Abstract
Medical imaging performs a vital role in the medical field as it provides important information on the internal body parts for the clinical analysis and medical intervention which enables physicians to diagnose and treat a variety of diseases. Nowadays the medical diagnosis is increasing at a very high rate, which results in the formation of a huge medical image database, and retrieving similar medical images from such a huge database is a very difficult task. A literature review of various methods for biomedical image indexing and retrieval is presented here. Over 140 contributions are included from the literature in this survey. And it is mainly concentrated on the methodology based on the visual representation of the medical images as content-based medical image retrieval (CBMIR) approaches retrieve similar medical images more efficiently as compared to text-based biomedical image retrieval approaches. It also delineates how various ideas were adopted from different computer science methodologies for developing CBMIR systems.
Similar content being viewed by others
References
Hendee WR, Ritenour ER (2003) Medical imaging physics. Wiley, New York
Bradley WG (2008) History of medical imaging. Proc Am Philos Soc 152(3):349–361
Huang HK, Dwyer III SJ, Angus WM, Capp MP, Arenson RL, Kangarloo H (1987) Picture archiving and communications systems (PACS). In: Radiological Society of North America 73rd scientific assembly and annual meeting (Abstracts)
Fisher HD, McNeil KM, Vercillo R, Lamoreaux RD (1989) U.S. Patent No. 4,833,625. U.S. Patent and Trademark Office, Washington, DC
Archiving P (1991) Communication system. Fijifilm Medical Systems, USA
Innovative Flemish In vivo Imaging Technology. A history of medical imaging. Ghent University. http://www.infinityugent.be/research-development/a-history-of-medical-imaging
Dayhoff RE, Maloney DL, Kuzmak PM, Shepard BM (1991) Integrating medical images into hospital information systems. J Digit Imaging 4(2):87–93
Huang HK (1991) Picture archiving and communications systems. Comput Med Imaging Graph 15:743–749
Kim Y, Park HW, Haynor DR (1991) Requirements for PACS workstations. In: The second international conference on image management and communication (IMAC) in patient care: new technologies for better patient care, 1991. IEEE, pp 36–41
Smutek JM, Wenig RI, Webb NJ, Waisman A (1985) U.S. Patent No. 4,553,206. U.S. Patent and Trademark Office, Washington, DC
Youssif AA, Darwish AA, Mohamed RA (2010) Content based medical image retrieval based on pyramid structure wavelet. Int J Comput Sci Netw Secur 10(3):157–164
Chang SK, Hou TY, Hsu A (1992) Smart image design for large image databases. J Vis Lang Comput 3(4):323–342
Grosky WI (1984) Toward a data model for integrated pictorial databases. Comput Vis Graph Image Process 25(3):371–382
Iyengar SS, Kashyap RL (1988) Guest editor’s introduction: image databases. IEEE Trans Softw Eng 14(5):608
Kelly PM, Cannon TM (1994) Candid: comparison algorithm for navigating digital image databases. In: Seventh international working conference on scientific and statistical database management, 1994. Proceedings. IEEE, pp 252–258
Orphanoudakis SC, Chronaki C, Kostomanolakis S (1994) I2C: a system for the indexing, storage, and retrieval of medical images by content. Med Inform 19(2):109–122
Mizotin M, Benois-Pineau J, Allard M, Catheline G (2012) Feature-based brain MRI retrieval for Alzheimer disease diagnosis. In: 2012 19th IEEE international conference on image processing (ICIP). IEEE, pp 1241–1244
Hwang KH, Lee H, Choi D (2012) Medical image retrieval: past and present. Healthcare Inform Res 18(1):3–9
Shyu CR, Brodley CE, Kak AC, Kosaka A, Aisen AM, Broderick LS (1999) ASSERT: a physician-in-the-loop content-based retrieval system for HRCT image databases. Comput Vis Image Underst 75(1–2):111–132
