A study on the relevance criteria for medical images

Sedghi, Shahram; Sanderson, Mark; Clough, Paul

doi:10.1016/j.patrec.2008.07.003

Cited by 17 publications

(14 citation statements)

References 21 publications

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“…In 2008, Sedghi et al, performed a user study with 26 participants in order to investigate the relevance criteria used by health care professionals to make relevance judgments when searching for medical images [36]. In total, 26 criteria were identified; amongst them, visual relevancy, background information and image quality were the most frequent criteria used by participants.…”

Section: Motivationmentioning

confidence: 99%

Evaluating performance of biomedical image retrieval systems—An overview of the medical image retrieval task at ImageCLEF 2004–2013

Kalpathy–Cramer¹,

Herrera²,

Demner‐Fushman³

et al. 2015

Computerized Medical Imaging and Graphics

102

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Medical image retrieval and classification have been extremely active research topics over the past 15 years. With the ImageCLEF benchmark in medical image retrieval and classification a standard test bed was created that allows researchers to compare their approaches and ideas on increasingly large and varied data sets including generated ground truth. This article describes the lessons learned in ten evaluations campaigns. A detailed analysis of the data also highlights the value of the resources created.

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Section: Motivationmentioning

confidence: 99%

Evaluating performance of biomedical image retrieval systems—An overview of the medical image retrieval task at ImageCLEF 2004–2013

Kalpathy–Cramer¹,

Herrera²,

Demner‐Fushman³

et al. 2015

Computerized Medical Imaging and Graphics

102

View full text Add to dashboard Cite

show abstract

“…In another paper, Selvarani and Annadurai [14] discuss general Fourier descriptor (GFD) for shape representation for the purpose of medical image retrieval. Sedghi et al [15] discuss the relevance criteria for searching of various medical images.…”

Section: Retrieval Of Fracture Containing Slicesmentioning

confidence: 99%

Computer Vision Based Hairline Mandibular Fracture Detection from Computed Tomography Images

Chowdhury

Mukherjee²,

Bhandarkar

et al. 2013

Advanced Computational Approaches to Biomedical Engineering

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This chapter addresses the problem of detection of hairline mandibular fractures from a sequence of computed tomography (CT) images. It has been observed that such a fracture can be easily overlooked during manual detection due to the absence of sharp surface and contour discontinuities and the presence of intensity inhomogeneity in the CT images. In this work, the 2D CT image slices of a mandible with hairline fractures are first identified from the input sequence of a fractured craniofacial skeleton. Two intensity-based image retrieval schemes with different measures of similarity, namely the Jaccard index and the Kolmogorov-Smirnov distance, are applied for that purpose. In the second part, we detect a hairline fracture in the previously identified subset of images using the maximum flow-minimum cut algorithm. Since a hairline fracture is essentially a discontinuity in the bone contour, we model it as a minimum cut in an appropriately weighted flow network constructed using the geometry of the human mandible. The Ford-Fulkerson algorithm with Edmonds-Karp refinement is employed to obtain a minimum cut. Experimental results demonstrate the effectiveness of the proposed method.A.S. Chowdhury ( )

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“…A growing number of clinicians, educators, researchers, and other professionals use digital images in their work [20]; biomedical images are often referenced for clinical decision support, educational purposes, and research [21], and specialized collections of biomedical images are considered of value for research and training purposes [22]. Health care professionals regularly search for images published in medical journals to find specialized information [23].…”

Section: Introductionmentioning

confidence: 99%

Do Physicians Make Their Articles Readable for Their Blind or Low-Vision Patients? An Analysis of Current Image Processing Practices in Biomedical Journals from the Point of View of Accessibility

et al. 2014

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Visual content in biomedical academic papers is a growing source of critical information, but it is not always fully readable for people with visual impairments. We aimed to assess current image processing practices, accessibility policies, and submission policies in a sample of 12 highly cited biomedical journals. We manually checked the application of text-based alternative image descriptions for every image in 12 articles (one for each journal). We determined whether the journals claimed to follow an accessibility policy and we reviewed their submission policy and their guidelines related to the visual content. We identified important features concerning the processing of images and the characteristics of the visual and the retrieval options of visual content offered by the publishers. The evaluation shows that the actual practices of textual image description in highly cited biomedical journals do not follow general guidelines on accessibility. The images within the articles analyzed lack alternative descriptions or have uninformative descriptions, even in the case of journals claiming to follow an accessibility policy. Consequently, the visual information of scientific articles is not accessible to people with severe visual disabilities.Instructions on image submission are heterogeneous and a declaration of accessibility guidelines was only found in two thirds of the sample of journals, with one third not explicitly following any accessibility policy, although they are required to by law.

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A study on the relevance criteria for medical images

Cited by 17 publications

References 21 publications

Evaluating performance of biomedical image retrieval systems—An overview of the medical image retrieval task at ImageCLEF 2004–2013

Evaluating performance of biomedical image retrieval systems—An overview of the medical image retrieval task at ImageCLEF 2004–2013

Computer Vision Based Hairline Mandibular Fracture Detection from Computed Tomography Images

Do Physicians Make Their Articles Readable for Their Blind or Low-Vision Patients? An Analysis of Current Image Processing Practices in Biomedical Journals from the Point of View of Accessibility

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