How do health care professionals select medical images they need?

Sedghi, Shahram; Sanderson, Mark; Clough, Paul

doi:10.1108/00012531211244815

Cited by 15 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Healthcare professionals determine relevance through a combination of textual background information, visual inspection, and mental comparison to imagined ideals (Sedghi, Sanderson, & Clough, 2012). Personal experience trumps other criteria, however, when determining image relevance (Markonis et al, 2012;Müller et al, 2006).…”

Section: Biomedicinementioning

confidence: 99%

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

Gregory

Groth

Cousijn

et al. 2019

Asso for Info Science & Tech

View full text Add to dashboard Cite

A cross‐disciplinary examination of the user behaviors involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data in selected disciplines. Two analytical frameworks, rooted in information retrieval and science and technology studies, are used to identify key similarities in practices as a first step toward developing a model describing data retrieval.

show abstract

Section: Biomedicinementioning

confidence: 99%

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

Gregory

Groth

Cousijn

et al. 2019

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…And intensity of the light is nothing but the Value. These three dimensional representation with the use of HSV color space is termed as a hexacone, and intensity is represented by the central vertical axis [9]. Hue is defined as an angle in the range [0,2π] relative to the Red axis with red at angle 0, green at 2π/3, blue at 4π/3 and red again at 2π.…”

Section: Figure 1 Hsv Planes Of Color Imagementioning

confidence: 99%

“…The question of image similarity has important applications in the medical domain because diagnostic decision-making has traditionally involved using evidence from a patient's data (image and non image) coupled with the physician's prior experiences of similar cases [8]. A recent study [9] has shown that manual selection of these similar cases is primarily based upon visual properties. It has been suggested that the reliance on imaging for various clinical workflows means that access to relevant stored data will allow for more informed and effective treatment [10].…”

Section: Introductionmentioning

confidence: 99%

Rough Texton based Fundus Image Retrieval

Sadarajupalli¹,

Putheti²

2015

IJCA

View full text Add to dashboard Cite

Content based image retrieval (CBIR), is a robust technique widely used in the field of image retrieval. This method uses visual contents like color, texture and shape, to search images from a large scale database of images. Among the primary image contents, texture is an important spatial feature. Texton is a statistical approach used to analyze the texture of an image. Texture-based approach proposed here can take into account the vagueness of images also while retrieving images just as an expert manually retrieves medical images.

show abstract

“…In clinical environments, the selection of similar studies is primarily based upon topicality, i.e., whether or not the images contain the same subject matter; judging whether a study is topical is primarily decided upon using the visual characteristics of the images [15]. However, the search capabilities provided by PACS are based on textual keywords, such as patient names, identifiers, and image device.…”

Section: Motivationmentioning

confidence: 99%

A graph-based approach for the retrieval of multi-modality medical images

Kumar

Kim

Wen

et al. 2014

Medical Image Analysis

View full text Add to dashboard Cite

Advances in sensors and imaging technologies are contributing to rapidly expanding data repositories that contain interrelated information from different modalities. The extraction and visualisation of knowledge from these repositories is a major challenge in the modern, digital world. In the medical domain, images are routinely acquired for a variety of tasks, including diagnosis and patient monitoring. Advances in imaging technologies have resulted in devices capable of acquiring images in multiple dimensions (volumetric and dynamic) as well as from multiple modalities. One example of a widely used volumetric and multimodality image is combined positron emission tomography and computed tomography (PET-CT), which presents physicians with complementary functional and anatomical features and spatial relationships. In clinical practice, PET-CT imaging has already proven its ability to improve cancer diagnosis, localisation, and staging compared to its single-modality counterparts.The clinical benefits provided by medical imaging have spurred increases in the data volume acquired in clinical environments. As such, massive medical imaging collections offer the opportunity for search-based applications in evidence-based diagnosis, physician training, and biomedical research. However, conventional search techniques that operate upon manually assigned textual annotations are not feasible for the volume of data acquired in modern hospitals. Qualitative text descriptions are also limited in their capacity to quantitatively describe the rich information inherent in medical images. Content-based image retrieval (CBIR) is an image search technique that utilises visual features as search criteria. CBIR has already demonstrated benefits forevidence-based diagnosis, physician training, and biomedical research by allowing clinical staff to consider relevant knowledge from retrieved cases. The majority of medical CBIR research has focused on single modality medical images leaving a clear deficiency in the retrieval of multi-modality images. In particular, images iii like PET-CT offer the ability for retrieval based upon the relationships between regions in different modalities, such as the location of tumour features (from PET) in relation to organ features (from CT). The challenge of multi-modality image retrieval for cancer patients lies in representing these complementary geometric and topologic attributes between tumours and organs. A secondary challenge lies in the human aspect of retrieval -effectively communicating the retrieved results to users and facilitating a better understanding of the similarity between the query and retrieved multi-modality images.As such, in this thesis we propose a new graph representation for multimodality images. Our representation preserves the spatial relationships between modalities by emphasising the inherent characteristics of these images that are used for disease staging and classification. This is done by structurally constraining the graph based on image features, e.g., spatia...

show abstract

How do health care professionals select medical images they need?

Cited by 15 publications

References 30 publications

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

Rough Texton based Fundus Image Retrieval

A graph-based approach for the retrieval of multi-modality medical images

Contact Info

Product

Resources

About