Medical image informatics infrastructure design and applications

1997 ◽  
Vol 22 (4) ◽  
pp. 279-289 ◽  
Author(s):  
H. K. Huang ◽  
S. T. C. Wong ◽  
E. Pietka
2013 ◽  
Vol 6 (3) ◽  
pp. 30-33
Author(s):  
Anant Madabhushi ◽  
Satish Viswanath ◽  
George Lee ◽  
Pallavi Tiwari

2019 ◽  
Vol 117 (3) ◽  
pp. 412 ◽  
Author(s):  
A. Umamageswari ◽  
M. A. Leo Vijilious

Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.


2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Mona Lundin

This study explores the use of a new protocol in hypertension care, in which continuous patient-generated data reported through digital technology are presented in graphical form and discussed in follow-up consultations with nurses. This protocol is part of an infrastructure design project in which patients and medical professionals are co-designers. The approach used for the study was interaction analysis, which rendered possible detailed in situ examination of local variations in how nurses relate to the protocol. The findings show three distinct engagements: (1) teasing out an average blood pressure, (2) working around the protocol and graph data and (3) delivering an analysis. It was discovered that the graphical representations structured the consultations to a great extent, and that nurses mostly referred to graphs that showed blood pressure values, which is a measurement central to the medical discourse of hypertension. However, it was also found that analysis of the data alone was not sufficient to engage patients: nurses' invisible and inclusion work through eliciting patients' narratives played an important role here. A conclusion of the study is that nurses and patients both need to be more thoroughly introduced to using protocols based on graphs for more productive consultations to be established. 


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