A Methodical Study of Content Based Medical Image Retrieval in Current Days

B. Satish
B. Satish * §
Contact
Dr. Supreethi K. P
Dr. Supreethi K. P
Contact
§ Kamala Institute of Technology and Science

Send Message

To: Author

A Methodical Study of Content Based Medical Image Retrieval in Current Days

Article Fingerprint

ReserarchID

CST6NM18

A Methodical Study of Content Based Medical Image Retrieval in Current Days Banner

Key Research Insights

Synthesized scholarly intelligence & interactive research assistant
Access Review Comments
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu
Reading Preferences
Font Size
Line Spacing
Background
This converted HTML version may contain rendering inconsistencies. Please refer to the PDF for the authoritative version, or click here to provide feedback.

Abstract

Content-based image retrieval (CBIR) is standout amongst the most rich research fields in the area of computer vision & significant advancement has been made throughout the decade. CBIR is an image search methodology that changed the traditional text-based retrieval of images by utilizing various visual features, for example, color, texture, & shape, as criteria of search. In the area of medical, images, particularly digital images, are generated in constantly increasing quantities & utilized for diagnostics & therapy. Content based approaches into medical images to support in making clinical decision has been suggested that would simplify the management of clinical data & scenarios to incorporate the content-based approaches. As, the total quantity of data generated in diagnostic centers has increased, it leads to the utilization of CBIR in the daily routine of hospitals & clinics.

