A Review: Image Analysis Techniques to Improve Labeling Accuracy of Medical Image Classification

  • Mazniha Berahim
  • Noor Azah Samsudin
  • Shelena Soosay Nathan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)

Abstract

Medical images contain the Region of Interest (ROI) from the affected area in human body and provide useful information to support clinical decision-making for diagnostics as well as the treatment planning. Unfortunately, medical image data may contain noise, missing values, inhomogeneous ROI that may give inaccurate diagnostic. Therefore, image analysis techniques are needed to improve the quality of an image. Then, features extraction task will be performed to produce best feature of images which leads to better classification result for accurate diagnostic. Many techniques have been used for image analysis. However, limited review have been done in categorize the list of related techniques for each image analysis task in medical imaging application. Thus, the aims of this paper is to gather and present general overview of image analysis task and their techniques in order to inspire researcher, pathologist or radiologist to adapt it when analyzing different types of medical image. The current study of image analysis task was summarized and discussed in this paper.

Keywords

Image analysis technique Medical image Image classification 

Notes

Acknowledgement

This work is supported by UTHM under Short Term Grant Vot U660.

References

  1. 1.
    James, A., Dasarathy, B.: Medical image fusion: a survey of the state of the art. Inf. Fusion (2014)Google Scholar
  2. 2.
    Ganesan, K., Acharya, R.U., Chua, C.K., Min, L.C., Mathew, B., Thomas, A.K.: Decision support system for breast cancer detection using mammograms. Proc. Inst. Mech. Eng. H. 227(7), 721–732 (2013)CrossRefGoogle Scholar
  3. 3.
    Ghasemian, F., Mirroshandel, S.A., Monji-Azad, S., Azarnia, M., Zahiri, Z.: An efficient method for automatic morphological abnormality detection from human sperm images. Comput. Methods Prog. Biomed. 122(3), 409–420 (2015)CrossRefGoogle Scholar
  4. 4.
    Fu, K.-S., Rosenfeld, A.: Pattern recognition. IEEE Trans. Comput. C-25, 1336–1346 (1976)Google Scholar
  5. 5.
    Lee, L., Liew, S.-C.: A Survey of medical image processing tools. In: IEEE 4th International Conference Software Engineering Computer System (ICSECS) (2015)Google Scholar
  6. 6.
    Chiuchisan, I.: A New FPGA-based real-time configurable system for medical image processing. In: 4th IEEE International Conference E-Health Bioengineering–EHB 2013, pp. 0–3 (2013)Google Scholar
  7. 7.
    Asaduzzaman, A., Martinez, A., Sepehri, A.: Time-efficient image processing algorithm for multicore/ manycore parallel computing. In: Proceedings of IEEE Southeast Conference 2015 (2015)Google Scholar
  8. 8.
    Ahirwar, V., Yadav, H., Jain, A.: Hybrid model for preserving brightness over the digital image processing. IEEE 4th International Conference Computerized Communication Technology 1, 48–53 (2013)Google Scholar
  9. 9.
    Abdullah, S., Asy, M., Mimi, W., Wan, D., Ibrahim, F.: X-Ray image enhancement for anterior osteophyte diagnosis. IEEE Int. Electron. Symp. pp. 47–52 (2015)Google Scholar
  10. 10.
    Senthilkumaran, N., Thimmiaraja, J.: Histogram equalization for image enhancement using MRI brain images. In: 2014 World Congress Computing Communicable Technologies, pp. 80–83 (2014)Google Scholar
  11. 11.
    Fu, J.J.C., Yu, Y.W., Lin, H.M., Chai, J.W., Chen, C.C.C.: Feature extraction and pattern classification of colorectal polyps in colonoscopic imaging. Comput. Med. Imaging Graph. 38(4), 267–275 (2014)CrossRefGoogle Scholar
  12. 12.
