Exploring presentation methods for tomographic medical image viewing

Heyden, J. E. van der; Inkpen, Kori; Atkins, M. Stella; Carpendale, M. Sheelagh T.

doi:10.1016/s0933-3657(00)00102-0

Cited by 12 publications

(4 citation statements)

References 20 publications

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“…The main IV applications in medicine focus on effective data exploration [11], advanced rendering techniques [12], or immersive environments [13][14][15].…”

Section: In Medicinementioning

confidence: 99%

“…In [11], the authors propose an effective toolkit for the exploration of tomographic medical images on a computer screen by carefully designing three aspects: control, navigation, and details in context. Control provides the ability to interactively create user-defined image groups and to control group location, visibility, and display size.…”

Section: In Medicinementioning

confidence: 99%

See 1 more Smart Citation

Visual MRI: Merging information visualization and non-parametric clustering techniques for MRI dataset analysis

Castellani

Cristani

Combi

et al. 2008

Artificial Intelligence in Medicine

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SummaryObjective. This paper presents Visual MRI, an innovative tool for the magnetic resonance imaging (MRI) analysis of tumoral tissues. The main goal of the analysis is to separate each magnetic resonance image in meaningful clusters, highlighting zones which are more probably related with the cancer evolution. Such non-invasive analysis serves to address novel cancer treatments, resulting in a less destabilizing and more effective type of therapy than the chemotherapy-based ones. The advancements brought by Visual MRI are two: first, it is an integration of effective infor-

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“…The main IV applications in medicine focus on effective data exploration [11], advanced rendering techniques [12], or immersive environments [13][14][15].…”

Section: In Medicinementioning

confidence: 99%

Section: In Medicinementioning

confidence: 99%

Visual MRI: Merging information visualization and non-parametric clustering techniques for MRI dataset analysis

Castellani

Cristani

Combi

et al. 2008

Artificial Intelligence in Medicine

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“…As reported in [11], while interpreting the imaging data, radiologists usually follow a model for visual search that postulates a preattentive global analysis followed by fixations and discovery scanning. Therefore, only some structures, which typically occupy a small percentage of the data, are of interest, but their analysis requires contextual information like locations within a specific organ or adjacency to sensitive structures [12]. Therefore, although interesting features are typically localized and appear in a small region of interest (ROI), also the context, which comprises all that is not in the ROI, is equally important for medical inspection.…”

Section: Introductionmentioning

confidence: 99%

High-Fidelity Visualization of Large Medical Datasets on Commodity Hardware

Gallo

Placitelli

2013

ISRN Biomedical Engineering

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Recent advances in CT and MRI static and dynamic scanning techniques have led to great improvements in the resolution and size of volumetric medical datasets, and this trend is still ongoing. However, the explosion of dataset size prevents clinicians from taking advantage of an interactive, high-resolution exploration of volumetric medical data on commodity hardware, due to the memory constraints of modern graphics cards. This paper presents a hybrid CPU-GPU volume ray-casting method and some hybrid-based inspection tools aimed at providing interactive, medical-quality visualization using an ordinary desktop PC. Experimental results show that the hybrid method provides a near-interactive high-fidelity visualization of large medical datasets even if only limited hardware resources are available.

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“…This can be described as the problem of presenting information within the space available on a computer screen. Van der Heyden et al [3] explored several presentation issues in the development of medical imaging viewing systems to overcome computer screen size limitations. Mathie and Strickland, and Kim et al [4,5] explored the stack mode solution where images are stacked all one in top of the other and are viewed in a user controlled cine mode.…”

Section: Introductionmentioning

confidence: 99%

Medical Imaging Environment – A Multi-Agent System for a Computer Clustering Based Multi-display

Alves

Marreiros

Nelas³

et al.

Progress in Artificial Intelligence

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This paper presents a solution to minimize a problem that normally arises from the huge amount of images that a radiologist usually has to interpret. A multi-agent system that implements a multi-display for medical imaging based on computer clustering of normal personal computers is therefore described, as well as the multi-agent architecture that caters for the system evolution. An evaluation study was performed and its results are presented.

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Exploring presentation methods for tomographic medical image viewing

Cited by 12 publications

References 20 publications

Visual MRI: Merging information visualization and non-parametric clustering techniques for MRI dataset analysis

Visual MRI: Merging information visualization and non-parametric clustering techniques for MRI dataset analysis

High-Fidelity Visualization of Large Medical Datasets on Commodity Hardware

Medical Imaging Environment – A Multi-Agent System for a Computer Clustering Based Multi-display

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