References

109 Cites in Article
  1. 1. A Smeulders,M Worring,S Santini,A Gupta,R Jain (2000). Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell, 22(12), 1349-1380.
  2. 2. M Lew,N Sebe,C Djeraba,R Jain (2006). Content-based multimedia information retrieval: State of the art and challenges. ACM Trans Multimed Comput Commun Appl, 2(1), 1-19.
  3. 3. Y Rui,T Huang,S Chang (1999). Image retrieval: Current techniques, promising directions, and open issues. J Vis Commun Image Represent, 10(1), 39-62.
  4. 4. Ritendra Datta,Dhiraj Joshi,Jia Li,James Wang (2008). Image retrieval. ACM Computing Surveys, 40(2), 1-60.
  5. 5. C Kgül,D Rubin,S Napel,C Beaulieu,H Greenspan,B Acar (2011). Content-based image retrieval in radiology: Current status and future directions. J Digit Imaging, 24, 208-222.
  6. 6. M Flickner,H Sawhney,W Niblack,J Ashley,Qian Huang,B Dom,M Gorkani,J Hafner,D Lee,D Petkovic,D Steele,P Yanker (1995). Query by image and video content: the QBIC system. Computer, 28(9), 23-32.
  7. 7. Jeffrey Bach,Charles Fuller,Amarnath Gupta,Arun Hampapur,Bradley Horowitz,Rich Humphrey,Ramesh Jain,Chiao-Fe Shu (1996). <title>Virage image search engine: an open framework for image management</title>. SPIE Proceedings, 2670, 76-87.
  8. 8. A Pentland,R Picard,S Sclaroff (1996). Photobook: Content-based manipulation of image databases. Int J Comput Vis, 18, 233-254.
  9. 9. G Chechik,V Sharma,U Shalit,S Bengio (2010). Large scale online earning of image similarity through ranking. J Mach Learn Res, 11, 1109-1135.
  10. 10. H Müller,N Michoux,D Bandon,A Geissbuhler (2004). A review of content-based image retrieval systems in medical applications-Clinical benefits and future directions. Int J Med Inform, 73(1), 1-23.
  11. 11. J Duncan,N Ayache (2000). Medical image analysis: progress over two decades and the challenges ahead. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 85-106.
  12. 12. C Lebozec,M.-C Jaulent,E Zapletal,P Degoulet (1998). Unified modeling language and design of a casebased retrieval system in medical imaging. proceedings of American society for Medical Informatics (AMIA)
  13. 13. A Bui,R Taira,J Dionision,D Aberle,S El-Saden,H Kangarloo (2002). Evidence-based radiology. Acad. Radiol, 9(6), 662-669.
  14. 14. C Kahn (1994). Artificial intelligence in radiology: decision support systems. RadioGraphics, 14(4), 849-861.
  15. 15. A Horsch,R Thurmayr (2003). How to identify and assess tasks and challenges of medical image processing. Proceedings of the Medical Informatics Europe Conference (MIE 2003)
  16. 16. H Abe,H Macmahon,R Engelmann,Q Li,J Shiraishi,M Katsuragawa,T Aoyama,K Ishida,C Ashizawa,K Metz,Doi (2003). Computer-aided diagnosis in chest radiography: Results of largescale observer tests at the 1996-2001 RSNA scientific assemblies. Radio Graphics, 23(1), 255-265.
  17. 17. B Kaplan,H Lundsgaarde (1996). Toward an evaluation of an integrated clinical imaging system: Identifying clinical benefits. Methods Inform. Med, 35, 221-229.
  18. 18. R Horsch,Thurmayr (2003). How to identify and assess tasks and challenges of medical image processing. Proceedings of the Medical Informatics Europe Conference (MIE 2003)
  19. 19. S Antani,L Long,G Thoma (2002). A biomedical information system for combined content-based retrieval of spine X-ray images and associated text information. Proceedings of the Third Indian Conference on Computer Vision, Graphics and Image Processing
  20. 20. J Smith,S.-F Chang (1996). Visualseek: a fully automated content-based image query system. Proceedings of the Fourth ACM International Multimedia Conference and Exhibition
  21. 21. S Sclaroff,L Taycher,M La Cascia (1997). ImageRover: a content-based image browser for the World Wide Web. 1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries, 2-9.
  22. 22. M Ortega,Y Rui,K Chakrabarti,K Porkaew,S Mehrotra,T Huang (1998). Supporting ranked boolean similarity queries in MARS. IEEE Trans. Knowledge Data Eng, 10(6), 905-925.
  23. 23. J Wang,G Wiederhold,O Firschein,S Wei (1997). Wavelet-based image indexing techniques with partial sketch retrieval capability. Proceedings of the Fourth Forum on Research and Technology Advances in Digital Libraries, 13-24.
  24. 24. H Müller,A Rosset,A Garcia,J Vallée,A Geissbuhler (2005). Benefits of content-based visual data access in radiology. Radiographics, 25(3), 849-858.