Keysers D, Ney H, Wein BB, Lehmann TM (2003) Statistical framework for model-based image retrieval in medical applications. J Electron Imaging 12(1):59–68
Lam MO, Disney T, Raicu DS, Furst J, Channin DS (2007) BRISC—an open source pulmonary nodule image retrieval framework. J Digit Imaging 20(1):63–71
Deselaers T, Keysers D, Ney H (2004) FIRE-flexible image retrieval engine: ImageCLEF 2004 evaluation. In CLEF, pp 688–698
Müller H, Michoux N, Bandon D, Geissbuhler A (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
Ghosh P, Antani S, Long LR, Thoma GR (2011) Review of medical image retrieval systems and future directions. In: 2011 24th international symposium on computer-based medical systems (CBMS). IEEE, pp 1–6
Zhou XS, Huang TS (2003) Relevance feedback in image retrieval: a comprehensive review. Multimed Syst 8(6):536–544
Akgül CB, Rubin DL, Napel S, Beaulieu CF, Greenspan H, Acar B (2011) Content-based image retrieval in radiology: current status and future directions. J Digit Imaging 24(2):208–222
Kumar A, Kim J, Cai W, Fulham M, Feng D (2013) Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data. J Digit Imaging 26(6):1025–1039
Rehman M, Iqbal M, Sharif M, Raza M (2012) Content based image retrieval: survey. World Appl Sci J 19(3):404–12
James AP, Dasarathy BV (2014) Medical image fusion: a survey of the state of the art. Inf Fus 19:4–19
Deep G, Kaur L, Gupta S (2016) Biomedical image indexing and retrieval descriptors: a comparative study. Procedia Comput Sci 85:954–961
Wanjale K, Borawake T, Chaudhari S (2010) Content based image retrieval for medical images techniques and storage methods—review paper. IJCA J 1(19):105–107
Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29(1):51–59
Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650
Rao LK, Rao DV (2015) Local quantized extrema patterns for content-based natural and texture image retrieval. Hum Centric Comput Inf Sci 5(1):26
ul Hussain S, Triggs B (2012) Visual recognition using local quantized patterns. In: Computer vision—ECCV 2012. Springer, Berlin, pp 716–729
Murala S, Maheshwari RP, Balasubramanian R (2012) Directional local extrema patterns: a new descriptor for content based image retrieval. Int J Multimed Inf Retr 1(3):191–203
Rao LK, Rao DV, Reddy LP (2016) Local mesh quantized extrema patterns for image retrieval. SpringerPlus 5(1):1–15
Deep G, Kaur L, Gupta S (2016) Directional local ternary quantized extrema pattern: a new descriptor for biomedical image indexing and retrieval. Eng Sci Technol Int J 19(4):1895–1909
Zhang L, Zhou Z, Li H (2012) Binary Gabor pattern: an efficient and robust descriptor for texture classification. In: 2012 19th IEEE international conference on image processing (ICIP). IEEE, pp 81–84
Chen J, Shan S, He C, Zhao G, Pietikainen M, Chen X, Gao W (2010) WLD: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720
Swanson MD, Tewfik AH (1996) A binary wavelet decomposition of binary images. IEEE Trans Image Process 5(12):1637–1650
Kamstra L (2003) The design of linear binary wavelet transforms and their application to binary image compression. In: 2003. ICIP 2003. Proceedings. 2003 International conference on image processing, vol 3. IEEE, pp III–241
Pan H, Jin LZ, Yuan XH, Xia SY, Xia LZ (2010) Context-based embedded image compression using binary wavelet transform. Image Vis Comput 28(6):991–1002
Murala S, Maheshwari RP, Balasubramanian R (2012) Directional binary wavelet patterns for biomedical image indexing and retrieval. J Med Syst 36(5):2865–2879
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Murala S, Maheshwari RP, Balasubramanian R (2012) Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans Image Process 21(5):2874–2886
