Download or read book Input Space Augmentation for Skin Lesion Segmentation in Dermoscopic Images written by Abhishek Kumar and published by . This book was released on 2020 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cancer is the second leading cause of death globally, and of all the cancers, skin cancer is the most prevalent. Early diagnosis of skin cancer is a crucial step for maximizing patient survival rates and treatment outcomes. Skin conditions are often diagnosed by dermatologists based on the visual properties of the affected regions, motivating the utility of automated algorithms to assist dermatologists and offer viable, low-cost, and quick results to assist dermatological diagnoses. Over the last decade, machine learning, and more recently, deep learning-based diagnoses of skin lesions have started approaching human performance levels. This thesis studies approaches to improve the segmentation of skin lesions in dermoscopic images, which is often the first and the most important task in the diagnosis of dermatological conditions. In particular, we present two methods to improve deep learning-based segmentation of skin lesions by augmenting the input space of convolutional neural network models. In the first contribution, we address the problem of the paucity of annotated data by learning to synthesize artificial skin lesion images conditioned on input segmentation masks. We then use these synthetic image mask pairs to augment our original segmentation training datasets. In our second contribution, we leverage certain color channels and skin imaging- and illumination-based knowledge in a deep learning framework to augment the input space of the segmentation models. We evaluate the two contributions on five dermoscopic image datasets: the ISIC Skin Lesion Segmentation Challenge 2016, 2017, and 2018 datasets, the DermoFit Image Library, and the PH2 Database, and observe performance improvements across all datasets.

Download or read book Dermoscopy Image Analysis written by M. Emre Celebi and published by CRC Press. This book was released on 2015-10-16 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dermoscopy is a noninvasive skin imaging technique that uses optical magnification and either liquid immersion or cross-polarized lighting to make subsurface structures more easily visible when compared to conventional clinical images. It allows for the identification of dozens of morphological features that are particularly important in identifyin

Download or read book Advances in Integrations of Intelligent Methods written by Ioannis Hatzilygeroudis and published by Springer Nature. This book was released on 2020-01-17 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a number of research efforts in combining AI methods or techniques to solve complex problems in various areas. The combination of different intelligent methods is an active research area in artificial intelligence (AI), since it is believed that complex problems can be more easily solved with integrated or hybrid methods, such as combinations of different soft computing methods (fuzzy logic, neural networks, and evolutionary algorithms) among themselves or with hard AI technologies like logic and rules; machine learning with soft computing and classical AI methods; and agent-based approaches with logic and non-symbolic approaches. Some of the combinations are already extensively used, including neuro-symbolic methods, neuro-fuzzy methods, and methods combining rule-based and case-based reasoning. However, other combinations are still being investigated, such as those related to the semantic web, deep learning and swarm intelligence algorithms. Most are connected with specific applications, while the rest are based on principles.

Download or read book Development of a Segmentation Method for Dermoscopic Images Based on Color Clustering written by Harald Galda and published by Grin Publishing. This book was released on 2007-08 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doctoral Thesis / Dissertation from the year 2003 in the subject Computer Science - Applied, grade: Pass grade, Kobe University (Faculty of Engineering, Department of Computer & Systems Engineering, Kitamura Lab), course: Doctor Course Information and Media Science, 41 entries in the bibliography, language: English, abstract: Malignant melanoma is a very dangerous kind of skin cancer. In order to treat malignant melanoma it must be detected as early as possible. However, when looking at a malignant melanoma by the naked eye, it can be mistaken as a nevus (benign skin lesion). Therefore, dermatologists use a microscope that shows the pigmented structure of the skin. This microscope is called "dermoscope". An irregular overall structure, an irregular border and several colors indicate that a skin lesion is malignant. A homogeneous structure, a regular border and few colors indicate that a lesion is benign. However, even when using a dermoscope a melanoma can be mistaken as a nevus. Therefore it is desirable to analyze dermoscopic images by a computer in order to classify them as malignant or benign. Before a dermoscopic image is classified, usually the skin lesion border is extracted. For this purpose, previously developed methods segment the image into regions of the same color (color segmentation) or into regions that fulfill a homogeneity criterion (region based segmentation). Color segmentation can be done using fuzzy c-means. When applying fuzzy c-means, the number of cluster centers corresponds to the number of distinguished colors and must be specified. However, the number of colors in dermoscopic images can vary and is not known in advance. The goal of this research is developing a method that automatically determines the number of clusters in color space. The clustering accuracy is evaluated by cluster validity index. Cluster validity indices describe how well a partition (cluster center set) represents the "natural" clusters of a data set. The method