    Li, X., Kang, Y.: A novel medical image enhancement method based on wavelet multi-resolution analysis. In: IEEE I8th International Conference Biomedical Engineering Informatics, pp. 727–731 (2015)Google Scholar
  13. 13.
    Beheshti, S.M.A., Ahmadi Noubari, H., Fatemizadeh, E., Khalili, M.: Classification of abnormalities in mammograms by new asymmetric fractal features. Biocybern. Biomed. Eng. 36(1), 56–65 (2014)CrossRefGoogle Scholar
  14. 14.
    Oak, P.V., Kamathe, P.R.S.: Contrast enhancement of brain MRI images using histogram based techniques. Int. J. Innov. Res. Electr. Electron. Instrument. Control Eng. 1(3), 90–94 (2013)Google Scholar
  15. 15.
    Albadarneh, A., Albadarneh, I., Alqatawna, J.: Iris Recognition System for Secure Authentication Based on Texture and Shape Features. IEEE Jordan Conference Applied Electronic Engineering Computized Technology, Iris (2015)CrossRefGoogle Scholar
  16. 16.
    Sivasundari, S., Kumar, R.S., Karnan, M.: Review of MRI Image Classification Techniques. Int. J. Res. Stud. Comput. Sci. Eng. 1(1), 21–28 (2014)Google Scholar
  17. 17.
    Rayudu, M., Jain, V., Kunda, M.R., Review of image processing techniques. In: IEEE Sixth International Conference Sensor Technology Review, pp. 320–325 (2012)Google Scholar
  18. 18.
    Krawczyk, B., Galar, M., Jelen, L., Herrera, F.: Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy. Appl. Soft Comput. J. 38, 714–726 (2016)CrossRefGoogle Scholar
  19. 19.
    GeethaRamani, R., Balasubramanian, L.: Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis. Biocybern. Biomed. Eng. 36(1), 102–118 (2015)MATHCrossRefGoogle Scholar
  20. 20.
    Rouhi, R., Jafari, M.: Classification of benign and malignant breast tumors based on hybrid level set segmentation. Expert Syst. Appl. 46, 45–59 (2016)CrossRefGoogle Scholar
  21. 21.
    Angayarkanni, A.S.P., Kamal, B.N.B.: Automatic classification of mammogram MRI using dendograms, Asian. J. Comput. Sci. Inf. Technol. J. 4, 78–81 (2012)Google Scholar
  22. 22.
    Legg, P.A., Rosin, P.L., Marshall, D., Morgan, J.E.: Feature neighbourhood mutual information for multi-modal image registration: an application to eye fundus imaging. Pattern Recogn. 48(6), 1937–1946 (2015)CrossRefGoogle Scholar
  23. 23.
    Chakraborty, S., Ray, R., Ghosh, S., Chatterjee, S., Chowdhuri, S., Dey, N.: Rigid image registration using parallel processing. In: Proceedings of International Conference Circuits, Communicable Control Comput. (I4C 2014) Rigid, no. November, pp. 21–22 (2014)Google Scholar
  24. 24.
    Woods, R.P., Mazziotta, J.C., Cherry, S.R.: MRI-PET registration with automated algorithm. J. Comput. Assist. Tomogr. 17(4), 536–546 (1993)CrossRefGoogle Scholar
  25. 25.
    Han, J., Pauwels, E.J., De Zeeuw, P.: Visible and infrared image registration in man-made environments employing hybrid visual features. Pattern Recognit. Lett. 34(1), 42–51 (2013)CrossRefGoogle Scholar
  26. 26.
    Cao, T., Zach, C., Modla, S., Powell, D., Czymmek, K., Niethammer, M.: Multi-modal Registration for Correlative Microscopy using image analogies. Med. Imag. Anal. 18(6), 914–926 (2014)CrossRefGoogle Scholar
  27. 27.
    Bedi, S.S., Agarwal, J., Agarwal, P.: Image fusion techniques and quality assessment parameters for clinical diagnosis: a review. Int. J. Adv. Res. Comput. Commun. Eng. 2(2), 1153–1157 (2013)Google Scholar
  28. 28.
    Sahoo, P.K., Pati, U.C.: Image Registration using Mutual Information with Correlation for Medical Image. IEEE (2015)Google Scholar
  29. 29.
    Patra, D., Pradhan, S.: Enhanced mutual information based medical image registration. IET Image Process. 10(5), 418–427 (2016)CrossRefGoogle Scholar
  30. 30.
    Bhatnagar, G., Wu, Q.M.J., Liu, Z.: Human Visual system inspired multi-modal medical image fusion framework. Expert Syst. Appl. 40(5), pp. 1708–1720 (2013)Google Scholar
