Computer Assisted Immunohistochemical Score Prediction via Simplified Image Acquisition Technique

2017 ◽  
Vol 29 (1) ◽  
pp. 83-88
Author(s):  
Salam N. Jawad ◽  
Bashar H. Abdullah
Keyword(s):  
Image Acquisition ◽  
Computer Assisted ◽  
Acquisition Technique

scholarly journals 3D GRASE PROPELLER: Improved image acquisition technique for arterial spin labeling perfusion imaging

2011 ◽  
Vol 66 (1) ◽  
pp. 168-173 ◽  
Author(s):  
Huan Tan ◽  
W. Scott Hoge ◽  
Craig A. Hamilton ◽  
Matthias Günther ◽  
Robert A. Kraft
Keyword(s):  
Perfusion Imaging ◽  
Arterial Spin Labeling ◽  
Image Acquisition ◽  
Spin Labeling ◽  
Acquisition Technique

IMAGE ACQUISITION FOR COMPUTER ASSISTED INSTRUCTION

1992 ◽  
Vol 27 (12) ◽  
pp. 1110
Author(s):  
J. C. Honeyman ◽  
C. K. Palmer ◽  
M. M. Frost ◽  
E. V. Staab
Keyword(s):  
Image Acquisition ◽  
Computer Assisted Instruction ◽  
Computer Assisted ◽  
Assisted Instruction

Application of Content-Based Image Retrieval in Medical Image Acquisition

2020 ◽  
pp. 220-240
Author(s):  
Vinayak Majhi ◽  
Sudip Paul
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Medical Diagnosis ◽  
Medical Image ◽  
Image Acquisition ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Extraction Algorithm ◽  
Day By Day ◽  
Acquisition Technique

Content-based image retrieval is a promising technique to access visual data. With the huge development of computer storage, networking, and the transmission technology now it becomes possible to retrieve the image data beside the text. In the traditional way, we find the content of image by the tagged image with some indexed text. With the development of machine learning technique in the domain of artificial intelligence, the feature extraction techniques become easier for CBIR. The medical images are continuously increasing day by day where each image holds some specific and unique information about some specific disease. The objectives of using CBIR in medical diagnosis are to provide correct and effective information to the specialist for the quality and efficient diagnosis of the disease. Medical image content requires different types of CBIR technique for different medical image acquisition techniques such as MRI, CT, PET Scan, USG, MRS, etc. So, in this concern, each CBIR technique has its unique feature extraction algorithm for each acquisition technique.

Image acquisition technique for high dynamic range scenes using a multiband camera

2006 ◽  
Vol 31 (4) ◽  
pp. 294-302 ◽  
Author(s):  
Hideaki Haneishi ◽  
Seiji Miyahara ◽  
Atsushi Yoshida
Keyword(s):  
Dynamic Range ◽  
Image Acquisition ◽  
High Dynamic Range ◽  
High Dynamic ◽  
Acquisition Technique

scholarly journals Safety and Usefulness of Non-Occlusion Image Acquisition Technique for Optical Coherence Tomography

2008 ◽  
Vol 72 (9) ◽  
pp. 1536-1537 ◽  
Author(s):  
Hideaki Kataiwa ◽  
Atsushi Tanaka ◽  
Hironori Kitabata ◽  
Toshio Imanishi ◽  
Takashi Akasaka
Keyword(s):  
Optical Coherence Tomography ◽  
Image Acquisition ◽  
Optical Coherence ◽  
Acquisition Technique

scholarly journals Cross-correlation-based image acquisition technique for manually-scanned optical coherence tomography

2009 ◽  
Vol 17 (10) ◽  
pp. 8125 ◽  
Author(s):  
Adeel Ahmad ◽  
Steven G. Adie ◽  
Eric J. Chaney ◽  
Utkarsh Sharma ◽  
Stephen A. Boppart
Keyword(s):  
Optical Coherence Tomography ◽  
Cross Correlation ◽  
Image Acquisition ◽  
Optical Coherence ◽  
Acquisition Technique
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