  25. 25. Daniel Keysers,J Dahmen,H Ney,B Wein,T Lehmann (2003). Statistical framework for model-based image retrieval in medical applications. Journal of Electronic Imaging, 12(1), 59.
  26. 26. Mark Güld,Christian Thies,Benedikt Fischer,Thomas Lehmann (2007). A generic concept for the implementation of medical image retrieval systems. International Journal of Medical Informatics, 76(2-3), 252-259.
  27. 27. D Iakovidis,N Pelekis,E Kotsifakos,I Kopanakis,H Karanikas,Y Theodoridis (2009). A pattern similarity scheme for medical image retrieval. IEEE Trans Inf Technol Biomed, 13(4), 442-450.
  28. 28. Sameer Antani,D Lee,L Long,George Thoma (2004). Evaluation of shape similarity measurement methods for spine X-ray images. Journal of Visual Communication and Image Representation, 15(3), 285-302.
  29. 29. S Antani,L Long,G Thoma,D Lee (2003). Evaluation of shape indexing methods for content-based retrieval of X-ray images. Proceedings of SPIE, 5021, 405-416.
  30. 30. D Lee,S Antani,L Long (2003). Similarity measurement using polygon curve representation and Fourier descriptors for shape-based vertebral image retrieval. Proceedings of SPIE, 5032, 1283-1291.
  31. 31. X Xu,D Lee,S Antani,L Long (2008). A spine X-ray image retrieval system using partial shape matching. IEEE Trans Inf Technol Biomed, 12(1), 100-108.
  32. 32. W Hsu,S Antani,L Long,L Neve,G Thoma (2009). SPIRS: A web-based image retrieval system for large biomedical databases. Int J Med Inform, 78(Supplement 1), 13-24.
  33. 33. D Lee,S Antani,Y Chang,K Gledhill,L Long,P Christensen (2003). CBIR of spine X-ray images on inter-retrieval of X-ray images. Proceedings of SPIE, 5021, 405-416.
  34. 34. D Lee,S Antani,L Long (2003). Similarity measurement using polygon curve representation and Fourier descriptors for shape-based vertebral image retrieval. Proceedings of SPIE, 5032, 1283-1291.
  35. 35. X Xu,D Lee,S Antani,L Long (2008). A spine X-ray image retrieval system using partial shape matching. IEEE Trans Inf Technol Biomed, 12(1), 100-108.
  36. 36. William Hsu,Sameer Antani,L Long,Leif Neve,George Thoma (2009). SPIRS: A Web-based image retrieval system for large biomedical databases. International Journal of Medical Informatics, 78(Supplement 1), S13-S24.
  37. 37. D Lee,S Antani,Y Chang,K Gledhill,L Long,P Christensen (2009). CBIR of spine X-ray images on intervertebral disc space and shape profiles using feature ranking and voting consensus. Data Knowl Eng, 68(12), 1359-1369.
  38. 38. X Qian,H Tagare,R Fulbright,R Long,S Antani (2010). Optimal embedding for shape indexing in medical image databases. Med Image Anal, 14(3), 243-254.
  39. 39. Zhiyun Xue,L Long,Sameer Antani,Jose Jeronimo,George Thoma (2007). Segmentation of mosaicism in cervicographic images using support vector machines. SPIE Proceedings, 7259, 72594X.
  40. 40. Z Xue,S Antani,L Long,G Thoma (2009). A system for searching uterine cervix images by visual attributes. IEEE International Symposium on Computer-Based Medical Systems, 1-5.
  41. 41. P Korn,N Sidiropoulos,C Faloutsos,E Siegel,Z Protopapas (1998). Fast and effective retrieval of medical tumor shapes. IEEE Trans Knowl Data Eng, 10(6), 889-904.
  42. 42. L Yang,Jin Mummert,L Sukthankar,R Goode,A Zheng,B (2010). A boosting framework for visualitypreserving distance metric learning and its application to medical image retrieval. IEEE Trans Pattern Anal Mach Intell, 32(1), 30-44.
  43. 43. J Dy,C Brodley,A Kak,L Broderick,A Aisen (2003). Unsupervised feature selection applied to content-based retrieval of lung images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(3), 373-378.
  44. 44. W Cai,D Feng,R Fulton (2000). Content-based retrieval of dynamic pet functional images. IEEE Trans Inf Technol Biomed, 4(2), 152-158.
  45. 45. J Kim,W Cai,D Feng,H Wu (2006). A new way for multidimensional medical data management: Volume of interest (VOI)-based retrieval of medical images with visual and functional features. IEEE Trans Inf Technol Biomed, 10(3), 598-607.
  46. 46. J Kim,L Constantinescu,W Cai,D Feng (2007). Contentbased dual-modality biomedical data retrieval using co-aligned functional and anatomical features. Proceedings of the MICCAI Workshop on Content-Based Image Retrieval for Biomedical Image Archives: Achievements, Problems and Prospects, 45-52.
