Skip to main content
SpringerLink
Log in

Low dimensional multi-block neighborhood combination pattern for biomedical image retrieval

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Content based image retrieval (CBIR) has been a thrust area of research to retrieve relevant images quickly from a huge image database. In this pursuit, a low dimensional multi-block neighborhood combination pattern (MNCP) is proposed for biomedical image retrieval. Traditional local binary pattern (LBP) failed to capture the macro-structures present in the image. A multi-block technique is applied here to design a feature insensitive to noise. Further, MNCP computes the modified Weber’s ratio by encoding three different combinations of change in intensities among pixels to obtain unique patterns. This process considers sign and magnitude both of intensity changes and hence, the direction of intensity changes is also incorporated. In order to make the feature robust, these three combination patterns are concatenated. The most significant features of MNCP are selected to provide maximum inter class separability and variance using principal component analysis (PCA) and linear discriminant analysis (LDA) algorithms. Experiments are conducted on four very distinct and popular medical image datasets namely: OASIS MRI, VIA/I-ELCAP CT, Emphysema CT and MESSIDOR retinal database to examine the ability of the proposed method. Results of the proposed approach proves its superiority by outperforming the existing handcrafted as well as deep learning techniques in terms of average retrieval precision (ARP), average retrieval rate (ARR) and mean average precision (MAP). The proposed CBIR system takes very less time in retrieving the relevant images hence, it is suitable for real time applications as well.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Agarwal M, Maheshwari R (2020) Multichannel local ternary co-occurrence pattern for content-based image retrieval. Iran J Sci Technol Trans Electr Eng 44(1):495–504

    Article  Google Scholar 

  2. Agarwal M, Singhal A, Lall B (2018) 3D Local ternary co-occurrence patterns for natural, texture, face and bio medical image retrieval. Neurocomputing 313:333–345

    Article  Google Scholar 

  3. Agarwal M, Singhal A, Lall B (2019) Multi-channel local ternary pattern for content-based image retrieval. Pattern Anal Applic 22(4):1585–1596

    Article  MathSciNet  Google Scholar 

  4. Aggarwal A, Sharma S, Singh K, Singh H, Kumar S (2019) A new approach for effective retrieval and indexing of medical images. Biomed Signal Process Control 50:10–34

    Article  Google Scholar 

  5. Baby CG, Chandy DA (2013) Content-based retinal image retrieval using dual-tree complex wavelet transform. In: International conference on signal processing, Image Processing & Pattern Recognition. IEEE, pp 195–199

  6. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  7. Cai Y, Li Y, Qiu C, Ma J, Gao X (2019) Medical image retrieval based on convolutional neural network and supervised hashing. IEEE Access 7:51877–51885

    Article  Google Scholar 

  8. Chakraborty S, Singh SK, Chakraborty P (2017) Local directional gradient pattern: a local descriptor for face recognition. Multimed Tools Appl 76 (1):1201–1216

    Article  Google Scholar 

  9. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, vol 1, pp 886–893

  10. Das P, Neelima A (2020) A robust feature descriptor for biomedical image retrieval, IRBM. https://doi.org/10.1016/j.irbm.2020.06.007

  11. 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

    Google Scholar 

  12. Deep G, Kaur L, Gupta S (2018) Local quantized extrema quinary pattern: a new descriptor for biomedical image indexing and retrieval. Comput Methods Biomech Biomed Eng Imaging Vis 6(6):687–703

    Google Scholar 

  13. Di Ruberto C (2017) Histogram of radon transform and texton matrix for texture analysis and classification. IET Image Process 11(9):760–766

    Article  Google Scholar 

  14. Dubey SR, Singh SK, Singh RK (2015) Local bit-plane decoded pattern: a novel feature descriptor for biomedical image retrieval. IEEE J Biomed Health Inf 20(4):1139–1147

    Article  Google Scholar 

  15. Dubey SR, Singh SK, Singh RK (2016) Novel local bit-plane dissimilarity pattern for computed tomography image retrieval. Electron Lett 52(15):1290–1292

    Article  Google Scholar 

  16. Dubey SR, Roy SK, Chakraborty S, Mukherjee S, Chaudhuri BB (2019) Local bit-plane decoded convolutional neural network features for biomedical image retrieval. Neural Comput Appl:1-13

  17. Galshetwar G, Waghmare L, Gonde A, Murala S (2017) Edgy salient local binary patterns in inter-plane relationship for image retrieval in diabetic retinopathy. Procedia Comput Sci 115:440–447

