ARTISAN : a prototype retrieval system for trade mark images

Abstract. Visual information retrieval is an emerging domain in the medical field as it has been in computer vision for more than ten years. It has the potential to help better managing the rising amount of visual medical data. One of the most frequent application fields for contentbased medical image retrieval (CBIR) is diagnostic aid. By submitting an image showing a certain pathology to a CBIR system, the medical expert can easily find similar cases. A major problem is the background surrounding the object in many medical images. System parameters of the imaging modalities are stored around the images in text as well as patient name or a logo of the institution. With such noisy input data, image retrieval often rather finds images where the object appears in the same area and is surrounded by similar structures. Whereas in specialised application domains, segmentation can focus the research on a particular area, PACS-like (Picture Archiving and Communication System) databases containing a large variety of images need a more general approach. This article describes an algorithm to extract the important object of the image to reduce the amount of data to be analysed for CBIR and focuses analysis to the important object. Most current solutions index the entire image without making a difference between object and background when using varied PACS-like databases or radiology teaching files. Our requirement is to have a fully automatic algorithm for object extraction. Medical images have the advantage to normally have one particular object more or less in the centre of the image. The database used for evaluating this task is taken from a radiology teaching file called casimage and the retrieval component is an open source retrieval engine called medGIFT.

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“…The data commonly analysed includes photographs, graphics, and videos. One typical application of image retrieval is trademark images [6].…”

Section: Introductionmentioning

confidence: 99%

Automated Object Extraction for Medical Image Retrieval Using the Insight Toolkit (ITK)

Müller

Heuberger

Depeursinge

et al. 2006

Information Retrieval Technology

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“…Approaches for information retrieval range from using purely visual features 4 to purely textual approaches based on associated text annotations, 5 with combined methods somewhere in between. Many restricted domains have been identified where specialised retrieval can have high impact, such as trademark retrieval 6 and the retrieval of medical images.…”

Section: Introductionmentioning

confidence: 99%

Using heterogeneous annotation and visual information for the benchmarking of image retrieval systems

et al. 2006

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Many image retrieval systems, and the evaluation methodologies of these systems, make use of either visual or textual information only. Only few combine textual and visual features for retrieval and evaluation. If text is used, it is often relies upon having a standardised and complete annotation schema for the entire collection. This, in combination with high-level semantic queries, makes visual/textual combinations almost useless as the information need can often be solved using just textual features. In reality, many collections do have some form of annotation but this is often heterogeneous and incomplete. Web-based image repositories such as FlickR even allow collective, as well as multilingual annotation of multimedia objects.This article describes an image retrieval evaluation campaign called ImageCLEF. Unlike previous evaluations, we offer a range of realistic tasks and image collections in which combining text and visual features is likely to obtain the best results. In particular, we offer a medical retrieval task which models exactly the situation of heterogenous annotation by combining four collections with annotations of varying quality, structure, extent and language. Two collections have an annotation per case and not per image, which is normal in the medical domain, making it difficult to relate parts of the accompanying text to corresponding images. This is also typical of image retrieval from the Web in which adjacent text does not always describe an image. The ImageCLEF benchmark shows the need for realistic and standardised datasets, search tasks and ground truths for visual information retrieval evaluation.

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Query by Visual Example: Assessing the Usability of Content-Based Image Retrieval System User Interfaces

Venters

Hartley

Cooper

et al. 2001

Advances in Multimedia Information Processing — PCM 2001

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ARTISAN : a prototype retrieval system for trade mark images

Cited by 3 publications

References 4 publications

Automated Object Extraction for Medical Image Retrieval Using the Insight Toolkit (ITK)

Automated Object Extraction for Medical Image Retrieval Using the Insight Toolkit (ITK)

Using heterogeneous annotation and visual information for the benchmarking of image retrieval systems

Query by Visual Example: Assessing the Usability of Content-Based Image Retrieval System User Interfaces

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