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A medical image retrieval scheme through a medical social network

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Abstract

Medical social networking sites have enabled multimedia content sharing in large volumes by allowing physicians and patients to upload their medical images. Moreover, it is necessary to employ new techniques to effectively handle and benefit from them. This huge volume of images needs to formulate new types of queries that pose complex questions to medical social network databases. Content-based image retrieval (CBIR) stills an active and efficient research topic to manipulate medical images. To palliate this situation, we propose in this paper the integration of a content-based medical image retrieval method through a medical social network, based on an efficient fusion of low-level visual image features (color, shape and texture features) which offers an efficient and flexible precision. A clear application of our CBIR system consists of providing stored images that are visually similar to a new (undiagnosed) one, allowing specialist and patients to check past exam diagnoses from comments and other physicians’ annotations, and to establish, therefore, a new diagnostic or to prepare a new report of an image’s exam. Experiments show that the proposed medical image retrieval scheme achieves better performance and accuracy in retrieving images. However, we need also to verify whether our approach is considered by the specialists as a potential aid in a real environment. To do so, we evaluate our methodology’s impact in the user’s decision, inquiring the specialists about the degree of confidence in our system. By analyzing the obtained results, we can argue that the proposed methodology presented a high acceptance regarding the specialists’ interests in the clinical practice domain and can improve the decision-making process during analysis.

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Notes

  1. http://iom.nationalacademies.org/.

  2. http://corp.yougov.com/healthcare/consumers-use-preference-expectations-hospital-social-media.

  3. http://www.sobercircle.com.

  4. http://www.sparkpeople.com/.

  5. https://www.fitocracy.com/.

  6. https://www.dacadoo.com/.

  7. http://www.asklepios.com/.

  8. http://www.acc.org/.

  9. http://www.diabspace.com/.

  10. http://www.parlonscancer.ca/.

  11. http://www.renaloo.com/.

  12. http://www.rxspace.com/.

  13. IRMA Homepage (English): http://www.irma-project.org/index_en.php.

  14. http://imagej.nih.gov/ij/.

References

  • Afifi AJ, Ashour WM (2012) Content-based image retrieval using invariant color and texture features. In: International conference on digital image computing techniques and applications (DICTA), IEEE, Fremantle, WA, pp 1–6

  • Agma J, Traina M, André G, Balan R, Bortolotti LM, Traina C Jr (2007) Content-based image retrieval using approximate shape of objects. In: The 17th IEEE symposium on computer-based medical systems (CBMS’07), pp 91–96

  • Aisen AM, Broderick LS, Winer-Muram H, Brodley CE, Kak AC, Pavlopoulou C et al (2003) Automated storage and retrieval of thin section CT images to assist diagnosis: system description and preliminary assessment. Radiology 228(1):265–270

    Article  Google Scholar 

  • Akakin HC, Gurcan MN (2012) Content-based microscopic image retrieval system for multi-image queries. IEEE Trans Inf Technol Biomed 16:758–769

    Article  Google Scholar 

  • Akgül C, Rubin D, Napel S, Beaulieu C, Greenspan H, Acar B (2011) Content-based image retrieval in radiology: current status and future directions. J Digit Imaging 24:208–222

    Article  Google Scholar 

  • Almansoori W, Zarour O, Jarada TN, Karampales P, Rokne J, Alhajj R (2011) Applications of social network construction and analysis in the medical referral process. In: Proceedings of the 2011 IEEE ninth international conference on dependable, autonomic and secure computing (DASC ‘11)

  • AMA Policy (2012) Professionalism in the use of social media. American Medical Association, 2012 Annual Meeting. http://www.ama-assn.org/ama/pub/meeting/professionalism-social-media.shtml

  • Antani S, Long LR, Thoma GR, Lee DJ (2003) Evaluation of shape indexing methods for content-based retrieval of X-ray images. In: Yeung MM, Lienhart RW, Li CS (eds) Proceedings of SPIE, vol 5021, pp 405–416