  47. 47. Y Song,W Cai,S Eberl,M Fulham,D Feng (2010). A content-based image retrieval framework for multimodality lung images. IEEE International Symposium on Computer-Based Medical Sys-tems, 285-290.
  48. 48. S Radhouani,J Lim,J Chevallet,G Falquet (2006). Combining textual and visual ontologies to solve medical multimodal queries. IEEE International Conference on Multimedia and Expo, 1853-1856.
  49. 49. C Lacoste,J Lim,J Chevallet,D Le (2007). Medicalimage retrieval based on knowledge-assisted text and image indexing. IEEE Trans Circ Syst Video Technol, 17(7), 889-900.
  50. 50. J Gobeill,H Müller,P Ruch (2006). Translation by text categorization: Medical image retrieval in ImageCLEFmed.
  51. 51. J Villena-Román,Lana Serrano,S González-Cristóbal,J (2007). Merging textual and visual strategies to improve medical image retrieval.
  52. 52. J Caicedo,J Moreno,E Niño,F González (2010). Combining visual features and text data for medical image retrieval using latent semantic kernels. Proceedings of the International Conference on Lecture Notes in Computer Science, 110-119.
  53. 53. Henning Müller,Jayashree Kalpathy-Cramer,Charles Kahn, Jr.,William Hersh (2009). Comparing the quality of accessing medical literature using content-based visual and textual information retrieval. SPIE Proceedings, 7264, 726405.
  54. 54. W Chu,I Ieong,R Taira (1994). A semantic modeling approach for Image retrieval by content. VLDB J-Int J Very Large Data Bases, 3(4), 445-477.
  55. 55. W Chu,Chih-Cheng Hsu,A Cardenas,R Taira (1998). Knowledge-based image retrieval with spatial and temporal constructs. IEEE Transactions on Knowledge and Data Engineering, 10(6), 872-888.
  56. 56. A Névéol,T Deserno,S Darmoni,M Güld,A Aronson (2009). Natural language processing versus content-based image analysis for medical document retrieval. J Am Soc Inf Sci Technol, 60(1), 123-134.
  57. 57. C Langlotz (2006). RadLex: A new method for indexing online educational materials. Radiographics, 26(6), 1595-1597.
  58. 58. H Müller,T Deselaers,T Deserno,J Kalpathy-Cramer,Kim,Hersh (2008). Overview of the ImageCLEFmed 2007 medical retrieval and medical annotation tasks.
  59. 59. H Müller,J Kalpathy-Cramer,C Kahn,W Hatt,S Bedrick,Hersh (2009). Overview of the ImageCLEFmed 2008 medical image retrieval task.
  60. 60. H Müller,J Kalpathy-Cramer,I Eggel,S Bedrick,S Radhouani,B Bakke (2010). Overview of the CLEF 2009 medical image retrieval track.
  61. 61. Md. Rahman,Sameer Antani,Rodney Long,Dina Demner-Fushman,George Thoma (2010). Multi-modal Query Expansion Based on Local Analysis for Medical Image Retrieval. Lecture Notes in Computer Science, 110-119.
  62. 62. J Liu,Y Hu,M Li,S Ma,Ying Ma,W (2007). Medical image annotation and retrieval using visual features.
  63. 63. B Müller,H,Syeda Mahmood,T Duncan,J Wang,F,Kalpathy-Cramer J Eds Medical Content-Based Retrieval for Clinical Decision Support. Medical Content-Based Retrieval for Clinical Decision Support, 5853
  64. 64. Md. Rahman,Bipin Desai,Prabir Bhattacharya (2008). Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion. Computerized Medical Imaging and Graphics, 32(2), 95-108.
  65. 65. H Akakin,M Gurcan (2012). Content-Based Microscopic Image Retrieval System for Multi-Image Queries. IEEE Transactions on Information Technology in Biomedicine, 16(4), 758-769.
  66. 66. G Allampalli-Nagaraj,I Bichindaritz (2009). Automatic semantic indexing of medical images using a web ontology language for case-based image retrieval. Eng Appl Artif Intell, 22(1), 18-25.
  67. 67. X Zhou,R Stern,H Müller (2012). Case-based fracture image retrieval. Int J Comput Assist Radiol Surg, 7, 401-411.
  68. 68. S Huang,M Phelps,E Hoffman,K Sideris,C Selin,D Kuhl (1980). Noninvasive determination of local cerebral metabolic rate of glucose in man. Am J Physiol-Endocrinol Metab, 238(1), 69-82.
  69. 69. E Chang,K Goh,G Sychay,G Wu (2003). CBSA: Contentbased soft annotation for multimodal image retrieval using Bayes point ma-chines. IEEE Trans Circ Syst Video Technol, 13(1), 26-38.
  70. 70. William Hersh,Henning Müller,Jayashree Kalpathy-Cramer (2009). The ImageCLEFmed Medical Image Retrieval Task Test Collection. Journal of Digital Imaging, 22(6), 648-655.
  71. 71. G Tian,H Fu,D Feng (2008). Automatic medical image categorization and annotation using LBP and MPEG-7 edge histograms. International Conference on Information Technology and Applications in Biomedicine, 51-53.
