Improving Anemic RBCs Images Recognition
Improving Anemic RBCs Recognition Using Spatial Spectral Statistical Features and Transformative Features With RBFNN
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In this research a new anemic red blood cells (RBCs) data set was collected and used to investigate the automatic recognition of the RBCs. It includes the required processes to extract Spatio- Statistical Spectral features of the RBCs and to utilize these features to recognize between normal RBCs and the other seven groups of abnormal RBCs from five most dominant types of anemic RBCs in Malaysia. Three sets of features were used and tested: geometrical,colo and textural. The suggested RBCs recognition scheme consists of four stages after data acquisition. The first one is to determine the back...
In this research a new anemic red blood cells (RBCs) data set was collected and used to investigate the automatic recognition of the RBCs. It includes the required processes to extract Spatio- Statistical Spectral features of the RBCs and to utilize these features to recognize between normal RBCs and the other seven groups of abnormal RBCs from five most dominant types of anemic RBCs in Malaysia. Three sets of features were used and tested: geometrical,colo and textural. The suggested RBCs recognition scheme consists of four stages after data acquisition. The first one is to determine the background and target colors, isolating the cell target area from the surrounding and tracing the external and internal boundaries of central pallor area pixels of the cell cut-out. Second is to determine the adopted geometrical features by using the trace points, such as: Fourier descriptors, aspect ratio and moments, which have been used to describe the shapes of RBCs. Some textural features were also computed to evaluate the spatial color variation within the RBCs. PCA technique was applied in order to remove redundant features that affected the classification accuracy and training time.
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