Murala S, Wu QJ (2014) Local mesh patterns versus local binary patterns: biomedical image indexing and retrieval. IEEE J Biomed Health Inform 18(3):929–938
Lumini A, Nanni L, Brahnam S (2016) Multilayer descriptors for medical image classification. Comput Biol Med 72:239–247
Ojansivu V, Heikkilä J (2008) Blur insensitive texture classification using local phase quantization. In: International conference on image and signal processing. Springer, Berlin, pp 236–243
Murala S, Wu QJ (2015) Spherical symmetric 3D local ternary patterns for natural, texture and biomedical image indexing and retrieval. Neurocomputing 149:1502–1514
Tizhoosh HR (2015) Barcode annotations for medical image retrieval: a preliminary investigation. In: 2015 IEEE international conference on image processing (ICIP). IEEE, pp 818–822
Tizhoosh HR, Gangeh M, Tadayyon H, Czarnota GJ (2016) Tumour ROI estimation in ultrasound images via radon barcodes in patients with locally advanced breast cancer. In: 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, pp 1185–1189
Tizhoosh HR, Zhu S, Lo H, Chaudhari V, Mehdi T (2016) MinMax radon barcodes for medical image retrieval. In: International symposium on visual computing. Springer International Publishing, pp 617–627
Tizhoosh HR, Mitcheltree C, Zhu S, Dutta S (2016) Barcodes for medical image retrieval using autoencoded radon transform. In: 2016 23rd international conference on pattern recognition (ICPR). IEEE, pp 3150–3155
Nouredanesh M, Tizhoosh HR, Banijamali E, Tung J (2016) Radon-Gabor barcodes for medical image retrieval. In: 2016 23rd international conference on pattern recognition (ICPR). IEEE, pp 1309–1314
Babaie M, Tizhoosh HR, Zhu S, Shiri ME (2017) Retrieving similar x-ray images from big image data using radon barcodes with single projections. arXiv preprint arXiv:1701.00449
Kundu MK, Chowdhury M, Das S (2017) Interactive radiographic image retrieval system. Comput Methods Programs Biomed 139:209–220
Ma L, Liu X, Gao Y, Zhao Y, Zhao X, Zhou C (2017) A new method of content based medical image retrieval and its applications to CT imaging sign retrieval. J Biomed Inform 66:148–158
Nowaková J, Prílepok M, Snášel V (2017) Medical image retrieval using vector quantization and fuzzy S-tree. J Med Syst 41(2):18
Chatzichristofis SA, Boutalis YS (2010) Content based radiology image retrieval using a fuzzy rule based scalable composite descriptor. Multimed Tools Appl 46(2–3):493–519
Zhang G, Ma ZM (2007) Texture feature extraction and description using Gabor wavelet in content-based medical image retrieval. In: ICWAPR’07. International conference on wavelet analysis and pattern recognition, 2007, vol 1. IEEE, pp 169–173
Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. BiolCybern 36(4):93–202
Fukushima K, Miyake S (1982) Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. In: van Hemmen JL (ed) Competition and cooperation in neural nets. Springer, Berlin, pp 267–285
Fukushima K, Miyake S (1982) Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognit 15(6):455–469
Fukushima K, Miyake S, Ito T (1983) Neocognitron: a neural network model for a mechanism of visual pattern recognition. IEEE Trans Syst Man Cybern 5:826–834
Fukushima K (1986) A neural network model for selective attention in visual pattern recognition. Biol Cybern 55(1):5–15
Fukushima K (1987) Neural network model for selective attention in visual pattern recognition and associative recall. Appl Opt 26(23):4985–92
Fukushima K (1988) Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural Netw 1(2):119–130
Fukushima K (1988) A neural network for visual pattern recognition. Computer 21(3):65–75
Lo SC, Lou SL, Lin JS, Freedman MT, Chien MV, Mun SK (1995) Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Trans Med Imaging 14(4):711–718
