Image Analysis and Modeling in Medical Image Computing

Medical image computing has become one of the most challenging fields in medical informatics. In image-based diagnostics of the future software assistance will become more and more important, and image analysis systems integrating advanced image computing methods are needed to extract quantitative image parameters to characterize the state and changes of image structures of interest (e.g. tumors, organs, vessels, bones etc.) in a reproducible and objective way. Furthermore, in the field of software-assisted and navigated surgery medical image computing methods play a key role and have opened up new perspectives for patient treatment. However, further developments are needed to increase the grade of automation, accuracy, reproducibility and robustness. Moreover, the systems developed have to be integrated into the clinical workflow. For the development of advanced image computing systems methods of different scientific fields have to be adapted and used in combination. The principal methodologies in medical image computing are the following: image segmentation, image registration, image analysis for quantification and computer assisted image interpretation, modeling and simulation as well as visualization and virtual reality. Especially, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients and will gain importance in diagnostic and therapy of the future. From a methodical point of view the authors identify the following future trends and perspectives in medical image computing: development of optimized application-specific systems and integration into the clinical workflow, enhanced computational models for image analysis and virtual reality training systems, integration of different image computing methods, further integration of multimodal image data and biosignals and advanced methods for 4D medical image computing. The development of image analysis systems for diagnostic support or operation planning is a complex interdisciplinary process. Image computing methods enable new insights into the patient's image data and have the future potential to improve medical diagnostics and patient treatment.

Texture is an important property useful for image segmentation and the inference of 3-D information in the scene. Many approaches were proposed for analyzing textures. Among them are feature-based approaches and model-based approaches. In a feature-based environment various textural features are extracted from each textured image(or subimage) and are used to classify or discriminate given textures i. e. no explicit consideration of models is taken into account and thus the generation aspect is ignored. In model-based analysis we describe texture in terms of mathematical model which has both analysis and synthesis abilities. In the literature several comparative studies of feature-based methods are found. However few explicit comparative studies of model-based methods have been reported. This paper describes the development of some criteria to compare two model-based texture analysis methods (Time Series model and Markov Random Field model).

This correspondence investigates object-based analysis-synthesis coding (OBASC) for the encoding of moving images at very low data rates. According to the source model, each moving object of an image is described and encoded by three parameter sets defining its motion, shape, and surface color. The parameter sets of each object are obtained by model-based image analysis. They are coded by an object-dependent parameter coding. Using the coded parameter sets, an image can be synthesized by model-based image synthesis. Here, OBASC based on the source model of "moving flexible 3-D objects with 3-D motion" (F3D) is introduced. The efficiency of this source model F3D is compared to the efficiency of OBASC based on the source model of "moving rigid 3-D objects with 3-D motion" (R3D). Compared to R3D, F3D requires the additional transmission of flexible-shape parameters. Therefore, the source model F3D is only applied in those areas of the image which cannot be described by the source model R3D. The new source model F3D reduces the bit rate from 64 to 56 kb/s, providing the same picture quality measured by the SNR of the encoded color parameters.

Techniques of medical image processing and analysis play a crucial role in many clinical scenarios, including in diagnosis and treatment planning. However, immense quantities of data and high complexity of the algorithms often used are computationally demanding. As a result, there now exists a wide range of techniques of medical image processing and analysis that require the application of high-performance computing solutions in order to reduce the required runtime. The main purpose of this review is to provide a comprehensive reference source of techniques of medical image processing and analysis that have been accelerated by high-performance computing solutions. With this in mind, the articles available in the Scopus and Web of Science electronic repositories were searched. Subsequently, the most relevant articles found were individually analyzed in order to identify: (a) the metrics used to evaluate computing performance, (b) the high-performance computing solution used, (c) the parallel design adopted, and (d) the task of medical image processing and analysis involved. Hence, the techniques of medical image processing and analysis found were identified, reviewed, and discussed, particularly in terms of computational performance. Consequently, the techniques reviewed herein present the progress made so far in reducing the computational runtime involved, and the difficulties and challenges that remain to be overcome.

