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This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder–decoder network, meta-learning, conditional variational encoder–decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.

Image segmentation. --- Image partitioning --- Partitioning, Image --- Segmentation, Image --- Image analysis --- Image processing --- Digital techniques

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High-Order Models in Semantic Image Segmentation reviews recent developments in optimization-based methods for image segmentation, presenting several geometric and mathematical models that underlie a broad class of recent segmentation techniques. Focusing on impactful algorithms in the computer vision community in the last 10 years, the book includes sections on graph-theoretic and continuous relaxation techniques, which can compute globally optimal solutions for many problems. The book provides a practical and accessible introduction to these state-of -the-art segmentation techniques that is ideal for academics, industry researchers, and graduate students in computer vision, machine learning and medical imaging.

Image segmentation. --- Semantic computing. --- Computer science --- Electronic data processing --- Semantics --- Image partitioning --- Partitioning, Image --- Segmentation, Image --- Image analysis --- Image processing --- Digital techniques

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This book focuses on a variety of interdisciplinary perspectives concerning the theory and application of artificial intelligence (AI) in medicine, medically oriented human biology, and healthcare. The list of topics includes the application of AI in biomedicine and clinical medicine, machine learning-based decision support, robotic surgery, data analytics and mining, laboratory information systems, and usage of AI in medical education. Special attention is given to the practical aspect of a study. Hence, the inclusion of a clinical assessment of the usefulness and potential impact of the submitted work is strongly highlighted.

computational intelligence --- medical assistance --- instance-based learning --- healthcare --- clinical decision support systems --- deep neural networks --- medical imaging --- backdoor attacks --- security and privacy --- COVID-19 --- gastric cancer --- endoscopy --- deep learning --- convolutional neural network --- brain --- pituitary adenoma --- dysembryoplastic neuroepithelial tumor --- DNET --- ganglioglioma --- digital pathology --- computer vision --- machine learning --- CNN --- ATLAS --- HarDNet --- Swin transformer --- segmentation --- U-Net --- cerebral infarction --- CycleGAN --- advanced statistics --- schizophrenia --- aggression --- forensic psychiatry --- medical image segmentation --- CT image segmentation --- kernel density --- semi-automated labeling tool --- Bayesian learning --- neuroimaging --- feature selection --- kernel formulation --- mental disorders --- MRI --- visual acuity --- fundus images --- ophthalmology --- SVM --- n/a

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This book discusses email spam detection and its challenges such as text classification and categorization. The book proposes an efficient spam detection technique that is a combination of Character Segmentation and Recognition and Classification (CSRC). The author describes how this can detect whether an email (text and image based) is a spam mail or not. The book presents four solutions: first, to extract the text character from the image by segmentation process which includes a combination of Discrete Wavelet Transform (DWT) and skew detection. Second, text characters are via text recognition and visual feature extraction approach which relies on contour analysis with improved Local Binary Pattern (LBP). Third, extracted text features are classified using improvised K-Nearest Neighbor search (KNN) and Support Vector Machine (SVM). Fourth, the performance of the proposed method is validated by the measure of metric named as sensitivity, specificity, precision, recall, F-measure, accuracy, error rate and correct rate. Presents solutions to email spam detection and discusses its challenges such as text classification and categorization; Analyzes the proposed techniques’ performance using precision, F-measure, recall and accuracy; Evaluates the limitations of the proposed research thereby recommending future research.

Electrical engineering. --- Optical data processing. --- Computational intelligence. --- Algorithms. --- Communications Engineering, Networks. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Computational Intelligence. --- Algorism --- Algebra --- Arithmetic --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Optical computing --- Visual data processing --- Bionics --- Electronic data processing --- Integrated optics --- Photonics --- Computers --- Electric engineering --- Engineering --- Foundations --- Optical equipment --- Image segmentation. --- Image partitioning --- Partitioning, Image --- Segmentation, Image --- Image analysis --- Image processing --- Digital techniques

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Many recent studies on medical image processing have involved the use of machine learning (ML) and deep learning (DL). This special issue, “Machine Learning/Deep Learning in Medical Image Processing”, has been launched to provide an opportunity for researchers in the area of medical image processing to highlight recent developments made in their fields with ML/DL. Seven excellent papers that cover a wide variety of medical/clinical aspects are selected in this special issue.

pancreas --- segmentation --- computed tomography --- deep learning --- data augmentation --- neoplasm metastasis --- ovarian neoplasms --- radiation exposure --- tomography --- x-ray computed --- prostate carcinoma --- microscopic --- convolutional neural network --- machine learning --- handcrafted --- oral carcinoma --- medical image segmentation --- colon cancer --- colon polyps --- OCT --- optical biopsy --- animal rat models --- CADx --- airway volume analysis --- artificial intelligence --- coronary artery disease --- SPECT MPI scans --- convolutional neural networks --- transfer learning --- classification models --- n/a