Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.3233/JCM-2009-0254 | J. Comput. Meth. in Science and Engineering |
Keywords | Field | DocType |
rbc geometrical property,light scattering image,novel method,identification task,human rbc,boundary element method,actual rbc size,identification accuracy,percent mean identification rate,human red blood cell,db snr,radial basis function,neural network,light scattering | Normalization (image processing),Light beam,Radial basis function,Mean squared error,Algorithm,Optics,Distortion,Additive white Gaussian noise,Mathematics,Wavelet,Light scattering | Journal |
Volume | Issue | ISSN |
9 | 1 | 1472-7978 |
Citations | PageRank | References |
2 | 0.58 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. Apostolopoulos | 1 | 2 | 1.25 |
| Stephanos V. Tsinopoulos | 2 | 5 | 1.68 |
| E. Dermatas | 3 | 86 | 11.86 |