Sickle cell disease (SCD) is the most popular inherited blood disease, that red blood cells change its shape form circular shape to sickle shape and loses its main job which carries oxygen throughout the body. The watershed segmentation method has become highly developed for automated analysis of overlapping red blood cell microscopic images. The aim of this work is to suppress over segmentation problem which is a major drawback of the watershed algorithm. The experimental resultsshowed that, watershed is most effective when done on filtered image using non local means de-noising method. The effectiveness of the proposed method is validated by analyzing the image segmentation quality measures. The proposed method provides higher performance in term of accuracy, sensitivity and specificity factors.
Mutwali, H (2021). Detection of sickle Cell Disease Based on an Improved Watershed Segmentation. Afribary. Retrieved from https://tracking.afribary.com/works/detection-of-sickle-cell-disease-based-on-an-improved-watershed-segmentation
Mutwali, Hala "Detection of sickle Cell Disease Based on an Improved Watershed Segmentation" Afribary. Afribary, 20 May. 2021, https://tracking.afribary.com/works/detection-of-sickle-cell-disease-based-on-an-improved-watershed-segmentation. Accessed 21 Nov. 2024.
Mutwali, Hala . "Detection of sickle Cell Disease Based on an Improved Watershed Segmentation". Afribary, Afribary, 20 May. 2021. Web. 21 Nov. 2024. < https://tracking.afribary.com/works/detection-of-sickle-cell-disease-based-on-an-improved-watershed-segmentation >.
Mutwali, Hala . "Detection of sickle Cell Disease Based on an Improved Watershed Segmentation" Afribary (2021). Accessed November 21, 2024. https://tracking.afribary.com/works/detection-of-sickle-cell-disease-based-on-an-improved-watershed-segmentation