Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
IEEE Spectrum
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
cDNA microarray image processing using fuzzy vector filtering framework
Fuzzy Sets and Systems
Blind Microarray Gridding: A New Framework
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Gridline: automatic grid alignment DNA microarray scans
IEEE Transactions on Image Processing
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Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix^(R) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.