Ivakhnenko AG, Lapa VG (1965) Cybernetic predicting devices. CCM Information Corporation
Hahnloser RH, Sarpeshkar R, Mahowald MA, Douglas RJ, Seung HS (2000) Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405(6789):947
Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp 315–323
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 249–256
Wan J, Wang D, Hoi SCH, Wu P, Zhu J, Zhang Y, Li J (2014) Deep learning for content-based image retrieval: a comprehensive study. In: Proceedings of the 22nd ACM international conference on multimedia, pp 157–166. ACM
Babenko A, Lempitsky V (2015) Aggregating local deep features for image retrieval. In: Proceedings of the IEEE international conference on computer vision, pp 1269–1277
Lin K, Yang HF, Hsiao JH, Chen CS (2015) Deep learning of binary hash codes for fast image retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 27–35
Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 35(5):1207–1216
van Tulder G, de Bruijne M (2016) Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted Boltzmann machines. IEEE Trans Med Imaging 35(5):1262–1272
Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJ, Išgum I (2016) Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 35(5):1252–1261
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118
Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31
Halicek M, Lu G, Little JV, Wang X, Patel M, Griffith CC, El-Deiry MW, Chen AY, Fei B (2017) Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. J Biomed Opt 22(6):060503–060503
Singh S, Gupta D, Anand RS, Kumar V (2015) Nonsubsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network. Biomed Signal Process Control 18:91–101
Carneiro G, Nascimento J, Freitas A (2010) Robust left ventricle segmentation from ultrasound data using deep neural networks and efficient search methods. In 2010 IEEE international symposium biomedical imaging: from nano to macro, pp 1085–1088
Salehi SSM, Erdogmus D, Gholipour A (2017) Auto-context convolutional neural network (auto-net) for brain extraction in magnetic resonance imaging. IEEE Trans Med Imaging 1–12. doi:10.1109/TMI.2017.2721362
Li X, Zhong A, Lin M, Guo N, Sun M, Sitek A, Ye J, Thrall J, Li Q (2017) Self-paced convolutional neural network for computer aided detection in medical imaging analysis. arXiv preprint arXiv:1707.06145
Todoroki Y, Han XH, Iwamoto Y, Lin L, Hu H, Chen YW (2017) Detection of liver tumor candidates from CT images using deep convolutional neural networks. In: International conference on innovation in medicine and healthcare. Springer, Cham, pp 140–145
Tan LK, Liew YM, Lim E, McLaughlin RA (2017) Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences. Med Image Anal 39:78–86
Lu L, Zheng Y, Carneiro G, Yang L (eds) (2017) Deep learning and convolutional neural networks for medical image computing: precision medicine, high performance and large-scale datasets. Springer, Berlin
Yan Z, Zhan Y, Peng Z, Liao S, Shinagawa Y, Zhang S, Zhou XS (2016) Multi-instance deep learning: discover discriminative local anatomies for bodypart recognition. IEEE Trans Med Imaging 35(5):1332–1343
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298
Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M (2013) Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: International conference on medical image computing and computer-assisted intervention, pp 246–253. Springer, Berlin
Bar Y, Diamant I, Wolf L, Greenspan H (2015) Deep learning with non-medical training used for chest pathology identification. In: Proceedings SPIE, vol 9414, p 94140V
Roth HR, Farag A, Lu L, Turkbey EB, Summers RM (2015) Deep convolutional networks for pancreas segmentation in CT imaging. arXiv preprint arXiv:1504.03967
