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Efficient anomaly detection from medical signals and images

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Abstract

Anomaly detection is a very vital area in medical signal and image processing due to its importance in automatic diagnosis. This paper presents three efficient anomaly detection approaches for applications related to Electroencephalogram (EEG) signal processing and retinal image processing. The first approach depends on the utilization of Scale-Invariant Feature Transform (SIFT) for automatic seizure detection. The second one is based on the utilization of digital filters in a statistical framework for seizure prediction. Finally, an automated Diabetic Retinopathy (DR) diagnosis approach is presented based on the segmentation and detection of anomalous objects from retinal images. The presented simulation results reveal the success of the proposed techniques towards automated medical diagnosis.

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Notes

  1. Statistics on Avoidable Blindness; The Initiative for the Elimination of Avoidable Blindness, Vision 2020, a Joint Program of the World Health Organization (WHO) and the International Agency for the Prevention of Blindness (IAPB). Retrieved May 25, 2016 from http://www.vision2020.org/main.cfm?type=WIBDIEBETIC.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, 61111, Egypt

    Ali Khalil & Ashraf A. M. Khalaf

  2. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Heba M. Emara, Asmaa Hamad, Eman M. Shahin, Noha A. El-Hag, Fatma Ibrahim, Zeinab M. Elsherbeny, Mahmoud Elreefy, O. Zahran, Mohamed Elwakeil, Walid El-Shafai, Mohamed Rihan, Taha E. Taha, Adel S. El-Fishawy, El-Sayed M. El-Rabaie, Moawad I. Dessouky, Nagy W. Messiha, Ibrahim M. Eldokany & Fathi E. Abd El-Samie

  3. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Heba A. El-Khobby & Mahmoud A. Attia

  4. KACST, Riyadh, 11442, Kingdom of Saudi Arabia

    Turky N. Alotaiby

  5. KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS), King Saud University, Riyadh, 11421, Saudi Arabia

    Saleh A. Alshebeili

  6. Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool, L69 3GJ, UK

    Waleed Al-Nuaimy

  7. Department of the Robotics and Inteligent Machines, Faculty of Artificial Inteligence, Kafrelsheikh University, Kafrelsheikh, Egypt

    Ahmed Sedik

  8. Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Ghada M. El Banby

  9. Department of Electrical Engineering, King Saud University, Riyadh, 11421, Saudi Arabia

    Saleh A. Alshebeili

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  1. Ahmed Sedik
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  2. Heba M. Emara
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  3. Asmaa Hamad
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  6. Ali Khalil
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  8. Zeinab M. Elsherbeny
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  9. Mahmoud Elreefy
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  10. O. Zahran
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  11. Heba A. El-Khobby
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  13. Mohamed Elwakeil
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  14. Walid El-Shafai
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  15. Ashraf A. M. Khalaf
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  16. Mohamed Rihan
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  17. Waleed Al-Nuaimy
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  18. Taha E. Taha
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  19. Mahmoud A. Attia
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  20. Adel S. El-Fishawy
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  21. El-Sayed M. El-Rabaie
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  22. Moawad I. Dessouky
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  25. Turky N. Alotaiby
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  26. Saleh A. Alshebeili
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  27. Fathi E. Abd El-Samie
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Correspondence to Ahmed Sedik.

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Sedik, A., Emara, H.M., Hamad, A. et al. Efficient anomaly detection from medical signals and images. Int J Speech Technol 22, 739–767 (2019). https://doi.org/10.1007/s10772-019-09610-z

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