A new multi criteria decision making approach for medical imaging systems considering risk factors

Kulak, Osman; Gören, Hacer Güner; Supçiller, Aliye Ayça

doi:10.1016/j.asoc.2015.03.004

Cited by 61 publications

(40 citation statements)

References 36 publications

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“…In its conventional formulation, HFMEA is launched since anomalies historically detected in the system for similar procedures or identified during testing. The following coefficients are determined for each possible anomaly or fault [25]:…”

Section: Introduction To the Hfmea Methodologymentioning

confidence: 99%

Clinical risk evaluation of medical device software: an axiomatic design-based methodology

et al. 2019

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The increasing complexity of medical device (MD) management software requires the adoption of new methodological approaches that pay particular attention to safety issues. The risk analysis is one of the key activities to be carried out by the manufacturer before the development of the software application as it determines the type of documentation to be provided as well as the activities to be performed to place the MD on the market. After the definition of software requirements and their iterative transformation into architectural items and/or units, the manufacturer defines the safety class of each item. The adoption of an axiomatic design approach facilitates this process. This combination of techniques helps to focus the design of medical device software on non-conformities with a clear link to clinical risk. This objective can be achieved by assessing the complexity of the system to be designed, both in terms of its functional size, and as a level of overall clinical risk. In this multi-dimensional perspective, the software effort expressed in function points provides an estimate of the development cost. While the clinical risk analysis allows to quickly identify critical areas, to intervene with the same promptness and to draft a management plan according to the regulations of the various control authorities.MATEC Web of Conferences 301, 00005 (2019)

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Section: Introduction To the Hfmea Methodologymentioning

confidence: 99%

Clinical risk evaluation of medical device software: an axiomatic design-based methodology

et al. 2019

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“…The authors used segmentation of CT images of the abdominal body part for demonstrating parallel capabilities of k-means algorithm. Kulak et al 2015 [11] have developed the fuzzy axiomatic design also having the RFAD(risk factors) approach. The authors are the first to use this approach in comparing multi attributes of medical imaging systems.…”

Section: Related Workmentioning

confidence: 99%

A comparative study – medical imaging techniques: gpu-based or parallelized?

2017

IJLTET

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“…Medical imaging is viewed as a solution to examine human body parts to identify, monitor, or treat various diseases or injuries such as a tumor or an accidental injury . Medical imaging modalities are categorized broadly into functional and anatomical modalities .…”

Section: Introductionmentioning

confidence: 99%

“…Medical imaging is viewed as a solution to examine human body parts to identify, monitor, or treat various diseases or injuries such as a tumor or an accidental injury. 1 Medical imaging modalities are categorized broadly into functional and anatomical modalities. 2 Functional modalities like Positron emission tomography (PET) give information about functioning or working of organs/tissues such as oxygen utilization, blood circulation, and so on.…”

Section: Introductionmentioning

confidence: 99%

An improved multimodal medical image fusion scheme based on hybrid combination of nonsubsampled contourlet transform and stationary wavelet transform

Ramlal

Sachdeva

Ahuja

et al. 2019

Int J Imaging Syst Tech

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This research proposes an improved hybrid fusion scheme for non‐subsampled contourlet transform (NSCT) and stationary wavelet transform (SWT). Initially, the source images are decomposed into different sub‐bands using NSCT. The locally weighted sum of square of the coefficients based fusion rule with consistency verification is used to fuse the detailed coefficients of NSCT. The SWT is employed to decompose approximation coefficients of NSCT into different sub‐bands. The entropy of square of the coefficients and weighted sum‐modified Laplacian is employed as the fusion rules with SWT. The final output is obtained using inverse NSCT. The proposed research is compared with existing fusion schemes visually and quantitatively. From the visual analysis, it is observed that the proposed scheme retained important complementary information of source images in a better way. From the quantitative comparison, it is seen that this scheme gave improved edge information, clarity, contrast, texture, and brightness in the fused image.

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A new multi criteria decision making approach for medical imaging systems considering risk factors

Cited by 61 publications

References 36 publications

Clinical risk evaluation of medical device software: an axiomatic design-based methodology

Clinical risk evaluation of medical device software: an axiomatic design-based methodology

A comparative study – medical imaging techniques: gpu-based or parallelized?

An improved multimodal medical image fusion scheme based on hybrid combination of nonsubsampled contourlet transform and stationary wavelet transform

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