  • Antani S, Lee D, Long LR, Thoma GR (2004) Evaluation of shape similarity measurement methods for spine X-ray images. J Vis Commun Image Represent 15(3):285–302

    Article  Google Scholar 

  • Ashish O, Manpreet S (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(1):300–306

    Google Scholar 

  • Bach JR, Fuller C, Gupta A, Hampapur A, Horowitz B, Humphrey R et al (1996) Virage image search engine: an open framework for image management. In: Sethi IK, Jain RC (eds) Proceedings of SPIE, vol 2670 (1), pp 76–87

  • Bhattacharjee N, Parekh R (2011) Skin texture analysis for medical diagnosis. In: The international conference on communication, computing and security. New York, pp 301–306

  • Bueno R, Ribeiro MX, Traina AJM (2010) Improving medical image retrieval through multi-descriptor similarity functions and association rules. In: IEEE 23rd international symposium on computer-based medical systems (CBMS), pp 309–314

  • Bugatti PH, Ponciano-Silva M, Agma J, Traina M, Traina C Jr, Marques P (2009) Content-based retrieval of medical images: from context to perception. In: 22nd IEEE international symposium on computer-based medical systems (CBMS), pp 1–8

  • Bugatti PH, Ribeiro MX, Traina AJM, Traina C (2011) Feature selection guided by perception in medical CBIR systems. In: First IEEE international conference on healthcare informatics, imaging and systems biology, pp 323–330

  • Chechik G, Sharma V, Shalit U, Bengio S (2010) Large scale online learning of image similarity through ranking. J Mach Learn Res 11:1109–1135

    MathSciNet  MATH  Google Scholar 

  • Daniel RG, Liza SR, Jennifer LK (2013) Dangers and opportunities for social media in medicine. Clin Obstet Gynecol 56(3). doi:10.1097/GRF.0b013e318297dc38

  • Datta R, Joshi D, Li J, Wang JZ (2008) Image retrieval: Ideas, influences, and trends of the new age. ACM Comput Surv 40(2):5:1–5:60

    Article  Google Scholar 

  • De Oliveira JEE, Machado AMC, Chavez GC, Lopes APB, Deserno TM, De Araujo AA (2010) Mammosys: a content-based image retrieval system using breast density patterns. Comput Methods Programs Biomed 99(3):289–297

    Article  Google Scholar 

  • Doganay S (2014) Healthcare social networks: new choices for doctors, patients. http://www.informationweek.com/healthcare/patient-tools/healthcare-social-networks-new-choices-for-doctors-patients/d/d-id/1234884

  • El-Naqa I, Yang Y, Galatsanos NP, Nishikawa RM, Wernick MN (2004) A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE Trans Med Imaging 23:1233–1244

    Article  Google Scholar 

  • Fazal M, Baharum B (2013) Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain. J King Saud Univ Comput Inform Sci 25:207–218

    Google Scholar 

  • Feldman DL (2012) Medical social media networks: communicating across the virtual highway. Quart J Health Care Pract Risk Manag Infocus 18(1):2–5

    Google Scholar 

  • Flickner M, Sawhney H, Niblack W, Ashley J, Huang Q, Dom B et al (1995) Query by image and video content: the QBIC system. Computer 28(9):23–32

    Article  Google Scholar 

  • Franklin V, Greene S (2007) Sweet talk: a text messaging support system. J Diabetes Nurs 11(1):22–26

    Google Scholar 

  • Goh K-S, Chang EY, Li B (2005) Using one-class and two-class SVMs for multiclass image annotation. IEEE Trans Knowl Data Eng 17(10):1333–1346

    Article  Google Scholar 

  • Gong J, Sun S (2011) Individual doctor recommendation model on medical social network. In: Proceedings of the 7th international conference on advanced data mining and applications (ADMA’11)

