Ontology of Gaps in Content-Based Image Retrieval

Deserno, Thomas M.; Antani, Sameer; Long, L. Rodney

doi:10.1007/s10278-007-9092-x

Cited by 119 publications

(88 citation statements)

References 24 publications

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“…Systems, architectures, and protocols are available to constitute reliable repositories of reference images and signals, including the enriched management of case data and reference data sets by means of CBIR [1,28]. Comprehensive and reliable evaluation will yield more robust algorithms, and image-or signal-based CAD schemes will be incorporated into PACS and assembled as a package for the detection of lesions and for differential diagnosis [29,30].…”

Section: Discussionmentioning

confidence: 99%

Towards a Repository for Standardized Medical Image and Signal Case Data Annotated with Ground Truth

Deserno

Welter

Horsch³

2011

J Digit Imaging

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Validation of medical signal and image processing systems requires quality-assured, representative and generally acknowledged databases accompanied by appropriate reference (ground truth) and clinical metadata, which are composed laboriously for each project and are not shared with the scientific community. In our vision, such data will be stored centrally in an open repository. We propose an architecture for a standardized case data and ground truth information repository supporting the evaluation and analysis of computer-aided diagnosis based on (a) the Reference Model for an Open Archival Information System (OAIS) provided by the NASA Consultative Committee for Space Data Systems (ISO 14721:2003), (b) the Dublin Core Metadata Initiative (DCMI) Element Set (ISO 15836:2009), (c) the Open Archive Initiative (OAI) Protocol for Metadata Harvesting, and (d) the Image Retrieval in Medical Applications (IRMA) framework. In our implementation, a portal bunches all of the functionalities that are needed for data submission and retrieval. The complete life cycle of the data (define, create, store, sustain, share, use, and improve) is managed. Sophisticated search tools make it easier to use the datasets, which may be merged from different providers. An integrated history record guarantees reproducibility. A standardized creation report is generated with a permanent digital object identifier. This creation report must be referenced by all of the data users. Peer-reviewed e-publishing of these reports will create a reputation for the data contributors and will form de-facto standards regarding image and signal datasets. Good practice guidelines for validation methodology complement the concept of the case repository. This procedure will increase the comparability of evaluation studies for medical signal and image processing methods and applications.

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Section: Discussionmentioning

confidence: 99%

Towards a Repository for Standardized Medical Image and Signal Case Data Annotated with Ground Truth

Deserno

Welter

Horsch³

2011

J Digit Imaging

Self Cite

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“…The performance of the proposed system is evaluated on different query images using Precision, and Recall [28,29] subsequently these values are compared with the Precision, Recall and F_measure values of conventional hierarchical clustering. We have used the Precision and Recall described in [28,29] for evaluating the performance of the proposed content based image retrieval system.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…We have used the Precision and Recall described in [28,29] for evaluating the performance of the proposed content based image retrieval system. The precision Eq.…”

Section: Performance Evaluationmentioning

confidence: 99%

Content based Image Retrieval System with Hybrid Feature Set and Recently Retrieved Image Library

Jadhav¹,

Ahmed²

2012

IJCA

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Content based image retrieval system is a fast growing research area, where the visual content of a query image is used to search images from large scale image databases. In this proposed an effective system, both the semantically and visually relevant features are used to retrieve the related images. The challenge for the CBIR system is how to efficiently capture the features of the query image for retrieval. In traditional content based retrieval system, the visual content features of the whole query image are used for the retrieval purpose. But in the proposed system, the object wise features of query image are utilized for the effective retrieval. Moreover, an active Recently Retrieved Image Library (RRI Library) is used, which increases the accuracy in each retrieval. An RRI library uses an index system, which maintains the recently retrieved images, and during the retrieval process, the proposed system searches the pertinent images from both the database as well as the RRI library and hence the retrieval precision is gradually increased in each retrieval. The proposed CBIR method is evaluated by querying diverse images and the retrieval efficacy is analyzed by calculating the precision-recall values for the retrieval results.

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“…As a result, the extracted visual features may not directly link to the target image category (semantic concept). It is the so-called semantic gap [21]. To address this issue, we use the distance metric learning (DML) methods to obtain the image similarity associated with semantic concepts in the BoW feature space.…”

Section: Introductionmentioning

confidence: 99%

Content-Based Retrieval of Focal Liver Lesions Using Bag-of-Visual-Words Representations of Single- and Multiphase Contrast-Enhanced CT Images

Yang

et al. 2012

J Digit Imaging

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This paper is aimed at developing and evaluating a content-based retrieval method for contrastenhanced liver computed tomographic (CT) images using bag-of-visual-words (BoW) representations of single and multiple phases. The BoW histograms are extracted using the raw intensity as local patch descriptor for each enhance phase by densely sampling the image patches within the liver lesion regions. The distance metric learning algorithms are employed to obtain the semantic similarity on the Hellinger kernel feature map of the BoW histograms. The different visual vocabularies for BoW and learned distance metrics are evaluated in a contrast-enhanced CT image dataset comprised of 189 patients with three types of focal liver lesions, including 87 hepatomas, 62 cysts, and 60 hemangiomas. For each single enhance phase, the mean of average precision (mAP) of BoW representations for retrieval can reach above 90 % which is significantly higher than that of intensity histogram and Gabor filters. Furthermore, the combined BoW representations of the three enhance phases can improve mAP to 94.5 %. These preliminary results demonstrate that the BoW representation is effective and feasible for retrieval of liver lesions in contrast-enhanced CT images.

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Ontology of Gaps in Content-Based Image Retrieval

Cited by 119 publications

References 24 publications

Towards a Repository for Standardized Medical Image and Signal Case Data Annotated with Ground Truth

Towards a Repository for Standardized Medical Image and Signal Case Data Annotated with Ground Truth

Content based Image Retrieval System with Hybrid Feature Set and Recently Retrieved Image Library

Content-Based Retrieval of Focal Liver Lesions Using Bag-of-Visual-Words Representations of Single- and Multiphase Contrast-Enhanced CT Images

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