Download or read book OR 2 0 Context Aware Operating Theaters Computer Assisted Robotic Endoscopy Clinical Image Based Procedures and Skin Image Analysis written by Danail Stoyanov and published by Springer. This book was released on 2018-10-01 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the First International Workshop on OR 2.0 Context-Aware Operating Theaters, OR 2.0 2018, 5th International Workshop on Computer Assisted Robotic Endoscopy, CARE 2018, 7th International Workshop on Clinical Image-Based Procedures, CLIP 2018, and the First International Workshop on Skin Image Analysis, ISIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 11 full papers presented at OR 2.0 2018, the 5 full papers presented at CARE 2018, the 8 full papers presented at CLIP 2018, and the 10 full papers presented at ISIC 2018 were carefully reviewed and selected. The OR 2.0 papers cover a wide range of topics such as machine vision and perception, robotics, surgical simulation and modeling, multi-modal data fusion and visualization, image analysis, advanced imaging, advanced display technologies, human-computer interfaces, sensors. The CARE papers cover topics to advance the field of computer-assisted and robotic endoscopy. The CLIP papers cover topics to fill gaps between basic science and clinical applications. The ISIC papers cover topics to facilitate knowledge dissemination in the field of skin image analysis, as well as to host a melanoma detection challenge, raising awareness and interest for these socially valuable tasks.

Download or read book Handbook of Dermoscopy written by Josep Malvehy and published by CRC Press. This book was released on 2006-01-17 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rise in popularity of dermoscopy has meant that more and more practitioners need a ready reference to consult in a clinical setting where larger atlases are less practical. The Handbook of Dermoscopy features a wealth of photographs, checklists, and algorithms to assist in spot diagnoses. Coverage includes melanocytic lesions, seborrheic kerato

Download or read book Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation written by Joanna Jaworek-Korjakowska and published by Infinite Study. This book was released on with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: Malignant melanoma is among the fastest increasing malignancies in many countries. Due to its propensity to metastasize and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. In non-Caucasian populations, melanomas are frequently located in acral volar areas and their dermoscopic appearance differs from the non-acral ones. Although lesion segmentation is a natural preliminary step towards its further analysis, so far virtually no acral skin lesion segmentation method has been proposed. Our goal was to develop an effective segmentation algorithm dedicated for acral lesions.

Download or read book Biomedical Data Mining for Information Retrieval written by Sujata Dash and published by John Wiley & Sons. This book was released on 2021-08-24 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.

Download or read book Automatic Segmentation of Skin Lesions from Dermatological Photographs written by Jeffrey Luc Glaister and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Atlases and Computational Models of the Heart Multi Sequence CMR Segmentation CRT EPiggy and LV Full Quantification Challenges written by Mihaela Pop and published by Springer Nature. This book was released on 2020-01-22 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-workshop proceedings of the 10th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 42 revised full workshop papers were carefully reviewed and selected from 76 submissions. The topics of the workshop included: cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods.

Download or read book An Efficient Block Based Algorithm for Hair Removal in Dermoscopic Images written by Ihab Zaqout and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hair occlusion in dermoscopy images affects the diagnostic operation of the skin lesion. Segmentation and classification of skin lesions are two major steps of the diagnostic operation required by Dermatologists. We propose a new algorithm for hair removal in dermoscopy images that includes two main stages: hair detection and inpainting. In hair detection, a morphological bottom-hat operation is implemented on Y-channel image of YIQ color space followed by a binarization operation. In inpainting, the repaired Y-channel is partitioned into 256 nonoverlapped blocks and for each block, white pixels are replaced by locating the highest peak of using a histogram function and a morphological close operation. Our proposed algorithm reports a true positive rate (sensitivity) of 97.36%, a false positive rate (fall-out) of 4.25%, and a true negative rate (specificity) of 95.75%. The diagnostic accuracy achieved is recorded at a high level of 95.78%.

Download or read book Dermoscopic Image Segmentation Using Fuzzy Techniques written by Sowmya Devi and published by LAP Lambert Academic Publishing. This book was released on 2012 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. This work explains the task of segmenting skin lesions in Dermoscopy images using various Fuzzy clustering techniques for the early diagnosis of Malignant Melanoma. Malignant Melanoma is the most frequent type of skin cancer and its incidence has been rapidly increasing over the last few decades. Dermoscopy is a non-invasive diagnosis technique for the observation of pigmented skin lesions used in dermatology. Dermoscopic images have great potential in the early diagnosis of malignant melanoma, but their interpretation is time consuming and subjective, even for trained dermatologists.The various Fuzzy clustering techniques used are Fuzzy C Means Algorithm (FCM), Possibilistic C Means Algorithm and Hierarchical C Means Algorithm. The segmented images are compared with the ground truth image using various parameters such as False Positive Error (FPE), False Negative Error (FNE), Coefficient of similarity, Spatial overlap and their performance is evaluated