Xu Y, Mo T, Feng Q, Zhong P, Lai M, Eric I, Chang C (2014) Deep learning of feature representation with multiple instance learning for medical image analysis. In: 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1626–1630
Shen W, Zhou M, Yang F, Yang C, Tian J (2015) Multi-scale convolutional neural networks for lung nodule classification. In: International conference on information processing in medical imaging. Springer, Cham, pp 588–599
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Sánchez CI (2017) A survey on deep learning in medical image analysis. arXiv preprint arXiv:1702.05747
Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312
Cao Y, Steffey S, He J, Xiao D, Tao C, Chen P, Müller H (2014) Medical image retrieval: a multimodal approach. Cancer Inform 13(Suppl 3):125
Sun Q, Yang Y, Sun J, Yang Z, Zhang J (2017) Using deep learning for content-based medical image retrieval. In: SPIE medical imaging. International Society for Optics and Photonics, pp 1013812–1013812
Qayyum A, Anwar SM, Awais M, Majid M (2017) Medical image retrieval using deep convolutional neural network. Neurocomputing 266:8–20
Rahman MM, Antani SK, Thoma GR (2011) A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback. IEEE Trans Inf Technol Biomed 15(4):640–646
Rahman MM, Antani SK, Thoma GR (2009) A medical image retrieval framework in correlation enhanced visual concept feature space. In: 22nd IEEE international symposium on computer-based medical systems, 2009. CBMS 2009. IEEE, pp 1–4
Rahman MM, Bhattacharya P, Desai BC (2007) A framework for medical image retrieval using machine learning and statistical similarity matching techniques with relevance feedback. IEEE Trans Inf Technol Biomed 11(1):58–69
Rahman MM, Desai BC, Bhattacharya P (2008) Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion. Comput Med Imaging Graph 32(2):95–108
Mohanapriya S, Vadivel M (2013) Automatic retrival of MRI brain image using multiqueries system. In: 2013 International conference on information communication and embedded systems (ICICES). IEEE, pp 1099–1103
Ramamurthy B, Chandran KR (2011) Content based image retrieval for medical images using canny edge detection algorithm. Int J Comput Appl 17(6):32–37
Nazari MR, Fatemizadeh E (2010) A CBIR system for human brain magnetic resonance image indexing. Int J Comput Appl 7(14):33–37
Amaral IF, Coelho F, da Costa JFP, Cardoso JS (2010) Hierarchical medical image annotation using SVM-based approaches. In: 2010 10th IEEE international conference on information technology and applications in biomedicine (ITAB). IEEE, pp 1–5
U.S. National Library of Medicine. http://www.nlm.nih.gov/
Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Rapp BA, Wheeler DL (2002) GenBank. Nucleic Acids Res 30(1):17
Bodenreider O (2004) The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res 32(suppl_1):D267–D270
Lacoste C, Chevallet JP, Lim JH, Wei X, Racoceanu D, Le DTH, Vuillenemot N (2006) IPAL knowledge-based medical image retrieval in ImageCLEFmed 2006. In: CLEF (Working Notes)
Lacoste C, Chevallet JP, Lim JH, Le DTH, Xiong W, Racoceanu D, Vuillenemot N (2006) Inter-media concept-based medical image indexing and retrieval with umls at IPAL. In: Workshop of the cross-language evaluation forum for European languages. Springer, Berlin, pp 694–701
Lim JH, Chevallet JP (2005) Vismed: a visual vocabulary approach for medical image indexing and retrieval. Inf Retr Technol. Part of Lecture Notes in Computer Science book series LNCS, vol 3689, pp 84–96
Lacoste C, Lim JH, Chevallet JP, Le DTH (2007) Medical-image retrieval based on knowledge-assisted text and image indexing. IEEE Trans Circuits Syst Video Technol 17(7):889–900
Lim JH, Chevallet JP, Le DTH, Goh H (2008) Bi-modal conceptual indexing for medical image retrieval. In: International conference on multimedia modeling. Springer Berlin, pp 456–465
