Atnaujinkite slapukų nuostatas

El. knyga: Creating New Medical Ontologies for Image Annotation: A Case Study

4.00/5 (2 ratings by Goodreads)
, , ,
Kitos knygos pagal šią temą:
Creating New Medical Ontologies for Image Annotation: A Case StudyDidesnis paveikslėlis
Kitos knygos pagal šią temą:

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

Creating New Medical Ontologies for Image Annotation focuses on the problem of the medical images automatic annotation process, which is solved in an original manner by the authors. All the steps of this process are described in detail with algorithms, experiments and results. The original algorithms proposed by authors are compared with other efficient similar algorithms.

In addition, the authors treat the problem of creating ontologies in an automatic way, starting from Medical Subject Headings (MESH). They have presented some efficient and relevant annotation models and also the basics of the annotation model used by the proposed system: Cross Media Relevance Models. Based on a text query the system will retrieve the images that contain objects described by the keywords.
1 Introduction
1(4)
2 Content-Based Image Retrieval in Medical Images Databases
5(10)
2.1 Introduction
5(2)
2.2 Content-Based Image Retrieval Systems
7(2)
2.3 Content-Based Image Query on Color and Texture Features
9(2)
2.4 Evaluation of the Content-Based Image Retrieval Task
11(1)
2.5 Conclusions
12(3)
References
13(2)
3 Medical Images Segmentation
15(30)
3.1 Introduction
15(1)
3.2 Related Work
16(3)
3.3 Graph-Based Image Segmentation Algorithm
19(13)
3.4 The Color Set Back-Projection Algorithm
32(1)
3.5 The Local Variation Algorithm
33(2)
3.6 Segmentation Error Measures
35(1)
3.7 Experiments and Results
36(4)
3.8 Conclusions
40(5)
References
41(4)
4 Ontologies
45(20)
4.1 Ontologies: A General Overview
45(2)
4.2 Ontology Design and Development Tools
47(4)
4.3 Medical Ontologies
51(3)
4.4 Topic Maps
54(1)
4.5 MeSH Description
55(3)
4.6 Mapping MeSH Content to the Ontology and Graphical Representation
58(7)
References
63(2)
5 Medical Images Annotation
65(26)
5.1 General Overview
65(8)
5.2 Annotation Systems in the Medical Domain
73(2)
5.3 Cross-Media Relevance Model Based on an Object-Oriented Approach
75(10)
5.3.1 Cross-Media Relevance Model Description
75(2)
5.3.2 The Database Model
77(1)
5.3.3 The Annotation Process
78(4)
5.3.4 Measures for the Evaluation of the Annotation Task
82(1)
5.3.5 Experimental Results
83(2)
5.4 Conclusions
85(6)
References
85(6)
6 Semantic-Based Image Retrieval
91(12)
6.1 General Overview
91(6)
6.2 Semantic-Based Image Retrieval Using the Cross-Media Relevance Model
97(2)
6.3 Experimental Results
99(1)
6.4 Conclusions
100(3)
References
101(2)
7 Object Oriented Medical Annotation System
103
7.1 Software System Architecture
103(7)
7.2 Conclusions
110
Daugiau apie elektronines knygas Daugiau apie elektronines knygas
Pastovi nuoroda: https://www.kriso.lt/db/97814614190992e.html
Raktažodžiai: