Adaptive techniques for microarray image analysis with related quality assessment.

Journal of Electronic Imaging (Impact Factor: 1.06). 01/2007; 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: Simple kidney cysts analysis from CT images is nowadays performed in a direct visual and hardly reproducible way. Computer-aided measurements of simple kidney cysts from CT images may help radiologists to accomplish an objective analysis of the clinical cases under observation. We propose a semi-automatic segmentation algorithm for this task. Experiments performed on real datasets confirm the effectiveness and usefulness of the proposed method.
    Medical Measurements and Applications, 2009. MeMeA 2009. IEEE International Workshop on; 06/2009
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The DNA microarray images allow to analyze the natural gene expressions. In this paper we propose an advanced method to efficiently address the imaging storage as well as the performance of the algorithm used to retrieve information from DNA images. The cellular neural networks (CNNs) based core is able to provide a method to extract foreground (the DNA gene expression information) from DNA images. It is also proposed an innovative method to compress the DNA image by re-organizing the signal data belonging to the background by making use of a novel way to apply the re-indexing techniques to almost ¿uncorrelated¿ signal. Experiments confirm how the proposed method outperform previous solution in almost all cases.
    Image Processing (ICIP), 2009 16th IEEE International Conference on; 12/2009
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we propose a novel microarray segmentation strategy to separate background and foreground signals in microarray images making use of a neurofuzzy processing pipeline. In particular a Kohonen Self Organizing Map followed by a Fuzzy K-Mean classifier are employed to properly manage critical cases like saturated spot and spike noise. To speed up the overall process a Hilbert sampling is performed together with an ad-hoc analysis of statistical distribution of signals. Experiments confirm the validity of the proposed technique both in terms of measured and visual inspection quality.
    Pattern Recognition, 2008. ICPR 2008. 19th International Conference on; 01/2009

Full-text (2 Sources)

Available from
May 22, 2014