Skip to main content
Log in

Extraction of target region in lung immunohistochemical image based on artificial neural network

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

Abstract

Immunohistochemistry is widely used in clinical pathology analysis and diagnosis, the target regions segmentation is the key procedure and always provides support for many qualitative and quantitative analyses on digitized immunohistochemical image. In lung tissue immunohistochemistry applications, the target region needs to be extracted out of the whole image firstly. Most existing methods based on color cannot fulfill the extraction of antibody region. Thus, there is a need of effective extraction method. Methods: According to the features of target region in images to be processed, this paper presents a solution framework based on artificial neural network (ANN). Results: Six effective features of the candidate regions are analyzed and extracted as the inputs of the ANN; three-layers back propagation neural network with six inputs and one output is constructed, and ANN’s parameters are trained by the learning image set. By the trained ANN, target region core are obtained and then expanded to the whole target region through conditional expansion. Conclusion: Through testing the framework by testing image set and comparing with the main existing methods, it can be concluded that the proposed framework can remove non-target regions and extract the target regions well, while the contrast methods cannot remove all the non-target regions. Significance: The method presented in this paper has practical and potential significance to realize automated and quantitative tissue immunohistochemical image analysis.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

References

  1. Amaral T, McKenna SJ, Robertson K, Thompson A (2013) Classification and immunohistochemical scoring of breast tissue microarray spots. IEEE Trans Biomed Eng 60(10):2806–2814

    Article  Google Scholar 

  2. Awad M et al (2007) Multicomponent image segmentation using a genetic algorithm and artificial neural network. IEEE Geosci Remote Sens Lett 4(4):571–575

    Article  Google Scholar 

  3. Boughrara H, Chtourou M, Chokri BA, Chen L (2016) Facial expression recognition based on a mlp neural network using constructive training algorithm. Multimed Tools Appl 75(2):709–731

    Article  Google Scholar 

  4. Chen YL, et al. (2013) Using immunoadjuvant agent glycated chitosan to enhance anti-cancer stem like cell immunity induced by HIFU. SPIE Conference: Biophotonics and Immune Responses VIII, San Francisco, California, USA

  5. Ficarra E et al (2011) Automated segmentation of cells with IHC membrane staining. IEEE Trans Biomed Eng 58(5):1421–1429

    Article  Google Scholar 

  6. Forsberg F, et al. (2014) The antiangiogenic effects of a vascular endothelial growth factor decoy receptor can be monitored in vivo using contrast-enhanced ultrasound imaging. Mol. Imaging 13(2)

  7. Fu R, Shen H (2007) Study on immunohistochemical color image C- clustering segmentation technology based on analysis of criterion of colorimetry. Chin J Stereol Image Anal 12(1):6–10

    MathSciNet  Google Scholar 

  8. Haltaş A, Alkan A, Karabulut M (2014) Use of artificial neural network algorithm in the immunohistochemical dyeing based diagnosis of thyroid tumor. Proceedings of 2014 I.E. 22nd Signal Processing and Communications Applications Conference (SIU 2014), p 1106–1109

  9. Hatanaka Y et al (2008) Quantitative immuno-histochemical evaluation of HER2/neu expression with HercepTestTM in breast carcinoma by image analysis. Pathol Int 51(1):33–36

    Article  Google Scholar 

  10. Hinton G, Osindero S, Teh YA (2006) Fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  MathSciNet  MATH  Google Scholar 

  11. Hu C, Xu Z et al (2014) Semantic link network based model for organizing multimedia big data. IEEE Trans Emerg Top Comput 2(3):376–387

    Article  MathSciNet  Google Scholar 

  12. Hu C, Xu Z et al (2015) Video structured description technology for the new generation video surveillance system. Front Comput Sci 9(6):980–989

    Article  Google Scholar 

  13. Irshad H, Veillard A, Roux L, Racoceanu D (2014) Methods for nuclei detection, segmentation, and classification in digital histopathology: a review—current status and future potential. IEEE Rev Biomed Eng 7:97–114

    Article  Google Scholar 

  14. Irshad H et al (2014) Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential. IEEE Rev Biomed Eng 7:97–114

    Article  Google Scholar 

  15. Lejune M et al (2008) Quantification of diverse subcellular immunohistochemical markers with clinicobiological relevancies: validation of a new computer-assisted image analysis procedure. J Anat 21(6):868–878

    Article  Google Scholar 

  16. Liu BH et al (2000) Automatic analysis of liver tissue immunohistochemistry pathology image. Chin J Stereol Image Anal 5(4):226–229

    Google Scholar 

  17. Liu BH, et al. (2006) Automatic extraction of positive cells in tumor immunohistochemical pathology image based on YCbCr. Proceedings of the World Congress on Intelligent Control and Automation (WCICA), Dalian, China, p 9708–9712

  18. Masmoudi H et al (2009) Automated quantitative assessment of HER-2/neu immunohistochemical expression in breast cancer. IEEE Trans Med Imaging 28(6):916–925

    Article  Google Scholar 

  19. Mei YC, et al. (2012) Image segmentation via normalised cuts and clustering algorithm. Control System, Computing and Engineering (ICCSCE), 2012 I.E. International Conference on, Penang, Malaysia, p 430–435

  20. Mostaço-Guidolin LB et al (2014) Quantitative nonlinear optical assessment of atherosclerosis progression in rabbits. Anal Chem 86(13):6346–6354

    Article  Google Scholar 

  21. Mouelhi A, et al. (2014) A novel morphological segmentation method for evaluating estrogen receptors’ status in breast tissue images. 2014 1st International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2014, Tunis, Tunisia, p 177–182

  22. Ngo TA, Carneiro G (2015) Lung segmentation in chest radiographs using distance regularized level set and deep-structured learning and inference. Proceedings - International Conference on Image Processing, ICIP, p 2140–2143

