A methodology for estimating the shape of biconcave red blood cells using multicolor scattering images

In this paper, a novel methodology for estimating the shape of human biconcave red blood cells (RBCs), using color scattering images, is presented. The information retrieval process includes, image normalization, features extraction using two-dimensional discrete transforms, such as angular radial transform (ART), Zernike moments and Gabor filters bank and features dimension reduction using both independent component analysis (ICA) and principal component analysis (PCA). A radial basis neural network (RBF-NN) estimates the RBC geometrical properties. The proposed method is evaluated in both regression and identification tasks by processing images of a simulated device used to acquire scattering phenomena of moving RBCs. The simulated device consists of a tricolor light source (light emitting diode – LED) and moving RBCs in a thin glass. The evaluation database includes 23,625 scattering images, obtained by means of the boundary element method. The regression and identification accuracy of the actual RBC shape is estimated using three feature sets in the presence of additive white Gaussian noise from 60 to 10 dB SNR and systematic distortion, giving a mean error rate less than 1% of the actual RBC shape, and more than 99% mean identification rate.