  72. 72. V Spitzer,M Ackerman,A Scherzinger,D Whitlock (1996). The visible human male: A technical report. J Am Med Inform Assoc, 3(2), 118-130.
  73. 73. D Lowe (2004). Distinctive image features from scaleinvariant keypoints. Int J Comput Vis, 60, 91-110.
  74. 74. J Czernin,M Dahlbom,O Ratib,C Schiepers (2004). Atlas of PET/CT Imaging in Oncology. Atlas of PET/CT Imaging in Oncology
  75. 75. G Goerres,Von Schulthess,G Steinert,H (2004). Why most PET of lung and head-and-neck cancer will be PET/CT. J Nucl Med, 45(Supplement 1), 66S-71S.
  76. 76. K Fu (1986). A Step Towards Unification of Syntactic and Statistical Pattern Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(3), 398-404.
  77. 77. A Kumar,J Kim,L Wen,D Feng (2011). A graph-based approach to the retrieval of volumetric PET-CT lung images. Proceedings of the 34th Annual International Conference of the IEEE Engineer-ing in Medicine and Biology Society, 6, 69.
  78. 78. Ashnil Kumar,Jinman Kim,Lei Bi,Michael Fulham,Dagan Feng (2012). Designing user interfaces to enhance human interpretation of medical content-based image retrieval: application to PET-CT images. International Journal of Computer Assisted Radiology and Surgery, 8(6), 1003-1014.
  79. 79. R Pompl,W Bunk,A Horsch,W Stolz,W Abmayr,W Brauer,A Glässl,G Morfill (2000). MELDOQ: Ein System zur Un-terstützung der Früherkennung des malignen Melanoms durch digitale Bildverarbeitung. Proceedings of the Workshop Bildverarbeitung für die Medizin
  80. 80. F Meyer (1986). Automatic screening of cytological specimens. Comput. Vis. Graphics Image Proces, 35, 356-369.
  81. 81. M Mattie,L Staib,E Stratmann,H Tagare,J Duncan,P Miller (2000). PathMaster: Content-based Cell Image Retrieval Using Automated Feature Extraction. Journal of the American Medical Informatics Association, 7(4), 404-415.
  82. 82. Tao Peng,Yidong Gu,Jing Wang (2021). Lung contour detection in Chest X-ray images using Mask Region-based Convolutional Neural Network and Adaptive Closed Polyline Searching Method. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2839-2842.
  83. 83. K Veropoulos,C Campbell,G Learnmonth (1998). Image processing and neural computing used in the diagnosis of tuberculosis. Proceedings of the Colloquium on Intelligent Methods in Healthcare and Medical Applications (IMHMA)
  84. 84. M.-C Jaulent,C Bozec,Y Cao,E Zapletal,P De-Goulet (2000). A property concept frame representation for exibleimage content retrieval in histopathology databases. Proceedings of the Annual Symposium of the Ameri-can Society for Medical Informatics (AMIA)
  85. 85. L Tang,R Hanka,R Lan,H Ip (1998). Automatic se-mantic labelling of medical images for contentbased retrieval. Proceedings of the International Conference on Artificial Intelligence, Expert Systems and Applications (EXPERSYS 1998), 77-82.
  86. 86. L Tang,R Hanka,H Ip,K Cheung,R Lam (2000). Integration of intelligent engines for a large scale medical image database. Proceedings 13th IEEE Symposium on Computer-Based Medical Systems. CBMS 2000, 235-240.
  87. 87. A Kumar,J Kim,M Fulham,D Feng (2012). Graph-based retrieval of multi-modality medical images: A comparison of representations using simulated images. IEEE International Symposium on --6
  88. 88. H Tang,Lilian,R Hanka,H Ip,K Cheung,R Lam (2000). Semantic query processing and annotation generation for content-based retrieval of histological images. International Symposium on Medical Imaging, 3976
  89. 89. A Sbober,C Eccher,E Blanzieri,P Bauer,M Cristifolini,G Zumiani,S Forti (2003). A multiple classifier system for early melanoma diagnosis. Artificial Intel. Med, 27, 29-44.
  90. 90. A Kumar,D Haraguchi,J Kim,L Wen,S Eberl,M Fulham (1996). Medical image collection indexing: shapebased retrieval using KD-trees. Comput. Vis. Graphics Image Proces, 20(4), 209-217.
  91. 91. A Constantinidis,M Fairhurst,A Rahman (2001). A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms. Pattern Recognition, 34(8), 1527-1537.
  92. 92. P Korn,N Sidiropoulos,C Faloutsos,E Siegel,Z Protopapas (1998). Fast and effective retrieval of medical tumor shapes. IEEE Transactions on Knowledge and Data Engineering, 10(6), 889-904.
  93. 93. S Baeg,N Kehtarnavaz (2002). Classification of breast mass abnormalities using denseness and architectural distorsion. Electronic Lett. Comput. Vis. Image Anal, 1(1), 102.
