Integrated Multiple Features for Tumor Image Retrieval Using Classifier and Feedback Methods

AbstractThe content based image retrieval method greatly assists in retrieving medical images close to the query image from a large database basing on their visual features. This paper presents an effective approach in which the region of the object is extracted with the help of multiple features ignoring the background of the object by employing edge following segmentation method followed by extracting texture and shape characteristics of the images. The former is extracted with the help of Steerable filter at different orientations and radial Chebyshev moments are used for extracting the later. Initially the images similar to the query image are extracted from a large group of medical images. Then the search is by accelerating the retrieval process with the help of Support Vector Machine (SVM) classifier. The performance of the retrieval system is enhanced by adapting the subjective feedback method. The experimental results show that the proposed region based multiple features and integrated with classifier and subjective feedback method yields better results than classical retrieval systems

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Prototypes for Content-Based Image Retrieval in Clinical Practice

Content-based image retrieval (CBIR) has been proposed as key technology for computer-aided diagnostics (CAD). This paper reviews the state of the art and future challenges in CBIR for CAD applied to clinical practice

Enhancing Multimodal Information Retrieval Through Integrating Data Mining and Deep Learning Techniques

Multimodal information retrieval, the task of re trieving relevant information from heterogeneous data sources such as text, images, and videos, has gained significant attention in recent years due to the proliferation of multimedia content on the internet. This paper proposes an approach to enhance multimodal information retrieval by integrating data mining and deep learning techniques. Traditional information retrieval systems often struggle to effectively handle multimodal data due to the inherent complexity and diversity of such data sources. In this study, we leverage data mining techniques to preprocess and structure multimodal data efficiently. Data mining methods enable us to extract valuable patterns, relationships, and features from different modalities, providing a solid foundation for sub- sequent retrieval tasks. To further enhance the performance of multimodal information retrieval, deep learning techniques are employed. Deep neural networks have demonstrated their effectiveness in various multimedia tasks, including image recognition, natural language processing, and video analysis. By integrating deep learning models into our retrieval framework, we aim to capture complex intermodal dependencies and semantically rich representations, enabling more accurate and context-aware retrieval

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Recuperação multimodal e interativa de informação orientada por diversidade

Orientador: Ricardo da Silva TorresTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os métodos de Recuperação da Informação, especialmente considerando-se dados multimídia, evoluíram para a integração de múltiplas fontes de evidência na análise de relevância de itens em uma tarefa de busca. Neste contexto, para atenuar a distância semântica entre as propriedades de baixo nível extraídas do conteúdo dos objetos digitais e os conceitos semânticos de alto nível (objetos, categorias, etc.) e tornar estes sistemas adaptativos às diferentes necessidades dos usuários, modelos interativos que consideram o usuário mais próximo do processo de recuperação têm sido propostos, permitindo a sua interação com o sistema, principalmente por meio da realimentação de relevância implícita ou explícita. Analogamente, a promoção de diversidade surgiu como uma alternativa para lidar com consultas ambíguas ou incompletas. Adicionalmente, muitos trabalhos têm tratado a ideia de minimização do esforço requerido do usuário em fornecer julgamentos de relevância, à medida que mantém níveis aceitáveis de eficácia. Esta tese aborda, propõe e analisa experimentalmente métodos de recuperação da informação interativos e multimodais orientados por diversidade. Este trabalho aborda de forma abrangente a literatura acerca da recuperação interativa da informação e discute sobre os avanços recentes, os grandes desafios de pesquisa e oportunidades promissoras de trabalho. Nós propusemos e avaliamos dois métodos de aprimoramento do balanço entre relevância e diversidade, os quais integram múltiplas informações de imagens, tais como: propriedades visuais, metadados textuais, informação geográfica e descritores de credibilidade dos usuários. Por sua vez, como integração de técnicas de recuperação interativa e de promoção de diversidade, visando maximizar a cobertura de múltiplas interpretações/aspectos de busca e acelerar a transferência de informação entre o usuário e o sistema, nós propusemos e avaliamos um método multimodal de aprendizado para ranqueamento utilizando realimentação de relevância sobre resultados diversificados. Nossa análise experimental mostra que o uso conjunto de múltiplas fontes de informação teve impacto positivo nos algoritmos de balanceamento entre relevância e diversidade. Estes resultados sugerem que a integração de filtragem e re-ranqueamento multimodais é eficaz para o aumento da relevância dos resultados e também como mecanismo de potencialização dos métodos de diversificação. Além disso, com uma análise experimental minuciosa, nós investigamos várias questões de pesquisa relacionadas à possibilidade de aumento da diversidade dos resultados e a manutenção ou até mesmo melhoria da sua relevância em sessões interativas. Adicionalmente, nós analisamos como o esforço em diversificar afeta os resultados gerais de uma sessão de busca e como diferentes abordagens de diversificação se comportam para diferentes modalidades de dados. Analisando a eficácia geral e também em cada iteração de realimentação de relevância, nós mostramos que introduzir diversidade nos resultados pode prejudicar resultados iniciais, enquanto que aumenta significativamente a eficácia geral em uma sessão de busca, considerando-se não apenas a relevância e diversidade geral, mas também o quão cedo o usuário é exposto ao mesmo montante de itens relevantes e nível de diversidadeAbstract: Information retrieval methods, especially considering multimedia data, have evolved towards the integration of multiple sources of evidence in the analysis of the relevance of items considering a given user search task. In this context, for attenuating the semantic gap between low-level features extracted from the content of the digital objects and high-level semantic concepts (objects, categories, etc.) and making the systems adaptive to different user needs, interactive models have brought the user closer to the retrieval loop allowing user-system interaction mainly through implicit or explicit relevance feedback. Analogously, diversity promotion has emerged as an alternative for tackling ambiguous or underspecified queries. Additionally, several works have addressed the issue of minimizing the required user effort on providing relevance assessments while keeping an acceptable overall effectiveness. This thesis discusses, proposes, and experimentally analyzes multimodal and interactive diversity-oriented information retrieval methods. This work, comprehensively covers the interactive information retrieval literature and also discusses about recent advances, the great research challenges, and promising research opportunities. We have proposed and evaluated two relevance-diversity trade-off enhancement work-flows, which integrate multiple information from images, such as: visual features, textual metadata, geographic information, and user credibility descriptors. In turn, as an integration of interactive retrieval and diversity promotion techniques, for maximizing the coverage of multiple query interpretations/aspects and speeding up the information transfer between the user and the system, we have proposed and evaluated a multimodal learning-to-rank method trained with relevance feedback over diversified results. Our experimental analysis shows that the joint usage of multiple information sources positively impacted the relevance-diversity balancing algorithms. Our results also suggest that the integration of multimodal-relevance-based filtering and reranking was effective on improving result relevance and also boosted diversity promotion methods. Beyond it, with a thorough experimental analysis we have investigated several research questions related to the possibility of improving result diversity and keeping or even improving relevance in interactive search sessions. Moreover, we analyze how much the diversification effort affects overall search session results and how different diversification approaches behave for the different data modalities. By analyzing the overall and per feedback iteration effectiveness, we show that introducing diversity may harm initial results whereas it significantly enhances the overall session effectiveness not only considering the relevance and diversity, but also how early the user is exposed to the same amount of relevant items and diversityDoutoradoCiência da ComputaçãoDoutor em Ciência da ComputaçãoP-4388/2010140977/2012-0CAPESCNP

