Abstract
In this paper, a novel method for the estimation of the human Red Blood Cell (RBC) size using light scattering images is presented. The information retrieval process includes, image normalization, a two-dimensional Discrete Cosine Transformation (DCT2) or Wavelet transformation (DWT2), and a Radial Basis Neural Network (RBF-NN) estimates the RBC geometrical properties. The proposed method is evaluated in both regression and identification tasks when three important geometrical properties of the human RBC are estimated using a database of 1575 simulated images generated with the boundary element method. The experimental setup consists of a light beam at 632.8 nm and moving RBCs in a thin glass and additive noise distortion is simulated using white Gaussian noise from 60 to 0 dB SNR. The regression and identification accuracy of actual RBC sizes is estimated using three feature sets, giving a mean error rate less than 1 percent of the actual RBC size, in case of noisy image data at 10 dB SNR or better, and more than 97 percent mean identification rate.