    Article  Google Scholar 

  18. Galshetwar G, Waghmare L, Gonde A, Murala S (2018) Multi-dimensional multi-directional mask maximum edge pattern for bio-medical image retrieval. Int J Multimed Inf Retr 7(4):231–239

    Article  Google Scholar 

  19. Galshetwar G, Waghmare L, Gonde A, Murala S (2019) Local energy oriented pattern for image indexing and retrieval. J Vis Commun Image Represent 64:102615. https://doi.org/10.1016/j.jvcir.2019.102615

  20. Ghose S, Das A, Bhunia AK, Roy PP (2020) Fractional local neighborhood intensity pattern for image retrieval using genetic algorithm. Multimed Tools Appl:1–26

  21. Gonde A, Patil PW, Galshetwar G, Waghmare L (2017) Volumetric local directional triplet patterns for biomedical image retrieval. In: Fourth international conference on image information processing (ICIIP). IEEE, pp 1–6

  22. Hassan G, Hosny KM, Farouk RM, Alzohairy AM (2020) An efficient retrieval system for biomedical images based on radial associated laguerre moments. IEEE Access 8:175669–175687

  23. He X, Yan S, Hu Y, Niyogi P, Zhang H-J (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  24. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  25. Huang Y, Wang Y, Tan T (2006) Combining statistics of geometrical and correlative features for 3d face recognition. In: BMVC, Citeseer, pp 879–888

  26. Hussain CA, Rao DV, Mastani SA (2020) Retrievenet: a novel deep network for medical image retrieval. Evol Intel:1–10

  27. Kasban H, Salama D (2019) A robust medical image retrieval system based on wavelet optimization and adaptive block truncation coding. Multimed Tools Appl 78(24):35211–35236

    Article  Google Scholar 

  28. Kumar Y, Aggarwal A, Tiwari S, Singh K (2018) An efficient and robust approach for biomedical image retrieval using zernike moments. Biomed Signal Process Control 39:459–473

    Article  Google Scholar 

  29. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  30. Li M, Yuan B (2005) 2D-lda: a statistical linear discriminant analysis for image matrix. Pattern Recogn Lett 26(5):527–532

    Article  Google Scholar 

  31. Liu Y, Zhang D, Lu G, Ma W-Y (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recogn 40:262–282

    Article  MATH  Google Scholar 

  32. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  33. Mandal M, Chaudhary M, Vipparthi SK, Murala S, Gonde A, Nagar SK (2019) ANTIC: Antithetic Isomeric cluster patterns for medical image retrieval and change detection. IET Comput Vis 13(1):31–43

    Article  Google Scholar 

  34. Mohite N, Waghmare L, Gonde A, Vipparthi S (2019) 3D Local circular difference patterns for biomedical image retrieval. Int J Multimed Inf Retr 8(2):115–125

    Article  Google Scholar 

  35. Murala S, Wu QJ (2013) Local ternary co-occurrence patterns: a new feature descriptor for MRI and CT image retrieval. Neurocomputing 119:399–412

    Article  Google Scholar 

  36. Murala S, Wu QJ (2014) MRI And CT image indexing and retrieval using local mesh peak valley edge patterns. Signal Process Image Commun 29 (3):400–409

    Article  Google Scholar 

  37. Muralaa S, Jonathan Wu QM (2015) Spherical symmetric 3D local ternary patterns for natural, texture and biomedical image indexing and retrieval. Neurocomputing 149:1502–1514

    Article  Google Scholar 

  38. 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 

  39. 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

    Article  MATH  Google Scholar 

  40. Pinapatruni R, Bindu CS (2020) Learning image representation from image reconstruction for a content-based medical image retrieval. Signal Image Video Process:1–8

  41. Shinde A, Rahulkar A, Patil C (2019) Content based medical image retrieval based on new efficient local neighborhood wavelet feature descriptor. Biomed Eng Lett 9(3):387–394

    Article  Google Scholar 

  42. Shivashankar S, Veerashetty SKS (2011) PCA plus LDA on wavelet co-occurrence histogram features: Application to CBIR. In: International Workshop on Multi-disciplinary Trends in Artificial Intelligence. Springer, pp 193–200

  43. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556

  44. Singhal A, Agarwal M, Pachori RB (2020) Directional local ternary co-occurrence pattern for natural image retrieval. Multimedia Tools and Applications