  31. 31.
    Du, J., Li, W., Xiao, B., Nawaz, Q.: Union Laplacian Pyramid with Multiple Features for Medical Image Fusion. Neurocomputing 194, 326–339 (2016)CrossRefGoogle Scholar
  32. 32.
    Liu, S., Cai, W., Liu, S. Pujol, S., Kikinis, R., Feng, D.: Subject-centered multi-view feature fusion for neuroimaging retrival and classsification. IEEE Int. Conf. Image Process. 2505–2509 (2015)Google Scholar
  33. 33.
    Dimitrovski, I., Kocev, D., Kitanovski, I., Loskovska, S., Džeroski, S.: Improved Medical Image Modality Classification Using a Combination of Visual and Textual features. Comput. Med. Imag. Graph 39, 14–26 (2015)CrossRefGoogle Scholar
  34. 34.
    Wang, Q., Li, S., Qin, H., Hao, A.: Robust multi-modal medical image fusion via anisotropic heat diffusion guided low-rank structural analysis. Inf. Fusion 26, 103–121 (2015)CrossRefGoogle Scholar
  35. 35.
    Kumar, G., Bhatia, P.K.: A detailed review of feature extraction in image processing systems. In: 2014 Fourth International Conference Advanced Computerized Communication Technology. February 2014, pp. 5–12 (2014)Google Scholar
  36. 36.
    Tian, D.P.: A review on image feature extraction and representation techniques. Int. J. Multimed. Ubiquitous Eng. 8(4), 385–395 (2013)Google Scholar
  37. 37.
    Beura, S., Majhi, B., Dash, R.: Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing 154, 1–14 (2015)CrossRefGoogle Scholar
  38. 38.
    Khan, A., Syed, N.A., Reyaz, M.: Image processing techniques for brain tumor extraction from MRI images using SVM classifier. Int. J. Recent Innov. Trends Comput. Commun. 3(May), 2707–2711 (2015)Google Scholar
  39. 39.
    Saritha, M., Joseph, K.P., Mathew, A.T.: Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recogn. Lett. 34(16), 2151–2156 (2013)CrossRefGoogle Scholar
  40. 40.
    Hemanth, J.D., Vijila, C.K.S., Selvakumar, A.I., Anitha, J.: Performance improved iteration-free artificial neural networks for abnormal magnetic resonance brain image classification. Neurocomputing 130, 98–107 (2014)CrossRefGoogle Scholar
  41. 41.
    Sudeb, D., Manish, C., Kundu, M.K.: Brain MR image classification using multi- scale geometric analysis of ripplet. Prog. Electromagn. Res. 137(February), 1–17 (2013)Google Scholar
  42. 42.
    Liu, X., Zeng, Z.: A new automatic mass detection method for breast cancer with false positive reduction. Neurocomputing 152, 388–402 (2015)CrossRefGoogle Scholar
  43. 43.
    Rastghalam, R., Pourghassem, H.: Breast cancer detection using MRF-based probable texture feature and decision-level fusion-based classification using HMM on thermography images. Pattern Recognit. 51, 176–186 (2014)CrossRefGoogle Scholar
  44. 44.
    Sharif, M.S., Qahwaji, R., Ipson, S., Brahma, A.: Medical image classification based on artificial intelligence approaches: a practical study on normal and abnormal confocal corneal images. Appl. Soft Comput. J. 36, 269–282 (2015)CrossRefGoogle Scholar
  45. 45.
    Ota, K., Oishi, N., Ito, K., Fukuyama, H.: Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer’s disease. J. Neurosci. Methods 256, 168–183 (2015)CrossRefGoogle Scholar
  46. 46.
    Thamilselvan, P., Sathiaseelan, J.G.R.: An enhanced k nearest neighbor method to detecting and classifying MRI lung cancer images for large amount data. Int. J. Appl. Eng. Res. ISSN 0973-4562, 11(6), 4223–4229 (2016)Google Scholar
  47. 47.
    Mbaga, A.H., ZhiJun, P.: Pap Smear images classification for early detection of cervical cancer. Int. J. Comput. Appl. 118(7), 8887 (0975–8887) (2015)Google Scholar
  48. 48.
    Feizi-Derakhshi, M.-R., Ghaemi, M.: Classifying different feature selection algorithms based on the search strategies. Int. Conf. Mach. Learn. Electr. Mech. Eng. (ICMLEME’ 2014), 17–21 (2014)Google Scholar
