Medical Image Retrieval Using Visual and Semantic Features (original) (raw)
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Content-Based Image Retrieval in Medical Domain: A Review
Journal of Physics: Conference Series
Content-based Image Retrieval (CBIR) aids radiologist to identify similar medical images in recalling previous cases during diagnosis. Although several algorithms have been introduced to extract the content of the medical images, the process is still a challenge due to the nature of the feature itself where most of them are extracted in low level form. In addition to the dimensionality reduction problem caused by the low-level features, current features are also insufficient to convey the semantic meaning of the images. This paper reviews the recent works in CBIR that attempts to reduce the semantic gap in extracting the features from medical images, precisely for mammogram images. Approaches such as the use of relevance feedback, ontology as well as machine learning algorithms are summarized and discussed.
A Medical Image Retrieval System based on Semantic Annotations
Biomedical Engineering, 2013
This paper presents the design and implementation of a semantic Content Based Image Retrieval Systems (CBIR) developed in Matlab from scratch by choosing a combination of texture, color and shape as low level features to represent the images, and by using a multilabeling classifier to associate these low level features to a semantic label. We used the Bayes Point machine classifier to classify the images. The classification results are further enhanced by using an explicit relevance feedback algorithm. The system is tested on a set of medical images combined with other types of images and the results are presented.
Content based image retrieval in medical applications: an improvement of the two-level architecture
… 2009, EUROCON'09. …, 2009
In the past few years, immense improvement was obtained in the field of content-based image retrieval (CBIR). Nevertheless, existing systems still fail when applied to medical image databases. Simple feature-extraction algorithms that operate on the entire image for characterization of color, texture, or shape cannot be related to the descriptive semantics of medical knowledge that is extracted from images by human experts.
Classification driven semantic based medical image indexing and retrieval
1998
The motivation for our work is to develop a truly semantic-based image retrieval system that can discriminate between images differing only through subtle, domain-specific cues, which is a characteristic feature of many medical images. We propose a novel image retrieval framework centered around classification driven search for a good similarity metric (image index features) based on the image semantics rather than on appearance. Given a semantically well-defined image set, we argue that image classification and image ...
Semantic based categorization, browsing and retrieval in medical image databases
2002
Content-based retrieval (CBIR) methods in medical databases have been designed to support specific tasks, such as retrieval of digital mammograms or 3D MRI images. These methods cannot be transferred to other medical applications since different imaging modalities require different types of processing. To enable content-based queries in diverse collections of medical images, the retrieval system must be familiar with the current image class prior to the query processing. We describe a novel approach for the automatic categorization of medical images according to their modalities. We propose a semantically based set of visual features, their relevance and organization for capturing the semantics of different imaging modalities. The features are used in conjunction with a new categorization metric, enabling "intelligent" annotation, browsing/searching of medical databases. Our algorithm provides basic semantic knowledge about the image, and may serve as a front-end to the domain specific medical image analysis methods. To demonstrate the effectiveness of our approach, we have designed and implemented an Internet portal for browsing/querying online medical databases, and applied it to a large number of images. Our results demonstrate that accurate categorization can be achieved by exploiting the important visual properties of each modality.
Knowledge-Assisted Medical Image Retrieval
2007
In this paper, we present a knowledge-assisted approach to index and retrieve large volume of medical images. Both images and associated texts are indexed using medical concepts from the Unified Medical Language System (UMLS) metathesaurus. We propose a structured learning framework for modular acquisition of medical semantics from images with complementary global and local image indexing schemes. Two fusion approaches are also developed to improve text retrieval using the UMLS-based image indexing: a simple post-query fusion and a visual modality filtering to remove visually aberrant images according to the query modality concepts. On the ImageCLEFmed 2005 database, our framework outperformed our previous result which ranked top in the ImageCLEFmed 2005 Medical Image Retrieval task benchmark.
Content-based medical image retrieval using Low-Level visual features and modality identification
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008
This paper presents the image retrieval results obtained by the BioIngenium Research Group, in the frame of the ImageCLEFmed 2007 edition. The applied approach consists of two main phases: a preprocessing phase, which builds an image category index and a retrieval phase, which ranks similar images. Both phases are based only on visual information. The experiments show a consistent frame with theory in content-based image retrieval: filtering images with a conceptual index outperforms only-ranking-based strategies; combining features is better than using individual features; and low-level features are not enough to model image semantics.
International Journal of Research in Advent Technology, 2019
In medical sectors, medical imaging is animportant concept in real time environments. Different types of medical images are captured and stored in digital format in medical research centers. Facing this type of large volume of image data with different types of image modalities, it is very important to implement efficient content based image retrieval (CBIR) for medical research centers. Visual features based image label indexing is a limitation to explore efficient image retrieval from different medical sources. So that, in this paper, we propose Novel Multi Model Semantic Approach (NMMSA) is introduced based on recent and advanced machine learning and visual based indexing approaches. In this approach, we first investigate the semantic analysis to integrate visual features based label of image from different medical image sources to provide connection to retrieve images based on label indexing with visual features. Experimental results with high amount of medical images have been shown the performance of the proposed approach in terms of medical image indexing and image retrieval systems.
Effective of Modern Techniques on Content-Based Medical Image Retrieval: A Survey
IJCSMC, 2022
The advancement in medical imaging has resulted in a rapid and large increase in medical images inside repositories. These medical images contain very important information that can be used in many things, including diagnosing diseases. This implies that a precise, efficient way of indexing and retrieving biomedical images is necessary to obtain medical images from such repositories in real-time. CBMIR, therefore, played an important part, where the CBMIR's area is very important in the field of image processing and involves low-level feature extraction and similarity measures for the comparison of medical images such as color histograms, edges, texture, shape. The majority of the methods already in use in CBMIR enhance the retrieval of a medical image and diseases diagnosis by reducing the issue of the semantic gap between low visual and high semantic levels. Also, secure access to the medical image of diverse cases, which are often kept on a network and are susceptible to malicious attacks is considered an important target for all medical practitioners. So, most CBMIRs try to cover this target for the purpose of privacy preservation. So, in this survey, the most advanced (CBMIR) frameworks that were used to reduce the issue of semantic gaps, high dimensionality feature maps were covered, disease diagnosis, and medical image security. Furthermore, the different publicly and standard databases used in measuring the performance of these frameworks also were covered.
MIARS: A Medical Image Retrieval System
Journal of Medical Systems, 2010
The next generation of medical information system will integrate multimedia data to assist physicians in clinical decision-making, diagnoses, teaching, and research. This paper describes MIARS (Medical Image Annotation and Retrieval System). MIARS not only provides automatic annotation, but also supports text based as well as image based retrieval strategies, which play important roles in medical training, research, and diagnostics. The system utilizes three trained classifiers, which are trained using training images. The goal of these classifiers is to provide multi-level automatic annotation. Another main purpose of the MIARS system is to study image semantic retrieval strategy by which images can be retrieved according to different levels of annotation.