Dual-Force ISOMAP: A New Relevance Feedback Method for Medical Image Retrieval

With great potential for assisting radiological image interpretation and decision making, content-based image retrieval in the medical domain has become a hot topic in recent years. Many methods to enhance the performance of content-based medical image retrieval have been proposed, among which the relevance feedback (RF) scheme is one of the most promising. Given user feedback information, RF algorithms interactively learn a user?s preferences to bridge the ?semantic gap? between low-level computerized visual features and high-level human semantic perception and thus improve retrieval performance. However, most existing RF algorithms perform in the original high-dimensional feature space and ignore the manifold structure of the low-level visual features of images. In this paper, we propose a new method, termed dual-force ISOMAP (DFISOMAP), for content-based medical image retrieval. Under the assumption that medical images lie on a low-dimensional manifold embedded in a high-dimensional ambient space, DFISOMAP operates in the following three stages. First, the geometric structure of positive examples in the learned low-dimensional embedding is preserved according to the isometric feature mapping (ISOMAP) criterion. To precisely model the geometric structure, a reconstruction error constraint is also added. Second, the average distance between positive and negative examples is maximized to separate them; this margin maximization acts as a force that pushes negative examples far away from positive examples. Finally, the similarity propagation technique is utilized to provide negative examples with another force that will pull them back into the negative sample set. We evaluate the proposed method on a subset of the IRMA medical image dataset with a RF-based medical image retrieval framework. Experimental results show that DFISOMAP outperforms popular approaches for content-based medical image retrieval in terms of accuracy and stability

Content Based Image Retrieval (CBIR) in Remote Clinical Diagnosis and Healthcare

Content-Based Image Retrieval (CBIR) locates, retrieves and displays images alike to one given as a query, using a set of features. It demands accessible data in medical archives and from medical equipment, to infer meaning after some processing. A problem similar in some sense to the target image can aid clinicians. CBIR complements text-based retrieval and improves evidence-based diagnosis, administration, teaching, and research in healthcare. It facilitates visual/automatic diagnosis and decision-making in real-time remote consultation/screening, store-and-forward tests, home care assistance and overall patient surveillance. Metrics help comparing visual data and improve diagnostic. Specially designed architectures can benefit from the application scenario. CBIR use calls for file storage standardization, querying procedures, efficient image transmission, realistic databases, global availability, access simplicity, and Internet-based structures. This chapter recommends important and complex aspects required to handle visual content in healthcare.Comment: 28 pages, 6 figures, Book Chapter from "Encyclopedia of E-Health and Telemedicine