identification of diseases in patients.
AI Innovation in Medical Imaging Diagnostics is an essential reference source that examines AI applications in medical imaging that can transform hospitals to become more efficient in the management of patient treatment plans through the production of faster imaging and the reduction of radiation dosages through the PET and SPECT imaging modalities. The book also explores how data clusters from these images can be translated into small data packages that can be accessed by healthcare departments to give a real-time insight into patient care and required interventions. Featuring research on topics such as assistive healthcare, cancer detection, and machine learning, this book is ideally designed for healthcare administrators, radiologists, data analysts, computer science professionals, medical imaging specialists, diagnosticians, medical professionals, researchers, and students.
Chapter 1 detects Ocular Pathologies from Iris Images using two algorithms and their results are also given in a graphical representation. The two algorithms used for the research study are Blind De-convolution and Fuzzy-C means Clustering.
Chapter 2 deals with the basics of machine learning techniques and different algorithm and their application in Healthcare system. The top applications of machine learning in healthcare are specified and ways to assess the growth of the healthcare industry in 2019 and beyond.
Chapter 3 reports about the various supervised and unsupervised techniques for brain tumor detection and segmentation such as K-nearest neighbor (K-NN), K-means clustering, and also morphological operator and also specified the experimental results.
Chapter 4 performs the Breast Cancer Diagnosis in Mammograms Using Wavelet Analysis, Haralick Descriptors and Auto encoder. The proposed work produced improved accuracy with comparison to the state-of-art techniques. Chapter 5 covers the Feature Selection for classification of Lung CT Images using Random Forest Algorithm. The proposed work produced better results than the existing techniques.
Chapter 6 focused on automatic feature selection algorithm for diagnosis of women breast cancer from digital mammographic images and breast cancer classification are achieved through multi-layer perceptron techniques. The outcome of the paper reduced false positive rate and improved diagnosis accuracy at a greater extent.
Chapter 7 deals with retrieval of medical images based on Content Based Approach to Medical Image Retrieval. In Chapter 8, correlation and analysis of overlapping leukocytes in blood cell images using intracellular markers and colocalization operation are discussed with their experimental results.
Chapter 9 covers the enchodroma Tumor Detection from MRI Images using SVM Classifier.
Medical Images captured through different image modality are to be stored in a cloud storage for the researchers to utilize the images for their research work. So, the approach to Cloud Computing for Medical Image Analysis well discussed in Chapter 10.
In Chapter 11 discuss the segmentation Of Spine Tumour using K-Means and Active Contour and also explains how the feature extraction can be done using Gray Level Co-occurrence Matrix.
The emphasis of Chapter 12 is to give a solution of low cost, accurate, automated, portable cancer screening tool that can be operated by a simple clinician. Unlike mammography, our imaging model can be radiation free, non-touch, not painful and works for women of all ages.
We are very much grateful to the authors and reviewers for their excellent contributions for making this book possible. Our special thanks to IGI Global, Lindsay Wertman and support teams especially for their excellent collaborations. I pledge my gratitude to my college honorable administrators Director, Mrs. Ramya Deepak and Director Academics Dr. V. Deepak for their fullest support and cooperation for the completion of this proposal. I also take this opportunity to thank my Principal, Dr. Ramesh and Dr. S P. Chokkalingam, Program Director for their kind support and motivation throughout the proposal. I also extend my heartfelt thanks to Dr. K. Sujatha, Professor from Dr. MGR University who helped me throughout this journey.
This edited book covers the automatic computer aided diagnosis of spine tumor, women breast cancer, brain tumor, Ocular Pathologies from Iris Images. .Being an interdisciplinary book, we hope it will be useful to a wide variety of readers and will provide useful information to professors, researchers and students.

Preface xv

Chapter 1 Detection of Ocular Pathologies From Iris Images Using Blind De-Convolution and Fuzzy C-Means Clustering: Detection of Ocular Pathologies 1

Chapter 2 Machine Learning in Healthcare 37

Chapter 3 Detection of Tumor From Brain MRI Images Using Supervised and Unsupervised Methods 61

Chapter 4 Breast Cancer Diagnosis in Mammograms Using Wavelet Analysis, Haralick Descriptors, and Autoencoder 76

Chapter 5 Feature Selection Using Random Forest Algorithm to Diagnose Tuberculosis From Lung CT Images 92

Chapter 6 An Ensemble Feature Subset Selection for Women Breast Cancer Classification 101

Chapter 7 A Content-Based Approach to Medical Image Retrieval 114

Chapter 8 Correlation and Analysis of Overlapping Leukocytes in Blood Cell Images Using Intracellular Markers and Colocalization Operation 137

Chapter 9 Enchodroma Tumor Detection From MRI Images Using SVM Classifier 155

Chapter 10 An Approach to Cloud Computing for Medical Image Analysis 164

Chapter 11 Segmentation of Spine Tumour Using K-Means and Active Contour and Feature Extraction Using GLCM 194

Chapter 12 A Survey on Early Detection of Women's Breast Cancer Using IoT 208

Compilation of References 220

About the Contributors 240

Index 247

中国医科院医学信息研究所