  94. 94. F Schnorrenberg,C Pattichis,C Schizas,K Kyr-Iacou (2000). Content-based retrieval of breast cancer biopsy slides. Technol. Health Care, 8, 103.
  95. 95. C Brodley,A Kak,C Shyu,J Dy,L Broderick,A Aisen (1999). Content-based retrieval from medical im-age databases: A synergy of human interaction, machine learning and computer vision. Proceedings of the 10th National Conference on Artificial Intelligence, 104.
  96. 96. C.-R Shyu,A Kak,C Brodley,L Broderick (1999). Testing for human perceptual categories in a physician-in-the-loop CBIR system for medical imagery. Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'99), 105.
  97. 97. C.-T Liu,P.-L Tai,A,-J Chen,C.-H Peng,J.-S Wang (2000). A content basedmedical teaching file assistant for CT lung image retrieval. Proceedings of the IEEE International Conference on Electronics, Circuits, Systems (ICECS 2000), 106.
  98. 98. C.-T Liu,P.-L Tai,A,-J Chen,C.-H Peng,T Lee,J.-S Wang (2001). A content-based CT lung retrieval system for assisting differential diagnosis images collection. Proceedings of the second International Conference on Multimedia and Exposition (ICME'2001), 107.
  99. 99. C Schaefer-Prokop,M Prokop,D Fleischmann,C Herold,C Han,H Chen,L He,W Wee (2003). High-resolution CT of diffuse interstitial lung 109. Proceedings of the SPIE Photonics West Conference on Internet Imaging IV, 5018, 110.
  100. 100. S Orphanoudakis,C Chronaki,D Vamvaka (1996). I2Cnet: content-based similarity search in geographically dis-tributed repositories of medical images. Comput. Med. Imag. Graphics, 20(4), 111.
  101. 101. S Sclaroff,A Pentland (1994). On modal modeling for medical images: underconstrained shape description and data compression. Proceedings of IEEE Workshop on Biomedical Image Analysis, 70-79.
  102. 102. Y Liu,F Dellaert,W Rothfus,A Moore,J Schneider,T Kanade (1997). Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures. Lecture Notes in Computer Science, 655-665.
  103. 103. W Cai,D Feng,R Fulton (2000). Content-based retrieval of dynamic PET functional images. IEEE Trans. Information Technol. Biomed, 4(2), 114.
  104. 104. S.-K Chang (1996). Active index for content-based medical image retrieval. Computerized Medical Imaging and Graphics, 20(4), 219-229.
  105. 105. L Long,G Thoma,L Berman (1997). A prototype client/server application for biomedical text/image re-trieval on the internet. Proceedings of the Conference on Storage and Retrieval for Image and Video Databases VI, 3312, 116.
  106. 106. Y Jing,H Rowley,C Rosenberg,J Wang,M Zhao,M Covell (2010). Google image swirl, a large-scale contentbased image browsing system. IEEE International Conference on Multimedia and Expo, 267-117.
  107. 107. M Tory,T Moller (2004). Human factors in visualization research. IEEE Trans Vis Comput Graph, 10(1), 118.
  108. 108. J Etzold,A Brousseau,P Grimm,T Steiner (2012). Contextaware que-rying for multimodal search engines.
  109. 109. Ahmet Ekin,Radu Jasinschi,Jeroen Van Der Grond,Mark Van Buchem (2007). Improving information quality of MR brain images by fully automatic and robust image analysis methods. Journal of the Society for Information Display, 15(6), 367-376.
Show All References
Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

B. Satish, Dr. Supreethi K. P. 2016. "A Methodical Study of Content Based Medical Image Retrieval in Current Days". Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 16 (GJCST Volume 16 Issue F2).

More Citation Formats

Download Citation

  • Open Access
  • CC-BY-ND-NC 4.0
Download Article
Access OJS
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords
Not Found
Classification
GJCST-F Classification H.3.3
J.3
  • Submission ReceivedDecember 8, 2015
  • Peer Review Double Blind
    Handling Editor
  • Accepted December 31, 2015
  • Published January 15, 2016
Version of record

v1.2

Issue date
September 20, 2016

Language
English
Order Article Reprint
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR
Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D
Article Matrices
Total Views: 3K
Total Downloads: 160
All Trends

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

A Methodical Study of Content Based Medical Image Retrieval in Current Days

B. Satish
B. Satish Kamala Institute of Technology and Science
Dr. P
Dr. P
  • Submission ReceivedDecember 8, 2015
  • Peer Review Double Blind
    Handling Editor
  • Accepted December 31, 2015
  • Published January 15, 2016