  • Greenspan H (2007) Medical image categorization and retrieval for PACS using the GMM-KL framework. IEEE Trans Inform Technol Biomed 11:190–202

    Article  Google Scholar 

  • Grenier C (2003) The role of intermediate subject to understand the structuring of an organizational network of actors and technology—case of a care network. In: Proceedings of the 9th conference of the association information and management, Grenoble

  • Güld MO, Thies C, Fischer B, Lehmann TM (2007) A generic concept for the implementation of medical image retrieval systems. Int Med Inform 76(2–3):252–259

    Article  Google Scholar 

  • Harishchandra H, Mushigeri S, Niranjan UC (2014) Medical image retrieval—performance comparison using texture features. Int J Eng Res Dev 9(9):30–34

    Google Scholar 

  • Hsu W, Antani S, Long LR, Neve L, Thoma GR (2009) SPIRS: a web based image retrieval system for large biomedical databases. Int J Med Inform 78(1):13–24

    Article  Google Scholar 

  • Hu W, Xie N, Li L, Zeng X (2011) A survey on visual content-based video indexing and retrieval. IEEE Trans Syst Man Cybern Part C Appl Rev 41(6):797–819

    Article  Google Scholar 

  • Iakovidis D, Pelekis N, Kotsifakos E, Kopanakis I, Karanikas H, Theodoridis Y (2009) A pattern similarity scheme for medical image retrieval. IEEE Trans Inf Technol Biomed 13(4):442–509

    Article  Google Scholar 

  • ISO/IEC 25010 (2011) Systems and software engineering-systems and software quality requirements and evaluation (SQuaRE)-system and software quality models. International Standards Organization, Geneva

    Google Scholar 

  • John C, Kazunori O (2012) A comparative study of similarity measures for content-based medical image retrieval. http://ceur-ws.org/Vol-1178/CLEF2012wn-ImageCLEF-CollinsEt2012.pdf

  • Keysers D, Dahmen J, Ney H, Wein BB, Lehmann TM (2003) Statistica framework for model-based image retrieval in medical applications. J Electron Imaging 12(1):59–68

    Article  Google Scholar 

  • Komali A et al (2012) 3D color feature extraction in content-based image retrieval. Int J Soft Comput Eng (IJSCE) 2(3):560–563

    Google Scholar 

  • 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. doi:10.1007/s10278-013-9619-2

    Google Scholar 

  • Lee DJ, Antani S, Long LR (2003) Similarity measurement using polygon curve representation and Fourier descriptors for shape-based vertebral image retrieval. In: Sonka M, Fitzpatrick JM (eds) Proceedings of SPIE, vol 5032, pp 1283–1291

  • Lee DJ, Antani S, Chang Y, Gledhill K, Long LR, Christensen P (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

    Article  Google Scholar 

  • Lehmann TM, Guld MO, Deselaers T, Keysers D, Schubert H, Spitzer K, Ney H, Wein BB (2005) Automatic categorization of medical images for content-based retrieval and data mining. Comput Med Imaging Graph 29:143–155

    Article  Google Scholar 

  • Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: state of the art and challenges. ACM Trans Multimed Comput Commun Appl 2(1):1–19

    Article  Google Scholar 

  • Li J (2014) Data protection in healthcare social networks. J IEEE Softw 31(1):46–53

    Article  Google Scholar 

  • Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, New York

    Book  MATH  Google Scholar 

  • Messaoudi A, Bouslimi R, Akaichi J (2013) Indexing medical images based on collaborative experts reports. Int J Comput Appl (0975–887) 70(5):1–9

    Google Scholar 

  • Mezaris V, Kompatsiaris I, Strintzis MG (2005) An ontology approach to object based image retrieval. In: Proceedings of the international conference on image processing, pp 511–514

  • 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

    Article  Google Scholar 

  • Müller H, Rosset A, Garcia A, Vallée JP, Geissbuhler A (2005) Benefits of content-based visual data access in radiology. Radiographics 25(3):849–858

    Article  Google Scholar 

  • Nandagopalan S, Adiga BS, Deepak N (2008) A universal model for content-based image retrieval. World Acad Sci Eng Technol 46:644–647

    Google Scholar 

  • Napel SA, Beaulieu CF, Rodriguez C, Cui J, Xu J, Gupta A et al (2010) Automated retrieval of CT images of liver lesions on the basis of image similarity: method and preliminary results. Radiology 256(1):243–252

    Article  Google Scholar 

  • Pentland A, Picard RW, Sclaroff S (1996) Photobook: content-based manipulation of image databases. Int J Comput Vis 18:233–254

    Article  Google Scholar 

  • Qian X, Tagare HD, Fulbright RK, Long R, Antani S (2010) Optimal embedding for shape indexing in medical image databases. Med Image Anal 14(3):243–254

    Article  Google Scholar 

  • Rajakumar K, Muttan S (2013) MRI image retrieval using wavelet with mahalanobis distance measurement. J Electr Eng Technol 8(5):1188–1193

    Article  Google Scholar 

  • Ramamurthy B, Chandran KR (2012) Content based medical image retrieval with texture content using gray level co-occurrence matrix and k-means clustering algorithms. J Comput Sci 8(7):1070–1076

    Article  Google Scholar 

  • Ramamurthy B, Chandran KR, Meenakshi VR, Shilpa V (2012) CBMIR: content based medical image retrieval system using texture and intensity for dental images. In: International conference eco-friendly computing and communication systems, ICECCS, vol 305, pp 125–134

  • Rui Y, Huang TS, Chang SF (1999) Image retrieval: current techniques, promising directions, and open issues. J Vis Commun Image Represent 10(1):39–62

    Article  Google Scholar 

  • Schmidt-Saugenon P, Guillod J, Thiran JP (2003) Towards a computer-aided diagnosis system for pigmented skin lesions. Comput Med Imag Graph 27:65–78

    Article  Google Scholar 

  • Selvarani G, Annadurai S (2007) Medical image retrieval by combining low level features and DICOM features. In: International IEEE conference on computational intelligence and multimedia applications, pp 587–589

  • Seng WC, Mirisaee SH (2009) A content-based retrieval system for blood cells images. In: International IEEE conference on future computer and communication, pp 412–415

  • 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. Comp Vision Image Underst 75(1–2):111–132

    Article  Google Scholar 

  • Sidong L, Lei J, Weidong C, Lingfeng W, Eberl S, Fulham MJ, Dagan F (2010) Localized multiscale texture based retrieval of neurological image. In: IEEE 23rd international symposium on computer-based medical systems (CBMS), pp 243–248

  • Singh P, Singh S, Kaur G (2009) Efficient techniques used in CBMIR for medical image retrievals. Proc World Acad Sci Eng Technol 38:434–437

    Google Scholar 

  • Singh J, Kaleka JS, Sharma R (2012) Different approaches of CBIR techniques. Int J Comput Distributed Syst 1(2):76–78

    Google Scholar 

  • Smeulders A, Worring M, Santini S, Gupta A, Jain R (2000) Content based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380

    Article  Google Scholar 

  • Song Y (2012) Image analysis for automatic phenotyping measurements. Biomathematics and Statistics Scotland (BioSS), Wageningen

    Google Scholar 

  • Stojmenovic M, Nayak A (2008) Measuring the related properties of linearity and elongation of point sets. In: 13th Iberoamerican congress on pattern recognition, CIARP 2008, Havana, Cuba, pp 102–111

  • Stojmenovic M, Jevremovic A, Nayak A (2013) Fast iris detection via shape based circularity. In: 8th IEEE conference on industrial electronics and applications (ICIEA), pp 747–752

  • Swarnambiga A, Vasuki SM (2013) Distance measures for medical image retrieval. Int J Imag Syst Technol 23:9–21