  45. Song W, Zhang Y, Liu F, Chai Z, Ding F, Qian X, Park SC (2018) Taking advantage of multi-regions-based diagonal texture structure descriptor for image retrieval. Expert Syst Appl 96:347–357

    Article  Google Scholar 

  46. Sorensen L, Shaker SB, De Bruijne M (2010) Quantitative analysis of pulmonary emphysema using local binary patterns. IEEE Trans Med Imaging 29 (2):559–569

    Article  Google Scholar 

  47. Srivastava P, Khare A (2018) Utilizing multiscale local binary pattern for content-based image retrieval. Multimed Tools Appl 77(10):12377–12403

    Article  Google Scholar 

  48. Sucharitha G, Senapati RK (2020) Biomedical image retrieval by using local directional edge binary patterns and zernike moments. Multimed Tools Appl 79(3):1847–1864

    Article  Google Scholar 

  49. 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

    Article  MathSciNet  MATH  Google Scholar 

  50. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  51. Verma M, Raman B (2015) Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval. J Vis Commun Image Represent 32:224–236

    Article  Google Scholar 

  52. Verma M, Raman B (2016) Local tri-directional patterns: a new texture feature descriptor for image retrieval. Digital Signal Process 51:62–72

    Article  MathSciNet  Google Scholar 

  53. Verma M, Raman B (2018) Local neighborhood difference pattern: a new feature descriptor for natural and texture image retrieval. Multimed Tools Appl 77(10):11843–11866

    Article  Google Scholar 

  54. Wang X-Y, Chen Z-F (2009) A fast fractal coding in application of image retrieval. Fractals 17(04):441–450

    Article  MathSciNet  MATH  Google Scholar 

  55. Wang X, Wang Z (2013) A novel method for image retrieval based on structure elements descriptor. J Vis Commun Image Represent 24(1):63–74

    Article  Google Scholar 

  56. Wang X, Wang Z (2014) The method for image retrieval based on multi-factors correlation utilizing block truncation coding. Pattern Recogn 47(10):3293–3303

    Article  Google Scholar 

  57. Wang C, Wang X, Xia Z, Ma B, Shi Y-Q (2019) Image description with polar harmonic fourier moments. IEEE Trans Circ Syst Video Technol 30 (12):4440–4452

    Article  Google Scholar 

  58. Wang C, Wu P, Yan L, Ye Z, Chen H, Ling H (2021) Image classification based on principal component analysis optimized generative adversarial networks. Multimed Tools Appl 80(6):9687–9701

    Article  Google Scholar 

  59. Xu Y, Li Z, Yang J, Zhang D (2017) A survey of dictionary learning algorithms for face recognition. IEEE Access 5:8502–8514

    Article  Google Scholar 

  60. Yan L, Lu H, Wang C, Ye Z, Chen H, Ling H (2019) Deep linear discriminant analysis hashing for image retrieval. Multimed Tools Appl 78 (11):15101–15119

    Article  Google Scholar 

  61. Yang J, Jin Z, Yang J-y, Zhang D, Frangi AF (2004) Essence of kernel Fisher discriminant: KPCA plus LDA. Pattern Recogn 37(10):2097–2100

    Article  Google Scholar 

  62. Yang W, Zhang X, Li J (2020) A local multiple patterns feature descriptor for face recognition. Neurocomputing 373:109–122

    Article  Google Scholar 

  63. [dataset] OASIS-MRI database, (http://www.oasis-brains.org/) ([Online; accessed 07-June-2021])

  64. [dataset] VIA/I-ELCAP CT Lung Image Dataset, (http://www.via.cornell.edu/-databases/lungdb/) ([Online; accessed 07-June-2021])

  65. [dataset] Emphysema-CT database, (http://image.diku.dk/emphysemadatabase) ([Online; accessed 07-June-2021])

  66. [dataset] MESSIDOR Database, (http://messidor.crihan.fr) ([Online; accessed 07-June-2021])

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, India

    Ankita Wadhera & Megha Agarwal

Authors
  1. Ankita Wadhera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Megha Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Megha Agarwal.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wadhera, A., Agarwal, M. Low dimensional multi-block neighborhood combination pattern for biomedical image retrieval. Multimed Tools Appl 81, 27853–27877 (2022). https://doi.org/10.1007/s11042-022-12089-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12089-7

Keywords

Search

Navigation