Adaptive techniques for microarray image analysis with related quality assessment

Journal of Electronic Imaging (Impact Factor: 0.85). 10/2007; 16:043013. DOI: 10.1117/1.2816445
Source: DBLP

ABSTRACT We propose novel techniques for microarray image analysis. In particular, we describe an overall pipeline able to solve the most common problems of microarray image analysis. We pro- pose the microarray image rotation algorithm (MIRA) and the statis- tical gridding pipeline (SGRIP) as two advanced modules devoted to restoring the original microarray grid orientation and to detecting, the correct geometrical information about each spot of input mi- croarray, respectively. Both solutions work by making use of statis- tical observations, obtaining adaptive and reliable information about each spot property. They improve the performance of the microarray image segmentation pipeline (MISP) we recently developed. MIRA, MISP, and SGRIP modules have been developed as plug-ins for an advanced framework for microarray image analysis. A new quality measure able to effectively evaluate the adaptive segmentation with respect to the fixed (e.g., nonadaptive) circle segmentation of each spot is proposed. Experiments confirm the effectiveness of the pro- posed techniques in terms of visual and numerical data. © 2007 SPIE

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, the proposed approach consists of mainly three important steps: preprocessing, gridding and segmentation of micro array images. Initially, the microarray image is preprocessed using filtering and morphological operators and it is given for gridding to fit a grid on the images using hill-climbing algorithm. Subsequently, the segmentation is carried out using the fuzzy c-means clustering. Initially the enhanced fuzzy c-means clustering algorithm (EFCMC) is implemented to effectively clustering the image whether the image may be affected by the noises or not. Then, the EFCM method was employed the real microarray images and noisy microarray images in order to investigate the efficiency of the segmentation. Finally, the segmentation efficiency of the proposed approach was compared with the various algorithms in terms of quality index and the obtained results ensures that the performance efficiency of the proposed algorithm was improved in term of quality index rather than other algorithms.
  • [Show abstract] [Hide abstract]
    ABSTRACT: DNA microarray technology yields expression profiles for thousands of genes, in a single hybridization experiment. The quantification of the expression level is performed using image analysis. In this paper we introduce a supervised method for the segmentation of microarray images using classification techniques. The method is able to characterize the pixels of the image as signal, background and artefact. The proposed method includes five steps: (a) an automated gridding method which provides a cell of the image for each spot. (b) Three multichannel vector filters are employed to preprocess the raw image. (c) Features are extracted from each pixel of the image. (d) The dimension of the feature set is reduced. (e) Support vector machines are used for the classification of pixels as signal, background, artefacts. The proposed method is evaluated using both real images from the Stanford microarray database and simulated images generated by a microarray data simulator. The signal and the background pixels, which are responsible for the quantification of the expression levels, are efficiently detected. A quality measure (qindex) and the pixel-by-pixel accuracy are used for the evaluation of the proposed method. The obtained qindex varies from 0.742 to 0.836. The obtained accuracy for the real images is about 98%, while the accuracies for the good, normal and bad quality simulated images are 96, 93 and 71%, respectively. The proposed classification method is compared to clustering-based techniques, which have been proposed for microarray image segmentation. This comparison shows that the classification-based method reports better results, improving the performance by up to 20%. The proposed method can be used for segmentation of microarray images with high accuracy, indicating that segmentation can be improved using classification instead of clustering. The proposed method is supervised and it can only be used when training data are available.
    Computers in biology and medicine 07/2013; 43(6):705-16. DOI:10.1016/j.compbiomed.2013.03.003 · 1.48 Impact Factor
  • Source