Efficient anomaly detection from medical signals and images

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 firs...

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Bibliographic Details
Published inInternational journal of speech technology Vol. 22; no. 3; pp. 739 - 767
Main Authors Sedik, Ahmed, Emara, Heba M., Hamad, Asmaa, Shahin, Eman M., A. El-Hag, Noha, Khalil, Ali, Ibrahim, Fatma, Elsherbeny, Zeinab M., Elreefy, Mahmoud, Zahran, O., El-Khobby, Heba A., El Banby, Ghada M., Elwakeil, Mohamed, El-Shafai, Walid, Khalaf, Ashraf A. M., Rihan, Mohamed, Al-Nuaimy, Waleed, Taha, Taha E., Attia, Mahmoud A., El-Fishawy, Adel S., El-Rabaie, El-Sayed M., Dessouky, Moawad I., Messiha, Nagy W., Eldokany, Ibrahim M., Alotaiby, Turky N., Alshebeili, Saleh A., Abd El-Samie, Fathi E.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.09.2019
Springer Nature B.V
Subjects
Anomalies
Artificial Intelligence
Automation
Blood vessels
Diabetic retinopathy
Diagnosis
Digital filters
Electroencephalography
Engineering
Image detection
Image processing
Image segmentation
Medical diagnosis
Medical imaging
Object recognition
Retinal images
Segmentation
Seizures
Signal processing
Signal,Image and Speech Processing
Social Sciences
Digital filtering
Diabetic retinopathy
Exudates
SIFT
Texture analysis
EEG
Micro-aneurysms
Blood vessels
Seizure detection and prediction
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Summary: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.
ISSN:1381-2416
1572-8110
DOI:10.1007/s10772-019-09610-z