  23. Peppelman M, et al. (2014) Application of leukotriene B4 and reflectance confocal microscopy as a noninvasive in vivo model to study the dynamics of skin inflammation. Skin Res. Technol

  24. Petersen K, Nielsen M, Diao P, Karssemeijer N (2014) Breast tissue segmentation and mammographic risk scoring using deep learning. Lect Notes Comput Sci 8539:88–94

    Article  Google Scholar 

  25. Pham NA et al (2007) Quantitative image analysis of immunohistochemical stains using a CMYK color model. Diagn Pathol 2:8

    Article  Google Scholar 

  26. Ramos-Pollán R et al (2014) High throughput location proteomics in confocal images from the human protein atlas using a bag-of-features representation. Adv Intell Syst Comput 232:77–82

    Article  Google Scholar 

  27. Roth HR, Farag A, Lu L, Turkbey EB, Summers RM (2015) Deep convolutional networks for pancreas segmentation in CT imaging. Progress in Biomedical Optics and Imaging - Proceedings of SPIE 9413

  28. Ruifrok AC (2001) Quantification of histochemical by color deconvolution. Anal Quant Cytol Histol 23(4):291–299

    Google Scholar 

  29. Ruifrok AC (2004) Comparison of quantification of histochemical staining by Hue-Saturation-Intensity (HSI) transformation and color deconvolution. Appl Immunohistochem Mol Morphol 11(1):85–91

    Google Scholar 

  30. Rusek K, Guzik P (2014) Two-stage neural network regression of eye location in face images. Multimed Tools Appl. doi:10.1007/s11042-014-2114-z, pp.1-14

    Google Scholar 

  31. Shi J, Malik J (2002) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Google Scholar 

  32. Shrestha S, et al. (2014) Integrated quantitative fractal polarimetric analysis of monolayer lung cancer cells. SPIE Conference: Polarization: Measurement, Analysis, and Remote Sensing XI, Maryland, USA

  33. Shu SJ (2013) Optimization of the scanning technique and diagnosis of pulmonary nodules with first-pass 64-detector-row perfusion VCT. Clin Imaging 37(2):256–264

    Article  Google Scholar 

  34. Suzani A, Rasoulian A, Seitel A, Fels S, Rohling RN, Abolmaesumi P (2015) Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images. Progress in Biomedical Optics and Imaging - Proceedings of SPIE 9415

  35. Suzuki M, et al. (2012) Second harmonic generation microscopy differentiates collagen type I and type III in COPD. SPIE Conference: Multiphoton Microscopy in the Biomedical Sciences XII, San Francisco, California, USA

  36. Tang J (2010) A color image segmentation algorithm based on region growing. Computer Engineering and Technology (ICCET), 2010 2nd International Conference on, Chengdu, China, p 634–637

  37. Trabelsi RB, Masmoudi AD, Masmoudi DS (2016) Hand vein recognition system with circular difference and statistical directional patterns based on an artificial neural network. Multimed Tools Appl 75(2):687–707

    Article  Google Scholar 

  38. Vincent P, Larochelle H, Bengio Y, et al. (2008) Extracting and composing robust features with denoising autoencoders. Proc of the 25th International Conference on Machine Learning. ACM Press, New York, p 1096–1103

  39. Wang H, Zhou ZG, Jie LM (2011) Automatic detection of lumina in mouse liver immunohisto-chemical color image using support vector machine and cellular neural network. Proceeding of 2011 Seventh International Conference on Computational Intelligence and Security, p 1086–1090

  40. Wang H et al (2011) A new kind of immunohistochemical image segmentation algorithms. Comput Appl Softw 28(6):54–56

    Google Scholar 

  41. Wang MM et al (2013) Study on immunohistochemical image core segmentation based on color separation. Comput Appl Softw 30(4):165–170

    Google Scholar 

  42. Wemmert C et al. (2013) Stain unmixing in brightfield multiplexed immunohistochemistry. Image processing (ICIP), 2013 20th IEEE International Conference on, Melbourne, Australia, p 1125–1129

  43. Xu Z et al (2015) Semantic based representing and organizing surveillance big data using video structural description technology. J Syst Softw 102:217–225

    Article  Google Scholar 

  44. Xu Z et al (2016) Semantic enhanced cloud environment for surveillance data management using video structural description. Computing 98(1–2):35–54

    Article  MathSciNet  MATH  Google Scholar 

  45. Zafirellis K (2008) Prognostic value of COX-2 immunohistochemical expression evaluated by quantitative image analysis in colorectal cancer. Acta Pathol Microbiol Immunol Scand 116(10):912–922

    Article  Google Scholar 

  46. Zidan A, et al. (2012) Level set-based CT liver image segmentation with watershed and artificial neural networks. Hybrid intelligent systems (HIS), 2012 12th International Conference on, Pune, India, p 96–102

Download references

Acknowledgments

This paper partially aided by the Shandong Province Young Scientist Foundation (BS2012DX034, BS2013NJ023), China Postdoctoral Science Foundation (2012 M521361), Shandong Province Natural Science Foundation (ZR2012EEM021), Project of Shandong Province Higher Educational Science and Technology Program (J13LN17), Graduate Student Science and Technology Innovation Fund of SDUST(YC140212), and Project of South Africa/China Research Collaboration in Science and Technology (2012DFG71060).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maoyong Cao.

Ethics declarations

Conflict of interest

This article content has no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, D., Wei, L. & Cao, M. Extraction of target region in lung immunohistochemical image based on artificial neural network. Multimed Tools Appl 75, 12227–12244 (2016). https://doi.org/10.1007/s11042-016-3459-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3459-2

Keywords

Navigation