cDNA Microarray Basics
cDNA Microarray Imaging
cDNA Image Noise
cDNA Characteristics
cDNA Image Denoising
cDNA Data Normalization
cDNA Spot Localization
cDNA Spot Segmentation


cDNA Data Normalization

Since the noisy samples deviate from other samples in a given data population, the normalization operation should minimize systematic variations (attributed to noise) in the cDNA image measurements, enhance spot localization, and emphasize biological differences between the experimental and control population. To achieve the objective and simultaneously preserve the structural content of the image, local vector filtering operators should be used. These multichannel data-normalizing oprators are designed to replace the corrupted cDNA vectorial input most centrally located in a finite area of support with the vector which is statistically closest to all members within the area of support.


Real cDNA image

 Using robust vector order-statistics, the ordering of the aggregated distances or similarity functions calculated between cDNA vectors located inside the processing window identifies the outlying observations and allows for the smoothing of the cDNA vectors' population.

Normalized cDNA image


Magnitude characteristics of the real cDNA image


Magnitude characteristics of the normalized image


Directional characteristics of the real cDNA image


Directional characteristics of the normalized image


The cDNA vector minimizing the aggregated distance or maximizing the vector similarity criterion to other samples within the processing window is the most representative to the whole windowed set. Therefore, the choice of the sample associated with the minimum aggregated distance value is critical for the proper normalization of the image data. Using vector filtering operators which are selective in nature and use the minimization principle to determine the output, the noisy samples do not contribute to the filter output. This makes the cDNA data normalization approach robust to microarray image noise while it allows for preserving the structural content of the acquired cDNA image. The normalized cDNA image data should exhibit uniformity in the characteristics of the cDNA vectors. The same feature can be used as the base for microarray spot localization and image segmentation.


bulletR. Lukac and K.N. Plataniotis, "cDNA Microarray Image Segmentation Using Root Signals,"  International Journal of Imaging Systems and Technology, vol. 16, no. 2, pp. 51-64, April 2006.
bulletR. Lukac, K.N. Plataniotis, B. Smolka, and A.N. Venetsanopoulos, "cDNA Microarray Image Processing Using Fuzzy Vector Filtering Framework," Fuzzy Sets and Systems, Special Issue on Fuzzy Sets and Systems in Bioinformatics, vol. 152, no. 1, pp.17-35, May 2005.
bulletR. Lukac, B. Smolka, K. Martin, K.N. Plataniotis, and A.N. Venetsanopoulos, "Vector Filtering for Color Imaging," IEEE Signal Processing Magazine, Special Issue on Color Image Processing, vol. 22, no. 1, pp. 74-86, January 2005.
bulletR. Lukac, K.N. Plataniotis, B. Smolka, and A.N. Venetsanopoulos, "A Multichannel Order-Statistic Technique for cDNA Microarray Image Processing," IEEE Transactions on NanoBioscience, vol. 3, no. 4, pp. 272-285, December 2004.

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Last update: 10/15/06

2006 Rastislav Lukac