Greenspan H, Pinhas AT (2007) Medical image categorization and retrieval for PACS using the GMM-KL framework. IEEE Trans Inf Technol Biomed 11(2):190–202
Ramamurthy B, Chandran KR, Meenakshi VR, Shilpa V (2012) CBMIR: content based medical image retrieval system using texture and intensity for dental images. In: Mathew J, Patra P, Pradhan DK, Kuttyamma AJ (eds) Eco-friendly computing and communication systems. Springer, Berlin, pp 125–134
Oberoi A, Singh M (2012) Content based image retrieval system for medical databases (CBIR-MD)-lucratively tested on endoscopy, dental and skull images. IJCSI Int J Comput Sci Issues 9(3):1694–1814
Krishna AN, Prasad BG (2012) Automated image annotation for semantic indexing and retrieval of medical images. Int J Comput Appl 55(3):26–33
Quellec G, Lamard M, Cazuguel G, Cochener B, Roux C (2010) Wavelet optimization for content-based image retrieval in medical databases. Med Image Anal 14(2):227–241
Mueen A, Zainuddin R, Baba MS (2008) Automatic multilevel medical image annotation and retrieval. J Digit Imaging 21(3):290–295
Robinson GP, Tagare HD, Duncan JS, Jaffe CC (1996) Medical image collection indexing: shape-based retrieval using KD-trees. Comput Med Imaging Graph 20(4):209–217
Friedman JH, Bentley JL, Finkel RA (1977) An algorithm for finding best matches in logarithmic expected time. ACM Trans Math Softw (TOMS) 3(3):209–226
Murphy OJ, Selkow SM (1986) The efficiency of using KD trees for finding nearest neighbors in discrete space. Inf Process Lett 23(4):215–218
Tsishkou DV, Bovbel EI, Liventseva MM (2003) Medical images indexing and retrieval. In: Proceedings. Seventh international symposium on signal processing and its applications, 2003, vol 1. IEEE, pp 185–187
Shen H, Tao D, Ma D (2013) Multiview locally linear embedding for effective medical image retrieval. PLoS ONE 8(12):e82409
Lan R, Zhou Y (2016) Medical image retrieval via histogram of compressed scattering coefficients. IEEE J Biomed Health Inform 21(5):1338–1346
Markonis D, Schaer R, Müller H (2016) Evaluating multimodal relevance feedback techniques for medical image retrieval. Inf Retr J 19(1–2):100–112
Zare MR, Müller H (2016) A medical X-ray image classification and retrieval system. In: PACIS, p 13
Tagare HD, Jaffe CC, Duncan J (1997) Medical image databases: a content-based retrieval approach. J Am Med Inform Assoc 4(3):184–198
Glatard T, Montagnat J, Magnin IE (2004) Texture based medical image indexing and retrieval: application to cardiac imaging. In: Proceedings of the 6th ACM SIGMM international workshop on multimedia information retrieval. ACM, pp 135–142
Lehmann TM, Wein BB, Dahmen J, Bredno J, Vogelsang F, Kohnen M (1999) Content-based image retrieval in medical applications: a novel multistep approach. In: Yeung MM, Yeo BL, Bouman CA (eds) Electronic imaging. International Society for Optics and Photonics, San Jose, CA, USA pp 312–320
Güld MO, Thies C, Fischer B, Lehmann TM (2007) A generic concept for the implementation of medical image retrieval systems. Int J Med Inform 76(2):252–259
Kalpathy-Cramer J, Hersh W (2007) Automatic image modality based classification and annotation to improve medical image retrieval. In: Medinfo 2007: proceedings of the 12th world congress on health (medical) informatics; building sustainable health systems. IOS Press, p 1334
Korn F, Sidiropoulos N, Faloutsos C, Siegel E, Protopapas Z (1998) Fast nearest neighbor search in medical image databases. https://drum.lib.umd.edu/handle/1903/805
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Das, P., Neelima, A. An overview of approaches for content-based medical image retrieval. Int J Multimed Info Retr 6, 271–280 (2017). https://doi.org/10.1007/s13735-017-0135-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13735-017-0135-x