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This paper proposes a novel patient-specific approach to channel selection and seizure detection based on estimating the histograms of multi-channel scalp electroencephalography (sEEG) signals. It consists of two main phases: training and testing. In the training phase, the signal is segmented into non-overlapping 10-second segments, with five histograms estimated for each segment. These histograms...
Breast cancer is one of the most common cancers worldwide. Breast cancer rates are high in low- and middle-income countries, where breast tumors are diagnosed late. Early detection of masses or abnormalities that indicate breast cancer is a very important step for treating breast cancer in its first stages. The early detection of breast cancer tumors depends on both the ability of radiologists to...
This paper proposes using both of the Histogram matching and wavelet fusion as an enhancement technique for infrared images (IR). Evaluation metrics for image enhancement includes entropy, standard deviation, edge intensity, contrast and average gradient. Results revealed that histogram matching of IR images to visual images enhances of the appearance of the images. Moreover, wavelet fusion can achieve...
This paper introduces a cepstral approach for the detection of landmines from acoustic images. This approach is based on transforming the 2D landmine images to 1D signals using a spiral scan to make object pixels as close as possible to each other after the scan. The Mel-frequency cepstral coefficients (MFCCs) and polynomial shape coefficients are extracted from these 1D signals to form a database...
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