A computer vision approach is presented for border detection of malaria infected cells in microscopic blood images for accurate diagnosis. First, the microscopic 24-bits RGB color blood image converted in to 8-bits gray scale image for a single channel procesing. The poroposed two-stage
thresholdingmethod used for segmentation of malaria infected cells. Regarding border irregularities, the chosen descriptor is the perimeter factor and 4-connected neighbourhood. The experimental results on benchmark dataset that comprises around 300 images show that the proposed method successfully
detects borders of malaria infected cells with no prior knowledge of the contents of the image without parameter tuning. The proposed one compared with other existing methods and results are discussed.
SpindleU-Net: An Adaptive U-Net Framework for Sleep Spindle Detection in Single-Channel EEG
