Efficient anomaly detection from medical signals and images

Sedik, Ahmed; Emara, Heba M.; Hamad, Ahmed; Shahin, Eman M.; El-Hag, Noha A.; Khalil, Ali; Ibrahim, Fatma E.; Elsherbeny, Zeinab M.; Elreefy, Mahmoud; Zahran, O.; El-Khobby, Heba A.; Banby, Ghada M. El; 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.; El-Dokany, Ibrahim M.; Alotaiby, Turky N.; Alshebeili, Saleh A.; El‐Samie, Fathi E. Abd

doi:10.1007/s10772-019-09610-z

Cited by 19 publications

(11 citation statements)

References 29 publications

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“…Assume that there is an HR GPR image represented as f, which is a lexicographically-ordered vector. To get an LR version g of this image through the decimation process, there is a need to multiply f by the decimation operator D as follows [24][25][26][27][28][29][30][31]:…”

Section: Decimation Modelmentioning

confidence: 99%

“…where D is the decimation operator that converts the HR image to an LR image. It is represented as [24][25][26][27][28][29][30][31]:…”

Section: Decimation Modelmentioning

confidence: 99%

“…while the symbol ⊗ represents the Kronecker product, and D 1 is a filtering and down-sampling operator. In the case at hand [24][25][26][27][28][29][30][31],…”

Section: Decimation Modelmentioning

confidence: 99%

“…This approach is based on obtaining an image with maximum entropy. The entropy [27][28][29][30][31] of the image is defined as follows:…”

Section: Maximum Entropy Interpolationmentioning

confidence: 99%

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Gauss Gradient and SURF Features for Landmine Detection from GPR Images

El-Ghamry¹,

El‐Shafai²,

Abdalla³

et al. 2022

Computers, Materials &Amp; Continua

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Recently, ground-penetrating radar (GPR) has been extended as a well-known area to investigate the subsurface objects. However, its output has a low resolution, and it needs more processing for more interpretation. This paper presents two algorithms for landmine detection from GPR images. The first algorithm depends on a multi-scale technique. A Gaussian kernel with a particular scale is convolved with the image, and after that, two gradients are estimated; horizontal and vertical gradients. Then, histogram and cumulative histogram are estimated for the overall gradient image. The bin values on the cumulative histogram are used for discrimination between images with and without landmines. Moreover, a neural classifier is used to classify images with cumulative histograms as feature vectors. The second algorithm is based on scale-space analysis with the number of speeded-up robust feature (SURF) points as the key parameter for classification. In addition, this paper presents a framework for size reduction of GPR images based on decimation for efficient storage. The further classification steps can be performed on images after interpolation. The sensitivity of classification accuracy to the interpolation process is studied in detail.

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Section: Decimation Modelmentioning

confidence: 99%

“…where D is the decimation operator that converts the HR image to an LR image. It is represented as [24][25][26][27][28][29][30][31]:…”

Section: Decimation Modelmentioning

confidence: 99%

“…while the symbol ⊗ represents the Kronecker product, and D 1 is a filtering and down-sampling operator. In the case at hand [24][25][26][27][28][29][30][31],…”

Section: Decimation Modelmentioning

confidence: 99%

“…This approach is based on obtaining an image with maximum entropy. The entropy [27][28][29][30][31] of the image is defined as follows:…”

Section: Maximum Entropy Interpolationmentioning

confidence: 99%

See 2 more Smart Citations

Gauss Gradient and SURF Features for Landmine Detection from GPR Images

El-Ghamry¹,

El‐Shafai²,

Abdalla³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

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“…Sedik et al (Sedik et al, 2019) presented a technique for anomaly detection from medical signals and images. This technique is considered as an automated diagnosis tool from retinal images.…”

Section: Related Workmentioning

confidence: 99%

Classification of retinal images based on convolutional neural network

El-Hag

Sedik

El‐Shafai

et al. 2020

Microscopy Res & Technique

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Automatic detection of maculopathy disease is a very important step to achieve high‐accuracy results for the early discovery of the disease to help ophthalmologists to treat patients. Manual detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates from retinal images is applied for the maculopathy disease diagnosis. The first proposed framework in this paper for retinal image classification begins with fuzzy preprocessing in order to improve the original image to enhance the contrast between the objects and the background. After that, image segmentation is performed through binarization of the image to extract both blood vessels and the optic disc and then remove them from the original image. A gradient process is performed on the retinal image after this removal process for discrimination between normal and abnormal cases. Histogram of the gradients is estimated, and consequently the cumulative histogram of gradients is obtained and compared with a threshold cumulative histogram at certain bins. To determine the threshold cumulative histogram, cumulative histograms of images with exudates and images without exudates are obtained and averaged for each type, and the threshold cumulative histogram is set as the average of both cumulative histograms. Certain histogram bins are selected and thresholded according to the estimated threshold cumulative histogram, and the results are used for retinal image classification. In the second framework in this paper, a Convolutional Neural Network (CNN) is utilized to classify normal and abnormal cases.

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