Based on the top-hat algorithm of oral medical image information recognition and data processing

2012 ◽  
Author(s):  
Wang Yingli ◽  
Zhao Xueli ◽  
Wang Ming
Keyword(s):  
Data Processing ◽  
Medical Image ◽  
Image Information ◽  
Information Recognition

New medical image information processing and management technology for bone

2005 ◽  
Vol 48 (7) ◽  
pp. 403-413
Author(s):  
Isamu KASHIMA ◽  
Satsuki KUMASAKA ◽  
Ryota KAWAMATA ◽  
Yusuke KOZAI
Keyword(s):  
Information Processing ◽  
Medical Image ◽  
Image Information ◽  
Management Technology

The Image Information Recognition of Large Structures Based on CAD Model Characteristics

2014 ◽  
Vol 511-512 ◽  
pp. 506-509
Author(s):  
Yu Kun Zhang ◽  
Qing Zhang ◽  
Lei Zhao
Keyword(s):  
Optical Measurement ◽  
Geometric Model ◽  
Cad Model ◽  
Optical Instrument ◽  
Large Structure ◽  
Image Information ◽  
Large Structures ◽  
Information Recognition

Because of the interference of the noise signals, the actual feature of objects cant be reflected clearly by photo which is measured by optical measurement. Thus, the database of feature of geometric model is established at the platform of Pro/E. Then the information selected will be extracted and exported, and compared to the photo which is gotten from the optical instrument. This can identify the information to prove the correctness when detecting objects photographed, which is used to adjust to the large structure.

scholarly journals Application of Fuzzy Recognition in Image Information Recognition

2021 ◽  
Vol 1952 (2) ◽  
pp. 022039
Author(s):  
Junlian Huang ◽  
Dongyan Zhao ◽  
Boxue Lv
Keyword(s):  
Image Information ◽  
Information Recognition ◽  
Fuzzy Recognition

scholarly journals Suggested Protocol for Efficient Medical Image Information Exchange in Korea: Breast MRI

2018 ◽  
Vol 79 (5) ◽  
pp. 254 ◽  
Author(s):  
Ji Hee Park ◽  
Seon-Hyeong Choi ◽  
Sungjun Kim ◽  
Hwan Seok Yong ◽  
Hyunsik Woo ◽  
...  
Keyword(s):  
Information Exchange ◽  
Medical Image ◽  
Breast Mri ◽  
Image Information

Empirical study of image describing and searching behaviors of medical image users

2012 ◽  
Author(s):  
◽  
Xin Wang
Keyword(s):  
Mental Models ◽  
Medical Image ◽  
Domain Knowledge ◽  
Information Needs ◽  
Search Performance ◽  
Image Information ◽  
Retrieval Systems ◽  
Indexing And Retrieval ◽  
Pyramid Model ◽  
High Level

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Researchers in the field of image indexing and retrieval face a crucial question -- how to design and develop efficient and effective systems that meet real users' requirements by supporting them with advanced indexing and retrieval functions and options for user interaction. According to the Mental Models Theory, designers need to reduce the dissonance between designers' conceptual models and users' mental models through developing systems that are learnable, functional and usable. There are two fundamental issues that deserve more investigations for the design of image Information Retrieval (IR) systems: Firstly, how do users represent non-textual information needs? Secondly, how do users search for images and interact with image IR to obtain non-textual materials? To address these two issues, this dissertation research carried out two studies that focused on the describing and searching behaviors of image users in a specialty area of medicine -- Radiography. The goal of this research was to discover how expert, intermediate, and novice radiological technologists represented their image information needs and searched images with different search tactics. The first study of this dissertation was to build an efficient, robust, and user-centered medical image indexing procedure. To realize this goal, it was essential to index the images at the right level of description and ensure the indexed levels match users' interest level. The first study examined 240 medical image descriptions produced by image users with different levels of domain knowledge (novices, intermediates, and experts) in the area of radiography. There are several important findings in the first study: 1) The effect of domain knowledge has been found to have a significant relationship with the use of semantic image attributes in image users' descriptions. This study found that experts employed more high-level image attributes, which require high-reasoning or diagnostic knowledge to search for a medical image (Abstract Objects and Scenes) than novices; novices were more likely to describe some basic objects that do not require high level of radiological knowledge to search for an image they need (Generic Objects) than were experts; 2) All image users preferred to use image attributes on the semantic levels to represent the image they desired to find, especially using those specific-level and scene-related attributes; 3) Image attributes generated by medical image users can be mapped to all levels of the Pyramid model that was developed to structure visual information. Therefore, the Pyramid model could be considered a robust instrument for indexing medical imagery. The second study of this dissertation focused on the use of search tactics unique to medical image information. The study was designed to address how domain knowledge interacts with search task to influence the use of search tactics and search performance of medical image searchers. The main findings of this study include: 1) Experts used significantly more search tactics (such as using more newly-generated queries, spending longer time in reading instruction (preparation for searching), browsing more screen of search results, carefully examining more enlarged images, and using more frequently limiting devices of a search engine to narrow down search results) than intermediates and novices. 2) Novices used the Re-read Instruction tactic most in order to compensate for their incapability to understand and memorize search topics. Novices also used tactics such as Examining Enlarged Images and Refine tactics least, which suggested that novices were neither unable to interpret/evaluate an image nor lack of search expertise to process their searching tasks. As a result, novices' search performance was significantly lower than both intermediates and experts. 3) Specific Tasks raised the use of a variety of search tactics comparing to General Tasks and Abstract Tasks. This is likely because medical image searchers perceive Specific Tasks as the most difficult tasks among these three types of search tasks. As a result, searchers performed worst in Specific Tasks. The findings of this study provide a series of implications for designing and evaluating the medical image information system. For instance, the results showed that medical image searchers occasionally employ Refine and Manipulations tactics during their search and interaction process. Thus, it is better to employ the faceted search interface in an image information system acting as a query refinement control. In addition, this study found that novices less frequently employ visual stimuli during the search process due to lack of domain knowledge. Image retrieval systems need to provide novices context-sensitive knowledge assistance (e.g., annotated image features). The results of the second study are beneficial for the creation of adaptive and supportive tool sets that are appropriate for different image user groups. In addition to the contributions to the design and development of image IR, this dissertation research also provided significant information to library professionals. This research revealed what intellectual parts of a medical image document the indexer or archivists should consider for representation in the indexing, so library professionals may provide better user-oriented access points to these images. Also, the knowledge about users' image searching behaviors enables medical librarians to design better training activities to help users establish more accurate and complete mental models for various image retrieval systems.

DMI image information recognition based on improved YOLOv4 with focal loss

2021 ◽  
Author(s):  
He Han ◽  
Li Kaicheng ◽  
Lei Yuan ◽  
Wang Fei
Keyword(s):  
Image Information ◽  
Information Recognition
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