  49. 49.
    Huber, M.B., Bunte, K., Nagarajan, M.B., Biehl, M., Ray, L.A., Wismüller, A.: Texture feature ranking with relevance learning to classify interstitial lung disease patterns. Artif. Intell. Med. 56(2), 91–97 (2012)CrossRefGoogle Scholar
  50. 50.
    Rathi, V.P.G.P., Palani, S.: Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis. Int. J. Comput. Inf. Sci. Eng. 2(4), 131–146 (2012)Google Scholar
  51. 51.
    Mlambo, N., Cheruiyot, W.K., Kimwele, M.W.: A survey and comparative study of filter and wrapper feature selection techniques. Int. J. Eng. Sci. 5(8), 57–67 (2016)Google Scholar
  52. 52.
    Aswathy, M.A., Jagannath, M.: Detection of breast cancer on digital histopathology images: present status and future possibilities. Informat. Med. Unlocked, November, pp. 0–1 (2016)Google Scholar
  53. 53.
    Khazendar, S., Al-Assam, H., Du, H., Jassim, S., Sayasneh, A., Bourne, T., Kaijser, J., Timmerman, D.: Automated classification of static ultrasound images of ovarian tumours based on decision level fusion. In: 6th Computerized Science Electron Engineering Conference Proceedings, pp. 148–153 (2014)Google Scholar
  54. 54.
    Sanjeev Kumar, P.M., Chatterjee, S.: Computer aided diagnostic for cancer detection using MRI images of brain (brain tumor detection and classification system). IEEE Annual Indian Conference, (2016)Google Scholar
  55. 55.
    Chen, Y., Ling, L., Huang, Q.: Classification of breast tumors in ultrasound using biclustering mining and neural network. In: 9th Int. Congr. Image Signal Process. Biomed. Eng. Informatics (CISP-BMEI 2016), pp. 1787–1791 (2016)Google Scholar
  56. 56.
    Zeng, N., Wang, Z., Zineddin, B., Li, Y., Du, M., Xiao, L., Liu, X., Young, T.: Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach. IEEE Trans. Med. Imag. 33(5), 1129–1136 (2014)CrossRefGoogle Scholar
  57. 57.
    Mohammadi, S.M., Helfroush, M.S., Kazemi, K: Novel shape texture feature extraction for medical x-ray image classification. Int. J. Innov. Comput. Inf. Control 81(B) (2012)Google Scholar
  58. 58.
    Sudarshan, V., Acharya, U.R., Ng, E. Y.-K.S., Chou, M., Tan, R.S.: Automated identification of infarcted myocardium tissue characterisation using ultrasound images: a review. IEEE Rev. Biomed. Eng. PP(99), 1 (2014)Google Scholar
  59. 59.
    Aalaei, S., Shahraki, H., Rowhanimanesh, A., Eslami, S.: Feature selection using genetic algorithm for breast cancer diagnosis: experiment on three different datasets. Iran. J. Basic Med. Sci. 6, 476–482 (2016)Google Scholar
  60. 60.
    Al-Kadi, O.S.: A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours. Comput. Med. Imaging Graph. 41, 67–79 (2015)CrossRefGoogle Scholar
  61. 61.
    Liberman, G., Louzoun, Y., Aizenstein, O., Blumenthal, D.T., Bokstein, F., Palmon, M., Corn, B.W., Ben Bashat, D.: Automatic multi-modal mr tissue classification for the assessment of response to Bevacizumab in patients with glioblastoma. Eur. J. Radiol. 82, 2, e87–e94 (2013)Google Scholar
  62. 62.
    Battula, B.P., Prasad, R.S.: An overview of recent machine learning strategies in data mining. Int. J. Adv. Comput. Sci. Appl. 4(3), 50–54 (2013)Google Scholar
  63. 63.
    Xiang, Z., Lv, X., Zhang, K.: An Image Classification Method Based on Multi-feature Fusion and Multi-kernel SVM, 2014 Seventh Int. Symp. Comput. Intell. Des. 2(1), 49–52 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Mazniha Berahim
    • 1
  • Noor Azah Samsudin
    • 2
  • Shelena Soosay Nathan
    • 1
  1. 1.Department of Information Technology, Center for Diploma StudiesUniversiti Tun Hussein Onn MalaysiaParit Raja, Batu PahatMalaysia
  2. 2.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaParit Raja, Batu PahatMalaysia

Personalised recommendations