Medical Image Retrieval based on the Parallelization of the Cluster Sampling Algorithm

Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system.

One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text‐based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag‐of‐words approaches, in which the image retrieval problem turns into one of standard text‐based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical‐image‐dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical‐image‐dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image‐relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image‐relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state‐of‐the‐art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition.

Automatic medical image annotation and retrieval

In the evolving field of healthcare, centralized cloud-based medical image retrieval faces challenges related to security, availability, and adversarial threats. Existing deep learning-based solutions improve retrieval but remain vulnerable to adversarial attacks and quantum threats, necessitating a shift to more secure distributed cloud solutions. This article proposes SFMedIR, a secure and fault tolerant medical image retrieval framework that contains an adversarial attack-resistant federated learning for hashcode generation, utilizing a ConvNeXt-based model to improve accuracy and generalizability. The framework integrates quantum-chaos-based encryption for security, dynamic threshold-based shadow storage for fault tolerance, and a distributed cloud architecture to mitigate single points of failure. Unlike conventional methods, this approach significantly improves security and availability in cloud-based medical image retrieval systems, providing a resilient and efficient solution for healthcare applications. The framework is validated on Brain MRI and Kidney CT datasets, achieving a 60-70% improvement in retrieval accuracy for adversarial queries and an overall 90% retrieval accuracy, outperforming existing models by 5-10%. The results demonstrate superior performance in terms of both security and retrieval efficiency, making this framework a valuable contribution to the future of secure medical image management.

In this paper, we present an empirical study for monolingual medical image retrieval. In particular, we present a series of experiments in ImageCLEFmed 2009 task. There are three main goals. First, we evaluate traditional well-known weighting models in the text retrieval domain, such as BM25, TFIDF and Language Model (LM), for context-based image retrieval. Second, we evaluate statistical-based feedback models and ontology-based feedback models. Third, we investigate how content-based image retrieval can be integrated with these two basic technologies in traditional text retrieval domain. The experimental results have shown that: 1) traditional weighting models work well in context-based medical image retrieval task especially when the parameters are tuned properly; 2) statistical-based feedback models can further improve the retrieval performance when a small number of documents are used for feedback; however, the medical image retrieval can not benefit from ontology-based query expansion method used in this paper; 3) the retrieval performance can be slightly boosted via an integrated retrieval approach.

Medical image indexing and storage is gaining increased importance. Indexing and retrieval of these images efficiently is becomes an essential task. Indexing of medical images using text or numbers is a cumbersome task, difficult to memorize and time consuming. Indexing and Retrieval of images can be done through query by text and query by image which is also known as content based image retrieval (CBIR). Different medical images possess different texture and shape features. This paper presents a novel and hybrid approach of managing a huge medical image database and retrieving a medical image from the database by fusion of shape and texture features. Image is retrieved by comparing the features of query image and images of database. We attach some document information with each medical image such as patient identity, diseases, age, and case history. It can be used for further diagnostic and analysis purpose. Further the proposed algorithm also updates the database automatically if new query is found. MATLAB ® 7.01 and its image processing toolbox have been used to implement the algorithm.

The deep hash method has been proved to be the most efficient and approximate nearest neighbor search technique for large-scale image retrieval. However, the existing deep hashing methods have few cases in medical image retrieval, so we propose a deep hash network model for medical image retrieval. Firstly, in order to enhance the feature extraction and learning ability in the network model, we add SENet visual attention mechanism into each residual module of the backbone network ResNet50 for feature extraction. Then the extracted high-dimensional semantic features are added to the Cauchy hash module to generate a compact, centralized hash code, and then complete medical image retrieval. So as to enhance experimental comparison, the proposed method was validated experimentally on NUS-WIDE and MURA data sets. A large number of experiments verify that the proposed method is reliable and can be used for medical image retrieval. Compared with other existing hash algorithms, the retrieval performance of the proposed method has certain improved. The framework of the proposed method is shown in Figure 1.

We have submitted a secular variation (SV) candidate model for the thirteenth generation of International Geomagnetic Reference Field model (IGRF-13) using a data assimilation scheme and a magnetohydrodynamic (MHD) dynamo simulation code. This is the first contribution to the IGRF community from research groups in Japan. A geomagnetic field model derived from magnetic observatory hourly means, and CHAMP and Swarm-A satellite data, has been used as input data to the assimilation scheme. We adopt an ensemble-based assimilation scheme, called four-dimensional ensemble-based variational method (4DEnVar), which linearizes outputs of MHD dynamo simulation with respect to the deviation from a dynamo state vector at an initial condition. The data vector for the assimilation consists of the poloidal scalar potential of the geomagnetic field at the core surface and flow velocity field slightly below the core surface. Dimensionless time of numerical geodynamo is adjusted to the actual time by comparison of secular variation time scales. For SV prediction, we first generate an ensemble of dynamo simulation results from a free dynamo run. We then assimilate the ensemble to the data with a 10-year assimilation window through iterations, and finally forecast future SV by the weighted sum of the future extension parts of the ensemble members. Hindcast of the method for the assimilation window from 2004.50 to 2014.25 confirms that the linear approximation holds for 10-year assimilation window with our iterative ensemble renewal method. We demonstrate that the forecast performance of our data assimilation and forecast scheme is comparable with that of IGRF-12 by comparing data misfits 4.5 years after the release epoch. For estimation of our IGRF-13SV candidate model, we set assimilation window from 2009.50 to 2019.50. We generate our final SV candidate model by linear fitting for the weighted sum of the ensemble MHD dynamo simulation members from 2019.50 to 2025.00. We derive errors of our SV candidate model by one standard deviation of SV histograms based on all the ensemble members.

