Artificial Intelligence Techniques in Medical Imaging: A Systematic Review

This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Elasticity mapping for breast cancer diagnosis using tactile imaging and auxiliary sensor fusion

Tactile Imaging (TI) is a technology utilising capacitive pressure sensors to image elasticity distributions within soft tissues such as the breast for cancer screening. TI aims to solve critical problems in the cancer screening pathway, particularly: low sensitivity of manual palpation, patient discomfort during X-ray mammography, and the poor quality of breast cancer referral forms between primary and secondary care facilities. TI is effective in identifying ‘non-palpable’, early-stage tumours, with basic differential ability that reduced unnecessary biopsies by 21% in repeated clinical studies. TI has its limitations, particularly: the measured hardness of a lesion is relative to the background hardness, and lesion location estimates are subjective and prone to operator error. TI can achieve more than simple visualisation of lesions and can act as an accurate differentiator and material analysis tool with further metric development and acknowledgement of error sensitivities when transferring from phantom to clinical trials. This thesis explores and develops two methods, specifically inertial measurement and IR vein imaging, for determining the breast background elasticity, and registering tactile maps for lesion localisation, based on fusion of tactile and auxiliary sensors. These sensors enhance the capabilities of TI, with background tissue elasticity determined with MAE < 4% over tissues in the range 9 kPa – 90 kPa and probe trajectory across the breast measured with an error ratio < 0.3%, independent of applied load, validated on silicone phantoms. A basic TI error model is also proposed, maintaining tactile sensor stability and accuracy with 1% settling times < 1.5s over a range of realistic operating conditions. These developments are designed to be easily implemented into commercial systems, through appropriate design, to maximise impact, providing a stable platform for accurate tissue measurements. This will allow clinical TI to further reduce benign referral rates in a cost-effective manner, by elasticity differentiation and lesion classification in future works.Tactile Imaging (TI) is a technology utilising capacitive pressure sensors to image elasticity distributions within soft tissues such as the breast for cancer screening. TI aims to solve critical problems in the cancer screening pathway, particularly: low sensitivity of manual palpation, patient discomfort during X-ray mammography, and the poor quality of breast cancer referral forms between primary and secondary care facilities. TI is effective in identifying ‘non-palpable’, early-stage tumours, with basic differential ability that reduced unnecessary biopsies by 21% in repeated clinical studies. TI has its limitations, particularly: the measured hardness of a lesion is relative to the background hardness, and lesion location estimates are subjective and prone to operator error. TI can achieve more than simple visualisation of lesions and can act as an accurate differentiator and material analysis tool with further metric development and acknowledgement of error sensitivities when transferring from phantom to clinical trials. This thesis explores and develops two methods, specifically inertial measurement and IR vein imaging, for determining the breast background elasticity, and registering tactile maps for lesion localisation, based on fusion of tactile and auxiliary sensors. These sensors enhance the capabilities of TI, with background tissue elasticity determined with MAE < 4% over tissues in the range 9 kPa – 90 kPa and probe trajectory across the breast measured with an error ratio < 0.3%, independent of applied load, validated on silicone phantoms. A basic TI error model is also proposed, maintaining tactile sensor stability and accuracy with 1% settling times < 1.5s over a range of realistic operating conditions. These developments are designed to be easily implemented into commercial systems, through appropriate design, to maximise impact, providing a stable platform for accurate tissue measurements. This will allow clinical TI to further reduce benign referral rates in a cost-effective manner, by elasticity differentiation and lesion classification in future works

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification

Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest\u27 detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification --Abstract, page v