Heart disease is the leading cause of death in the modern world. Cardiac image processing is now routinely applied for detecting, classification and diagnosis of heart diseases. One of the most common uses of processing methods that are now widely applied are cardiac segmentation [1] and registration methods [2], that are used in order to extract the detailed anatomy and function of the heart. Automatic segmentation plays a central role when inspecting reconstructed 3D cardiac images from CT or MR scanners. An accurate classification of the different cardiac regions is usually the first step of tasks like: cardiac visualization, coronary artery inspection, measurement of the ejection fraction for the left and right ventricles and wall motion analysis. In a clinical context, physicians often mentally integrate image information from different modalities. Automatic registration, based on computer programs, might, however, offer better accuracy and repeatability and save time [3]. Cardiac image registration remains a challenge because of the numerous specific problems mainly related to the different motion sources (patient, respiration, heart) and to the specificity of each imaging modality. Up to now, no general method could automatically register any modality with any other modality so far. This paper presents a survey of shape modeling applications to cardiac image analysis from MRI, CT, echocardiography, PET, and SPECT and aims to introduce new methodologies in this field, classify major contributions in image-based cardiac modeling, provide a tutorial to beginners to initiate their own studies, and introduce the major challenges of registration and segmentation and provide practical examples. Image-driven processing methods, such as thresholding, region-based or edge-based techniques, or else pixel classification [4-6], provide a limited framework for further medical image analysis. They can include geometrical information, as well as high level information, in the so-called shape prior based segmentation framework, or through active shape models and active appearance models. At last, atlas guided segmentation also make use of a set of manually segmented images. By using different analysis software of cardiac images, CAD prototypes can be used in clinical routines in order to provide a computer output as a second reader to assist physicians in the detection of abnormalities, quantification of disease progression and differential diagnosis of lesions [7]. Computerized analysis of cardiac images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Deformable models, which include deformable contours (the popular snakes) and deformable surfaces, are a powerful model-based medical image analysis technique. We develop a new class of deformable models by formulating deformable surfaces in terms of an affine cell image decomposition (ACID). Our approach significantly extends standard deformable surfaces, while retaining their interactivity and other desirable properties. In particular, the ACID induces an efficient reparameterization mechanism that enables parametric deformable surfaces to evolve into complex geometries, even modifying their topology as necessary. We demonstrate that our new ACID-based deformable surfaces, dubbed T-surfaces, can effectively segment complex anatomic structures from medical volume images.

Medical image processing and analysis techniques play a significant role in diagnosing diseases. Thus, during the last decade, several noteworthy improvements in medical diagnostics have been made based on medical image processing techniques. In this article, we reviewed articles published in the most important journals and conferences that used or proposed medical image analysis techniques to diagnose diseases. Starting from four scientific databases, we applied the PRISMA technique to efficiently process and refine articles until we obtained forty research articles published in the last five years (2017–2021) aimed at answering our research questions. The medical image processing and analysis approaches were identified, examined, and discussed, including preprocessing, segmentation, feature extraction, classification, evaluation metrics, and diagnosis techniques. This article also sheds light on machine learning and deep learning approaches. We also focused on the most important medical image processing techniques used in these articles to establish the best methodologies for future approaches, discussing the most efficient ones and proposing in this way a comprehensive reference source of methods of medical image processing and analysis that can be very useful in future medical diagnosis systems.

<p>Patterns in biological and medical data exhibit highly structured geometries. Leveraging geometrical comparisons through quantitative descriptors or via contractive difference measures can facilitate the development of interpretable, unsupervised, and efficient models for clinical diagnosis and biological discovery.</p> <p>In medical image analysis, abnormality-related patterns often exhibit as quantifiable geometrical deformations. Guided by this principle, the following clinical challenges are addressed: a three-dimensional quantitative modeling of bone erosion and proliferation, and the development of robust detection algorithms for real-world applications. First, building upon radiological consensus, a bone erosion is modeled as significant concave regions on the cortical layer and a model-based 3D automatic erosion detection framework is proposed. This approach involves: (a) the cortical surface reconstruction and (b) the curvature-driven erosion identification, implemented via computational differential geometry and level set optimization. Second, the bone proliferation is mathematically formalized by integrating pathological insights with statistical shape analysis. The resulting Deformation-Controllable Elastic Shape (DCES) model provides an unsupervised solution for 3D proliferation quantification.</p> <p>Beyond medical images, the rapid growth of protein structure data demands advanced geometrical comparison methods. While existing approaches primarily rely on sequential geometrical comparisons, non-sequential comparison methods remain underdeveloped. To bridge this gap, the non-sequential comparison is framed as the Entropy-regularized Partial Linear Sum Assignment Problem (epLSAP) and solved by epLSAP-Align, a solver based on Sinkhorn algorithms that enables efficient topology-agnostic comparisons with tunable fidelity-coverage tradeoffs. In summary, these contributions help analyze biomedical structures from geometrical perspectives, enabling practical clinical/biology applications with geometrical insights.</p>

Over the past five years, artificial intelligence (AI) has introduced new models and methods for addressing the challenges associated with the broader adoption of AI models and systems in medicine. This paper reviews recent advances in AI for medical image and video analysis, outlines emerging paradigms, highlights pathways for successful clinical translation, and provides recommendations for future work. Hybrid Convolutional Neural Network (CNN) Transformer architectures now deliver state-of-the-art results in segmentation, classification, reconstruction, synthesis, and registration. Foundation and generative AI models enable the use of transfer learning to smaller datasets with limited ground truth. Federated learning supports privacy-preserving collaboration across institutions. Explainable and trustworthy AI approaches have become essential to foster clinician trust, ensure regulatory compliance, and facilitate ethical deployment. Together, these developments pave the way for integrating AI into radiology, pathology, and wider healthcare workflows.

