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International Journal of Speech Technology

, Volume 22, Issue 3, pp 739–767 | Cite as

Efficient anomaly detection from medical signals and images

  • Ahmed SedikEmail author
  • Heba M. Emara
  • Asmaa Hamad
  • Eman M. Shahin
  • Noha A. El-Hag
  • Ali Khalil
  • Fatma Ibrahim
  • Zeinab M. Elsherbeny
  • Mahmoud Elreefy
  • O. Zahran
  • Heba A. El-Khobby
  • Ghada M. El Banby
  • Mohamed Elwakeil
  • Walid El-Shafai
  • Ashraf A. M. Khalaf
  • Mohamed Rihan
  • Waleed Al-Nuaimy
  • Taha E. Taha
  • Mahmoud A. Attia
  • Adel S. El-Fishawy
  • El-Sayed M. El-Rabaie
  • Moawad I. Dessouky
  • Nagy W. Messiha
  • Ibrahim M. Eldokany
  • Turky N. Alotaiby
  • Saleh A. Alshebeili
  • Fathi E. Abd El-Samie
Article
  • 94 Downloads

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.

Keywords

EEG SIFT Seizure detection and prediction Digital filtering Diabetic retinopathy Blood vessels Exudates Micro-aneurysms Texture analysis 

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Ahmed Sedik
    • 7
    Email author
  • Heba M. Emara
    • 2
  • Asmaa Hamad
    • 2
  • Eman M. Shahin
    • 2
  • Noha A. El-Hag
    • 2
  • Ali Khalil
    • 1
  • Fatma Ibrahim
    • 2
  • Zeinab M. Elsherbeny
    • 2
  • Mahmoud Elreefy
    • 2
  • O. Zahran
    • 2
  • Heba A. El-Khobby
    • 3
  • Ghada M. El Banby
    • 8
  • Mohamed Elwakeil
    • 2
  • Walid El-Shafai
    • 2
  • Ashraf A. M. Khalaf
    • 1
  • Mohamed Rihan
    • 2
  • Waleed Al-Nuaimy
    • 6
  • Taha E. Taha
    • 2
  • Mahmoud A. Attia
    • 3
  • Adel S. El-Fishawy
    • 2
  • El-Sayed M. El-Rabaie
    • 2
  • Moawad I. Dessouky
    • 2
  • Nagy W. Messiha
    • 2
  • Ibrahim M. Eldokany
    • 2
  • Turky N. Alotaiby
    • 4
  • Saleh A. Alshebeili
    • 5
    • 9
  • Fathi E. Abd El-Samie
    • 2
  1. 1.Department of Electrical Engineering, Faculty of EngineeringMinia UniversityMiniaEgypt
  2. 2.Department of Electronics and Electrical Communications EngineeringFaculty of Electronic Engineering, Menoufia UniversityMenoufEgypt
  3. 3.Department of Electronics and Electrical Communications Engineering, Faculty of EngineeringTanta UniversityTantaEgypt
  4. 4.KACSTRiyadhKingdom of Saudi Arabia
  5. 5.KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS)King Saud UniversityRiyadhSaudi Arabia
  6. 6.Department of Electrical Engineering and Electronics, The University of LiverpoolLiverpoolUK
  7. 7.Department of the Robotics and Inteligent MachinesFaculty of Artificial Inteligence, Kafrelsheikh UniversityKafrelsheikhEgypt
  8. 8.Department of Industrial Electronics and Control EngineeringFaculty of Electronic Engineering, Menoufia UniversityMenoufEgypt
  9. 9.Department of Electrical EngineeringKing Saud UniversityRiyadhSaudi Arabia

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