    Article  Google Scholar 

  • Thies C, Malik A, Keysers D, Kohnen M, Fischer B, Lehmann TM (2003) Hierarchical feature clustering for content-based retrieval in medical image databases. Proc SPIE 5032:598–608

    Article  Google Scholar 

  • Wang M, Li H, Tao D, Lu K, Wu X (2012) Multimodal graph-based reranking for web image search. IEEE Trans Image Process 21(11):4649–4661

    Article  MathSciNet  Google Scholar 

  • Wu C, Tai X (2009) Application of gray level variation statistic in gastroscopic image retrieval. In: Eigth IEEE/ACIS international conference on computer and information science, pp 342–346

  • Xiang-Yang W, Bei-Bei Z, Hong-Ying Y (2012) Content-based image retrieval by integrating color and texture features. Multimed Tools Appl 68(3):545–569

    Google Scholar 

  • Xie Y, Chen Z, Cheng Y, Zhang K, Agrawal A, Liao WK, Choudhary A (2013) Detecting and tracking disease outbreaks by mining social media data. In: Proceedings of the twenty-third international joint conference on artificial intelligence (IJCAI’13)

  • Xu X, Lee DJ, Antani S, Long L (2008) A spine X-ray image retrieval system using partial shape matching. IEEE Trans Inf Technol Biomed 12(1):100–108

    Article  Google Scholar 

  • Yogapriya J, Ila V (2013) An integrated framework based on texture features, cuckoo search and relevance vector machine for medical image retrieval system. Am J Appl Sci 10(11):1398–1412

    Article  Google Scholar 

  • Yue J, Li Z, Liu L, Fu Z (2010) Content-based image retrieval using color and texture fused features. Math Comput Model 54:1121–1127

    Article  Google Scholar 

  • Zeyad SY, Dzulkifli M, Tanzila S, Mohammed HA, Amjad R, Al-R Mznah, Al-D Abdullah (2014) Content-based image retrieval using PSO and k-means clustering algorithm. Arab J Geosci 8(8):6211–6224. doi:10.1007/s12517-014-1584-7

    Google Scholar 

  • Zhang Q, Izquierdo E (2013) Histology image retrieval in optimized multi-feature spaces. IEEE J Biomed Health Inform 17:240–249

    Article  Google Scholar 

  • Zhang D, Lu G (2004) Review of shape representation and description techniques. Pattern Recognit 37(1):1–19

    Article  Google Scholar 

  • Zhang J, Ye L (2010) Series feature aggregation for content-based image retrieval. Comput Electr Eng 36(4):691–701

    Article  MATH  Google Scholar 

  • Zhang G, Ma Z M, He Y, Zhao T (2008) Texture Characteristic extraction for dominant directions in content-based medical image retrieval. In: IEEE international conference on biomedical engineering and informatics (BMEI), pp 253–257

  • Zhang D, Islam MdM, Lu G (2012) A review on automatic image annotation techniques. Pattern Recognit 45:346–362

    Article  Google Scholar 

  • Zheng L, Wetzel AW, Gilbertson J, Becich MJ (2003) Design and analysis of a content-based pathology image retrieval system. IEEE Trans Inf Technol Biomed 7(4):245–255

    Google Scholar 

  • Zhi W, Wenwu Z, Peng C, Lifeng S, Shiqiang Y (2013) Social media recommendation. Social Media Retr Comput Commun Netw. doi:10.1007/978-1-4471-4555-43

    Google Scholar 

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  1. Department of Computer Sciences, ISG, BESTMOD, Tunis, Tunisia

    Mouhamed Gaith Ayadi, Riadh Bouslimi & Jalel Akaichi

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  1. Mouhamed Gaith Ayadi
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Ayadi, M.G., Bouslimi, R. & Akaichi, J. A medical image retrieval scheme through a medical social network. Netw Model Anal Health Inform Bioinforma 5, 23 (2016). https://doi.org/10.1007/s13721-016-0130-9

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