Automatic Detection and Quantification of Malnutrition Identification using Iterative Structured Circle Detection Algorithm
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 10, Pages 3136-3144
Abstract
Counting and Segmentation of blood cells are an important step. It helps to extract the features to diagnose the diseases especially malaria, leukemia, anemia and malnutrition. The manual counting of white blood cells and red blood cells in microscopic images is difficult, inaccurate and time consuming process. So this process is very helpful for hematologist experts. It is very useful to perform faster and more accurate result. Here, Iterative structured circle detection algorithm is used for proposed method. This algorithm method used for the segmentation and counting of Wight Blood Cells and Red Blood Cells. The Thresholding method of Image processing is used for separation. Preprocessing step is applied to each and every cell type. The proposed method based on Modified Circle detection, this is used to automatically counting the blood cell images. To solve the detecting irregular circles, selecting the optimal circle, determine the number of iterations and initialization problem, the basic RCD algorithm is used. To determine segmentation accuracy, the validation method is used. It includes, Recall, Precision and F-measumement tests. The proposed method average accuracy is 98.4% for WBCs and 95.3% for RBCs. Based on this result, malnutrition is measured.
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