Segmentation of DNA microarray images using an adaptive graph-based method

Karimi, Nader; Samavi, Shadrokh; Shirani, Shahram; Behnamfar, Parisa

doi:10.1049/iet-ipr.2008.0241

Cited by 13 publications

(10 citation statements)

References 8 publications

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“…HKM is the hybrid k-means approach also of Bozinov and Rahnenführer, which augments the KM approach with a mask that can be used to eliminate outliers [3]. AG is the adaptive graph method of Karimi et al [13] which uses minimum graph cuts to segment the region around a spot into foreground and background.…”

Section: Simulated Data Benchmarkmentioning

confidence: 99%

Segmenting Microarrays with Deep Neural Networks

Jones

2015

Preprint

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Microarray images consist of thousands of spots, each of which corresponds to a different biological material. The microarray segmentation problem is to work out which pixels belong to which spots, even in presence of noise and corruption. We propose a solution based on deep neural networks, which achieves excellent results both on simulated and experimental data. We have made the source code for our solution available on Github under a permissive license.

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Section: Simulated Data Benchmarkmentioning

confidence: 99%

Segmenting Microarrays with Deep Neural Networks

Jones

2015

Preprint

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“…This process is known as segmentation and is one of the most active research topics on the analysis of DNA microarray images [8]. Many different approaches have been used for this purpose, and it is possible to find clustering-based [29]- [33], threshold-based [34], graph-based [35] and even wavelet-based [36] proposals in the literature.…”

Section: A Analysis Pipelinementioning

confidence: 99%

A Distortion Metric for the Lossy Compression of DNA Microarray Images

Hernández-Cabronero

Sánchez

Marcellin

et al. 2013

2013 Data Compression Conference

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DNA microarrays are state-of-the-art tools in biological and medical research. In this work, we discuss the suitability of lossy compression for DNA microarray images and highlight the necessity for a distortion metric to assess the loss of relevant information. We also propose one possible metric that considers the basic image features employed by most DNA microarray analysis techniques. Experimental results indicate that the proposed metric can identify and differentiate important and unimportant changes in DNA microarray images.

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“…In 2008, Battiato et al proposed a neurofuzzy segmentation strategy based on a Kohonen self-organizing map and an ulterior fuzzy k-means classifier [22]. In 2010, Karimi et al described a new approach using an adaptive graph-based method [23]. Uslan et al in 2010 [24] and Li et al in 2011 [25] proposed two methods based on fuzzy c-means clustering.…”

Section: Segmentationmentioning

confidence: 99%

Standard and Specific Compression Techniques for DNA Microarray Images

et al. 2012

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We review the state of the art in DNA microarray image compression and provide original comparisons between standard and microarray-specific compression techniques that validate and expand previous work. First, we describe the most relevant approaches published in the literature and classify them according to the stage of the typical image compression process where each approach makes its contribution, and then we summarize the compression results reported for these microarray-specific image compression schemes. In a set of experiments conducted for this paper, we obtain new results for several popular image coding techniques that include the most recent coding standards. Prediction-based schemes CALIC and JPEG-LS are the best-performing standard compressors, but are improved upon by the best microarray-specific technique, Battiato’s CNN-based scheme

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Segmentation of DNA microarray images using an adaptive graph-based method

Cited by 13 publications

References 8 publications

Segmenting Microarrays with Deep Neural Networks

Segmenting Microarrays with Deep Neural Networks

A Distortion Metric for the Lossy Compression of DNA Microarray Images

Standard and Specific Compression Techniques for DNA Microarray Images

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