This paper presents a review of online systems for content-based medical image retrieval (CBIR). The objective of this review is to evaluate the capabilities and gaps in these systems and to determine ways of improving relevance of multi-modal (text and image) information retrieval in the iMedline system, being developed at the National Library of Medicine (NLM). Seven medical information retrieval systems: Figuresearch, BioText, GoldMiner, Yale Image Finder, Yottalook, Image Retrieval for Medical Applications (IRMA), and iMedline have been evaluated here using the system of gaps defined in [1]. Not all of these systems take advantage of the visual information contained in biomedical literature as figures and illustrations. However, all attempt to extract metadata about the image from the full-text of the articles and retrieve figures/images in response to a query. iMedline aims to advance the state-of-the-art in multimodal information retrieval by unifying image and text features in computing relevance. We discuss the shortcomings of these current systems and discuss future directions and next steps in iMedline toward context-based medical image retrieval.

As artificial intelligence and digital medicine increasingly permeate healthcare systems, robust governance frameworks are essential to ensure ethical, secure, and effective implementation. In this context, medical image retrieval becomes a critical component of clinical data management, playing a vital role in decision-making and safeguarding patient information. Existing methods usually learn hash functions using bottleneck features, which fail to produce representative hash codes from blended embeddings. Although contrastive hashing has shown superior performance, current approaches often treat image retrieval as a classification task, using category labels to create positive/negative pairs. Moreover, many methods fail to address the out-of-distribution (OOD) issue when models encounter external OOD queries or adversarial attacks. In this work, we propose a novel method to consolidate knowledge of hierarchical features and optimization functions. We formulate the knowledge consolidation by introducing Depth-aware Representation Fusion (DaRF) and Structure-aware Contrastive Hashing (SCH). DaRF adaptively integrates shallow and deep representations into blended features, and SCH incorporates image fingerprints to enhance the adaptability of positive/negative pairings. These blended features further facilitate OOD detection and content-based recommendation, contributing to a secure AI-driven healthcare environment. Moreover, we present a content-guided ranking to improve the robustness and reproducibility of retrieval results. Our comprehensive assessments demonstrate that the proposed method could effectively recognize OOD samples and significantly outperform existing approaches in medical image retrieval (p<0.05). In particular, our method achieves a 5.6-38.9% improvement in mean Average Precision on the anatomical radiology dataset.

Due to the huge growth of the World Wide Web, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the images through automatically extracting visual information of the medical images, which is commonly known as content-based image retrieval (CBIR). Since each feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Meanwhile, experiments demonstrate that a special feature is not equally important for different image queries. Most of existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. Having considered that a special feature is not equally important for different image queries, the proposed query dependent feature fusion method can learn different feature fusion models for different image queries only based on multiply image samples provided by the user, and the learned feature fusion models can reflect the different importances of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.

Digital imaging has revolutionized the field of medical imaging and has led to the development of sophisticated computer hardware technologies and specialized software that empower physicians to better distinguish abnormalities, characterize findings, supervise interventions and predict prognosis. In fact, CAD is one of the major research subjects in medical imaging and diagnostic radiology. These benefits have motivated researchers to develop dedicated systems to specific medical domains from clinical decision making to medical education and research. The medical imaging field has generated additional interest in methods and tools for the management and analysis of these images. It is important to extend such applications by supporting the retrieval of medical images by content.KeywordsComputer Aided DiagnosisHealth Database ManagementMedical Image RetrievalSemanticRelevance Feedback

This paper presents an effective and efficient framework for Crowd-assisted Mobile Similarity Retrieval of the large-scale medical images in the resource-constraint mobile telemedicine systems (MTS), called the CMSR. The CMSR processing works as follows: when a user submits a retrieval medical image IR, a buffer checking processing is first invoked to check if the full (or partial) retrieval results have been cached in the buffer previously. After that, a parallel image data filtering and refinement processing is conducted at a master node level. Finally, the candidate images are concurrently validated by a mCrowd system to derive an answer set that is transmitted to the retrieval node. To better facilitate the effective and efficient CMSR processing, three enabling techniques, i.e., category-based image data interleaving placement scheme, hindex-support image filtering algorithm and a kNN-based buffering scheme are developed. To improve the retrieval throughput, finally, we propose an extension of the CMSR method called mCMSR to optimize the multiple CMSRs. The experimental results show that the performances of the CMSR and the mCMSR methods are: 1) effective in improving the retrieval accuracy; 2) efficient in minimizing the response time by decreasing the network transmission cost while increasing the parallelism of I/O and CPU.

This work presents a new approach for classification and retrieval of echocardiographic images from textual information of the anatomical structures and diagnosis features. These textual attributes will be acquired from the electronic medical report generated in an integrated healthcare environment. The medical report is provided by a specialist in the area during the analysis of the medical image stored in a PACS environment. Such innovation guarantees a more accurate classifier and a better optimization of the medical work, since the medical report and the attributes for the medical image classifier will be created at the same time. The system is being developed in the University Hospital of the University of Brasilia.

Content Based Image Retrieval (CBIR) is the huge field for image retrieval from the wide databases. It is one of the popular techniques from computer vision domain. CBIR consist of feature extraction followed by similarity comparison. Shape feature extraction can be made on two descriptors one is region based and other is contour based. This paper implements shape feature extraction with region based descriptors. Zernike moments and Hu’s seven moments have been used as a feature extraction techniques and Support Vector Machine (SVM) is used as a classifier. Different distance metrics are then used for similarity comparison with these feature extraction methods for efficient results. For performance evaluation distance metrics used are Euclidean, Chebyshev, Cityblock, Canberra, Standardized Euclidean (Seuclidean). Medical database with 6 classes consist of 100 images each namely head, hip, shoulder, pelvis, knee, ankle is used. After obtaining all the experimental results in terms of precision and recall, a comparative study is made for selected database.