Download or read book Artificial Intelligence and Internet of Things written by Lalit Mohan Goyal and published by CRC Press. This book was released on 2021-08-25 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reveals the applications of AI and IoT in smart healthcare and medical systems. It provides core principles, algorithms, protocols, emerging trends, security problems, and the latest e-healthcare services findings. The book also provides case studies and discusses how AI and IoT applications such as wireless devices, sensors, and deep learning could play a major role in assisting patients, doctors, and pharmaceutical staff. It focuses on how to use AI and IoT to keep patients safe and healthy and, at the same time, empower physicians to deliver superlative care. This book is written for researchers and practitioners working in the information technology, computer science, and medical equipment manufacturing industry for products and services having basic- and high-level AI and IoT applications. The book is also a useful guide for academic researchers and students.

Download or read book Attention based Skin Lesion Recognition written by Yiqi Yan and published by . This book was released on 2020 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: Skin cancer is one of the most common types of cancers in the world and is a big concern for people's health. In recent years, automatic algorithms to recognize skin cancers from dermoscopy images have gained lots of popularity, especially deep-learning-based methods. In this thesis, we propose an attention-based deep learning model for skin cancer recogni- tion. The attention modules, which are learned together with other network parameters, estimate attention maps that highlight image regions of interest that are relevant to lesion classification. These attention maps provide a more interpretable output as opposed to only outputting a class label. Additionally, we propose to utilize prior information by regulariz- ing attention maps with regions of interest (ROIs) (e.g., lesion segmentation or dermoscopic features). To our knowledge, we are the first to introduce an end-to-end trainable attention module with regularization for skin cancer recognition. We provide both quantitative and qualitative results on public datasets to demonstrate the effectiveness of our method. Experiments show that: (1) the attention module is capable of ruling out irrelevant areas in the image; (2) when the proposed attention regularization terms are applied, both the classification performance and the attention maps can be further refined; (3) the attention regularization is quite robust and flexible in that it can take advantage of sparse or even imperfect ROI maps. The code of this work is released at https://github.com/SaoYan/IPMI2019-AttnMel.

Download or read book Machine Learning with Health Care Perspective written by Vishal Jain and published by Springer Nature. This book was released on 2020-03-09 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.

Download or read book Deep Learning in Healthcare written by Yen-Wei Chen and published by Springer Nature. This book was released on 2019-11-18 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of deep learning (DL) in medical and healthcare applications, including the fundamentals and current advances in medical image analysis, state-of-the-art DL methods for medical image analysis and real-world, deep learning-based clinical computer-aided diagnosis systems. Deep learning (DL) is one of the key techniques of artificial intelligence (AI) and today plays an important role in numerous academic and industrial areas. DL involves using a neural network with many layers (deep structure) between input and output, and its main advantage of is that it can automatically learn data-driven, highly representative and hierarchical features and perform feature extraction and classification on one network. DL can be used to model or simulate an intelligent system or process using annotated training data. Recently, DL has become widely used in medical applications, such as anatomic modelling, tumour detection, disease classification, computer-aided diagnosis and surgical planning. This book is intended for computer science and engineering students and researchers, medical professionals and anyone interested using DL techniques.

Download or read book Deep Learning for Skin Lesion Segmentation written by Zahra MiriKharaji and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Skin cancer is a major public health problem requiring computer aided diagnosis to reduce the burden of disease's high incidence ratio and the associated expenses by assisting clinicians. Image segmentation, the task of decomposing an image into multiple regions by per pixel labeling, is a crucial step toward skin cancer diagnosis and treatments. However, the existence of natural and artificial artifacts (e.g. hair and air bubbles), intrinsic factors (e.g. lesion shape and contrast), and variation in image conditions originating from imaging tools and environments make skin lesion segmentation a challenging task. Recently, several efforts have been made to leverage the demonstrated superior performance of deep learning models in the segmentation of skin lesions from the surrounding healthy skin. In this thesis, after a thorough examination of the studies leveraging the capability of deep learning models in skin lesion segmentation, we propose novel segmentation prediction models advancing state-of-the-art skin lesion segmentation techniques. First, we introduce deep learning based models that leverage the auxiliary information in the form of domain knowledge, contextual information, and labels consistency to regularize model parameters toward a more generalizable solution. Specifically, we encode high order shape prior knowledge into the loss function and also incorporate high-level semantic information in learning a sequence of deep models. Second, we study the limitations of ground truth pixels level annotations to effectively leverage limited reliable annotations. Specifically, we propose a robust to noise network by learning spatially adaptive weight maps associated with training images encoding the level of annotation noise to reduce the requirement of careful labeling. Also, we avoid single annotator bias, by training in an ensemble paradigm that handles inter-annotator disagreements and learns from all available annotations.