Novel PCA-based color-to-gray image conversion

Seo, Ja-Won; Kim, Seong Dae

doi:10.1109/icip.2013.6738470

Cited by 20 publications

(12 citation statements)

References 6 publications

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“…Using kernel CCA, genes that greatly contribute to the correlations of TFs and TGs gain greater weights in canonical embeddings and TF–TG pair that both TF and TG show high weights should be remarked as valid pair. Inspired by Seo and Kim ( 2013 ), we weighted every k dimension with the corresponding eigenvalue so that the eigenvalue-weighted embeddings is dominated by the leading eigenvectors. For every possible TF–TG pair retrieved from public GRN, we next computed dot-product similarity of TF and TG embeddings to define an edge weight of the pair.…”

Section: Methodsmentioning

confidence: 99%

Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis

et al. 2021

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Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF–TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF–TG relations where a module of TF is regulating a module of TGs upon specific stress.

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Section: Methodsmentioning

confidence: 99%

Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis

et al. 2021

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“…Content may change prior to final publication. the significant variations among the image pixels using the scores of the principal component that represents the maximum variance direction through the given pixels [38], [39]. Furthermore, PCA adds robustness against the non-uniform illumination that is common in microscopy [40], [41].…”

Section: B Phase 2: Final Wbc Detection Based On Wavelet-based Thresholding Segmentationmentioning

confidence: 99%

A Cascaded Classification-Segmentation Reversible System for Computer-Aided Detection and Cells Counting in Microscopic Peripheral Blood Smear Basophils and Eosinophils Images

2021

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Computer-aided image analysis has a pivotal role in automated counting and classification of white blood cells (WBCs) in peripheral blood images. Due to their different characteristics, our proposed approach is based on investigating the variations between the basophils and eosinophils in terms of their color histogram, size, and shape before performing the segmentation process. Accordingly, we proposed a cascaded system using a classification-based segmentation process, called classification-segmentation reversible system (CSRS). Prior to applying the CSRS system, a Histogram-based Object to Background Disparity (HOBD) metric was deduced to determine the most appropriate color plane for performing the initial WBC detection (first segmentation). Investigating the local histogram features of both classes resulted in a 92.4% initial classification accuracy using the third-degree polynomial support vector machine (SVM) method. Subsequently, in the proposed CSRS approach, transformation-based segmentation algorithms were developed to fit the specific requirements of each of the two predicted classes. The proposed CSRS system is used, where the images from an initial classification process are fed into a second segmentation process for each class separately. The segmentation results demonstrated a similarity index of 94.9% for basophils, and 94.1% for eosinophils. Moreover, an average counting accuracy of 97.4% for both classes was achieved. In addition, a second classification was carried out after applying the CSRS, achieving a 5.2% increase in accuracy compared to the initial classification process.

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“…Decolorization aims to preserve as much information (structural features and chrominance contrast) of the original color image as possible. Many decolorization methods 2 – 27 have been proposed to transform color images into grayscale images. They can be classified into two categories: traditional methods (global mapping, 2 – 16 local mapping, 17 – 20 and hybrid methods 21 – 23 ) and deep learning methods 24 …”

Section: Introductionmentioning

confidence: 99%

“…Many decolorization methods 2 – 27 have been proposed to transform color images into grayscale images. They can be classified into two categories: traditional methods (global mapping, 2 – 16 local mapping, 17 – 20 and hybrid methods 21 – 23 ) and deep learning methods 24 – 27 …”

Section: Introductionmentioning

confidence: 99%

“…The grayscale value g of any color pixel is a linear combination of InormalR, IG, and InormalB (values of R, G, and B channels), g=αnormalRInormalR+αnormalGInormalG+αnormalBInormalB with fixed weights (αnormalR,αnormalG,αnormalB)=(0.2989,0.5870,0.1140) for all color images to obtain their grayscale results. Some linear global mapping methods 4 – 7 , 15 are similar to rgb2gray(), e.g., Nafchi et al 6 . present a low run-time global mapping method for color-to-gray conversion by estimating the three global linear weighting coefficients.…”

Section: Introductionmentioning

confidence: 99%

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Decolorization based on the weighted combination of image entropy and canny edge retention ratio

Zhang

Wan

2023

J. Electron. Imag.

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.Image decolorization is used to realize the color-to-gray conversion, which is widely used in black-and-white printing, digital-ink display, photo stylization, etc. Image contrast fidelity preservation is the key issue during this conversion process. We present a parametric model combining image entropy and the Canny edge retention ratio (CERR). Maximizing the image entropy keeps the global image contrast content, while maximizing the CERR enhances the local image contrast, mainly along the edges. The weighting parameter λ can be robustly estimated. Experimental results on Cadik’s and the COLOR250 datasets demonstrate that the model can achieve significant performance improvement over state-of-the-art methods both qualitatively and quantitatively.

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Novel PCA-based color-to-gray image conversion

Cited by 20 publications

References 6 publications

Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis

Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis

A Cascaded Classification-Segmentation Reversible System for Computer-Aided Detection and Cells Counting in Microscopic Peripheral Blood Smear Basophils and Eosinophils Images

Decolorization based on the weighted combination of image entropy and canny edge retention ratio

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