Overview of the CLEF 2009 Medical Image Retrieval Track

Müller, Henning; Kalpathy–Cramer, Jayashree; Eggel, Ivan; Bedrick, Steven; Radhouani, Saïd; Bakke, Brian; Kahn, Charles Η.; Hersh, William R.

doi:10.1007/978-3-642-15751-6_8

Cited by 96 publications

(57 citation statements)

References 5 publications

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“…As opposed to this, the importance of solid benchmarking protocols and publicly available databases has been confirmed repeatedly, e.g., in the machine vision and image analysis communities [8], [9], where the consensus is that it promotes comparison between methods and pushes forward progress. In addition to this, in the studies presented so far the number of considered movements is typically quite far from the dexterity of a human hand or from what a patient would need in daily life; at the same time, acquiring data only from few subjects makes it difficult to evaluate the generality of the obtained results across gender, age, characteristics of the amputation, etc.…”

Section: Introductionmentioning

confidence: 99%

Building the Ninapro database: A resource for the biorobotics community

Atzori

Gijsberts

Heynen

et al. 2012

2012 4th IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

210

152

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Abstract-This paper is about (self-powered) advanced hand prosthetics and their control via surface electromyography (sEMG). We hereby introduce to the biorobotics community the first version of the NINAPRO database, containing kinematic and sEMG data from the upper limbs of 27 intact subjects while performing 52 finger, hand and wrist movements of interest. The setup and experimental protocol are distilled from existing literature and thoroughly described; the data are then analysed and the results are discussed. In particular, it is clear that standard analysis techniques are no longer enough when so many subjects and movements are taken into account. The database is publicly available to download in standard ASCII format.The database is an ongoing work lasting several years, which is planned to contain data from more than 100 intact subjects and 50 trans-radial amputees; characteristics of the amputations, phantom limbs and prosthesis usage will be stored. We therefore hope that it will constitute a standard, widely accepted benchmark for all novel myoelectric hand prosthesis control methods, as well as a fundamental tool to deliver insight into the needs of trans-radial amputees.

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Section: Introductionmentioning

confidence: 99%

Building the Ninapro database: A resource for the biorobotics community

Atzori

Gijsberts

Heynen

et al. 2012

2012 4th IEEE RAS &Amp; EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

210

152

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“…Our investigation was conducted under the auspices of the ImageCLEF 2010 medical image retrieval challenge [5]. The medical task within ImageCLEF was initiated in 2004 with the goal of providing an evaluation forum for researchers in both text-and content-based image retrieval techniques to compare their techniques using a common dataset and a well understood set of evaluation metrics.…”

Section: Methodsmentioning

confidence: 99%

“…The lack of standardized test collections has limited medical image retrieval research [3]. The annual ImageCLEF conference, begun in 2003 as part of the Cross-Language Evaluation Forum (CLEF), has addressed the need for standardized test collections and multi-institutional evaluation forums, and has grown to become the pre-eminent venue for image retrieval evaluation [4,5].…”

Section: Introductionmentioning

confidence: 99%

Accurate Determination of Imaging Modality using an Ensemble of Text- and Image-Based Classifiers

et al. 2011

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Imaging modality can aid retrieval of medical images for clinical practice, research, and education. We evaluated whether an ensemble classifier could outperform its constituent individual classifiers in determining the modality of figures from radiology journals. Seventeen automated classifiers analyzed 77,495 images from two radiology journals. Each classifier assigned one of eight imaging modalities-computed tomography, graphic, magnetic resonance imaging, nuclear medicine, positron emission tomography, photograph, ultrasound, or radiographto each image based on visual and/or textual information. Three physicians determined the modality of 5,000 randomly selected images as a reference standard. A "Simple Vote" ensemble classifier assigned each image to the modality that received the greatest number of individual classifiers' votes. A "Weighted Vote" classifier weighted each individual classifier's vote based on performance over a training set. For each image, this classifier's output was the imaging modality that received the greatest weighted vote score. We measured precision, recall, and F score (the harmonic mean of precision and recall) for each classifier.Individual classifiers' F scores ranged from 0.184 to 0.892. The simple vote and weighted vote classifiers correctly assigned 4,565 images (F score, 0.913; 95% confidence interval, 0.905-0.921) and 4,672 images (F score, 0.934; 95% confidence interval, 0.927-0.941), respectively. The weighted vote classifier performed significantly better than all individual classifiers. An ensemble classifier correctly determined the imaging modality of 93% of figures in our sample. The imaging modality of figures published in radiology journals can be determined with high accuracy, which will improve systems for image retrieval.

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“…Even when services like TinEye, GazoPa or Google Images offer content-based visual searches, the query text based or keywords search are still the preferred way to access web images. This fact is supported by the high accuracy that text query-based systems can get compared to visual query-based systems [16].…”

Section: Related Workmentioning

confidence: 96%

Using ephemeral clustering and query logs to organize web image search results on mobile devices

Moreno

Dias

2011

Proceedings of the 2011 International ACM Workshop on Interactive Multimedia on Mobile and Portable Devices

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The recent shift in human-computer interaction from desktop to mobile computing fosters the needs of new interfaces for web image search results exploration. In this paper, we present two different strategies to cluster results gathered from an image search engine and propose an adapted interface for handled devices. For that purpose, we suggest to expand the original query based on labels of Ephemeral Clusters and compare it to a Query Log based approach. Consistent results were obtained for both strategies from manual and automatic evaluations, confirming that organizing image search results into clusters can improve mobile image information retrieval.

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Overview of the CLEF 2009 Medical Image Retrieval Track

Cited by 96 publications

References 5 publications

Building the Ninapro database: A resource for the biorobotics community

Building the Ninapro database: A resource for the biorobotics community

Accurate Determination of Imaging Modality using an Ensemble of Text- and Image-Based Classifiers

Using ephemeral clustering and query logs to organize web image search results on mobile devices

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