Estimation of human red blood cells size using light scattering images

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.

Detection of hypochromic anemia in blood sample microscopic images using adaptive network-based fuzzy inference system

“…Apostolopoulos et al measured the size of red blood cells using light scattering images and a radial basis function neural network (RBF) for estimating the RBC geometrical properties [2]. Buddhiwant and his colleagues used an approach based on fast Fourier transform for simultaneous determination of size and refractive index of a collection of red blood cells from the measured angular distribution of scattered light [3].…”