Medical image processing and analysis operations, particularly segmentation, can benefit a great deal from prior information encoded to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. Model/atlas-based methods are extant in medical image segmentation. Although multi-atlas/ multi-model methods have shown improved accuracy for image segmentation, if the atlases/models do not cover representatively the distinct groups, then the methods may not be generalizable to new populations. In a previous study, we have given an answer to address the following problem at image level: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor in a population? However, the number of models for different objects may be different, and at the image level, it may not be possible to infer the number of models needed for each object. So, the modified question to which we are now seeking an answer to in this paper is: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor for each object in a body region? To answer this question, we modified our method in the previous study for seeking the optimum grouping for a given population of images but focusing on the individual objects. We present our results on head and neck computed tomography (CT) scans of 298 patients.

Brain development and atrophy accompany people's life. Brain development diseases, such as autism and Alzheimer's disease, affect a large part of the population. Analyzing brain development is very important in public healthcare, and image registration is essential in medical brain image analysis. Many previous studies investigate registration accuracy by the “ground truth” dataset, marker-based similarity calculation, and expert check to find the best registration algorithms. But the evaluation of image registration technology only at the accuracy level is not comprehensive. Here, we compare the performance of three publicly available registration techniques in brain magnetic resonance imaging (MRI) analysis based on some key features widely used in previous MRI studies for classification and detection tasks. According to the analysis results, SPM12 has a stable speed and success rate, and it always works as a guiding tool for newcomers to medical image analysis. It can preserve maximum contrast information, which will facilitate studies such as tumor diagnosis. FSL is a mature and widely applicable toolkit for users, with a relatively stable success rate and good performance. It has complete functions and its function-based integrated toolbox can meet the requirements of different researchers. AFNI is a flexible and complex tool that is more suitable for professional researchers. It retains most details in medical image analysis, which makes it useful in fine-grained analysis such as volume estimation. Our study provides a new idea for comparing registration tools, where tool selection strategy mainly depends on the research task in which the selected tool can leverage its unique advantages.

Safety issues in hybrid systems increasingly result from a combination of multiple categories of hazard factors. However, current hazard analysis methods mainly focus on just one kind of system hazard. This paper presents a model-based safety analysis method for hybrid system that is based on multiple hazard factor categories. Using a Model-Based Safety Analysis framework, the functional model of a hybrid system is first established. Its logic component is modeled using a state diagram and its continuous component is modeled using a transfer function or a state equation. The safety model of the system is then constructed considering a variety of hazard factors. Improper control and logic inconsistency are established for the logic component. Component deviation and logic inconsistency are established for the continuous component. Finally, the safety of the system is then analyzed and the hazard paths are obtained. As an example, the Wheel Brake System for an aircraft is taken to analyze the safety of the aircraft landing phase. By taking into account multiple hazard factor categories, we are able to identify hazard paths that could not be found using a single hazard factor category.

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

The computerized analysis and interpretation of three-dimensional medical images is of significant interest for diagnosis as well as for studying pathological processes. Knowledge-based image analysis and interpretation of radiological images can provide a tool for identifying and labeling each part of the image. The authors have developed a knowledge-based biomedical image analysis system for interpreting medical images using an anatomical knowledge base of the appropriate organs. In this paper, the structure of the biomedical image analysis system, along with results from the analysis of images of the human chest cavity, are presented. This approach utilizes an image analysis system with the capability of analyzing the data in both bottom-up (or data driven) and top-down (or model driven) modes to improve the recognition process. After an initial identification is achieved, segmented regions are aggregated and features for these aggregates are recomputed and matched to the model. This process continues until a 'best' match is found for the subject model region. Initial results are encouraging; however, much work remains to be done.

Linkage analysis of pedigree data is a powerful tool for mapping genomic regions that are likely to contain genes influencing human diseases. In this chapter, we will first introduce concepts and rationale of linkage analysis. Following this, we will then describe in detail two major types of linkage analysis strategies: model-based and model-free linkage analysis methods for qualitative traits. We will illustrate practical issues with linkage analysis by analysis of a real dataset collected from an age-related macular degeneration study. We will also describe how to identify the single nucleotide polymorphisms (SNPs) that account for linkage signal after linkage analysis is conducted. Finally, we will compare model-based and model-free linkage analysis methods and various software packages.