An automatic microarray image gridding technique based on continuous wavelet transform

  • Authors:

  • Emmanouil Athanasiadis;Dionisis Cavouras;Panagiota Spyridonos;Ioannis Kalatzis;George Nikiforidis

  • Affiliations:

  • Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medical Science, University of Patras, Rion, Patras, Greece;Medical Image and Signal Processing Laboratory, Department of Medical Instruments Technology, Technological Educational Institute of Athens, Athens, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medical Science, University of Patras, Rion, Patras, Greece;Medical Image and Signal Processing Laboratory, Department of Medical Instruments Technology, Technological Educational Institute of Athens, Athens, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medical Science, University of Patras, Rion, Patras, Greece

  • Venue:
  • CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

In the present study, a new gridding method based on continuous wavelet transform (CWT) was performed. Line profiles of x and y axis were calculated, resulting to 2 different signals. These signals were independently processed by means of CWT at 15 different levels, using daubechies 4 mother wavelet. A summation, point by point, was performed on the processed signals, in order to suppress noise and enhance spot's differences. Additionally, a wavelet based hard thresholding filter was applied to each signal for the task of alleviating the noise of the signals. 14 real microarray images were used in order to visually assess the performance of our gridding method. Each microarray image contained 4 sub-arrays, each sub-array 40x40 spots, thus, 6400 spots totally. Moreover, these images contained contamination areas. According to our results, the accuracy of our algorithm was 98% in all 14 images and in all spots. Additionally, processing time was less than 3 sec on a 1024×1024×16 microarray image, rendering the method a promising technique for an efficient and fully automatic gridding processing.