The Effect of Median Filtering on Edge Estimation and Detection

Bovik, Alan C.; Huang, Thomas S.; Munson, D.C.

doi:10.1109/tpami.1987.4767894

Cited by 182 publications

(46 citation statements)

References 9 publications

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“…Median filtering is a standard noise reduction technique. Furthermore, it is a reasonable preprocessing step for mass detection since it preserves the edge information of suspicious areas while reducing noise [32]. Median filters with different size kernels were explored (3×3, 5×5, 7×7, 9×9, 11×11, 15×15, 21×21 pixels) for the task.…”

Section: B Methodsmentioning

confidence: 99%

Evaluating the Effect of Image Preprocessing on an Information-Theoretic CAD System in Mammography

et al. 2008

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Rationale and Objectives-In our earlier studies we reported an evidence-based Computer Assisted Decision (CAD) system for location-specific interrogation of mammograms. A contentbased image retrieval framework with information theoretic (IT) similarity measures serves as the foundation for this system. Specifically, the normalized mutual information (NMI) was shown to be the most effective similarity measure for reduction of false positive marks generated by other, prescreening mass detection schemes. The objective of this work was to investigate the importance of image filtering as a possible preprocessing step in our IT-CAD system. Materials and Methods-Different filters were applied, each one aiming to compensate for known limitations of the NMI similarity measure. The study was based on a region-of-interest database that included true masses and false positive regions from digitized mammograms. Results-ReceiverOperating Characteristics (ROC) analysis showed that IT-CAD is affected slightly by image filtering. Modest, yet statistically significant performance gain was observed with median filtering (overall ROC area index Az improved from 0.78 to 0.82). However, Gabor filtering improved performance for the high sensitivity portion of the ROC curve where a typical false positive reduction scheme should operate (partial ROC area index 0.90 A z improved from 0.33 to 0.37). Fusion of IT-CAD decisions from different filtering schemes markedly improved performance (A z =0.90 and 0.90 A z =0.55). At 95% sensitivity, the system's specificity improved by 36.6%.Conclusion-Additional improvement in false positive reduction can be achieved by incorporating image filtering as a preprocessing step in our information-theoretic CAD system.

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Section: B Methodsmentioning

confidence: 99%

Evaluating the Effect of Image Preprocessing on an Information-Theoretic CAD System in Mammography

et al. 2008

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“…To remove these random artifacts and to determine the SNP haplotype blocks within a cell, we designed a 1D median filter (1D-MF) that walks across the raw single-cell haplotypes for the informative SNPs genome wide and considers the raw haplotype state from multiple informative SNPs in a variable window (W k , see below). Because 1D median filters preserve edges while removing noise, [27][28][29] the locations of the homologous recombination sites in the reconstructed haplotypes of the cell are preserved.…”

Section: Module 2: Single-cell Haplarithmisismentioning

confidence: 99%

“…Considering that these WGA artifacts occur largely random ( Figures S5A and S5B), this is a genome-wide problem that prevents us from pinning down the positions of genuine genetic crossovers on the inherited homologs in the cell and as such also to accurately impute genetic mutations entrapped in a haplotype block. To address this problem, we developed a computational workflow, termed siCHILD ( Figure S1), that integrates (1) haplarithmisis (Figures 3B, 3E, 3F, and S6) with (2) the segmentation of phased single-cell discrete SNP genotypes into haplotypes by one-dimensional median filters (1D-MF), which remove noise but preserve boundaries [27][28][29] ( Figures 3A, 3C, 3D, and S6, Material and Methods).…”

Section: Single-cell Haplotyping Based On Discrete and Continuous Snpmentioning

confidence: 99%

Concurrent Whole-Genome Haplotyping and Copy-Number Profiling of Single Cells

Esteki

Dimitriadou

Mateiu

et al. 2015

The American Journal of Human Genetics

116

104

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“…The edges of the cube in Figure 1.3 are very sharp but they suffer from edge jitter; the final image is not a clean square. Edge jitter in median filtering has been studied in [24], and [140]. When images are filtered with a median filter, the phenomenon known as streaking also arises [22].…”

Section: Theoretical Analysis Of Median Filtersmentioning

confidence: 99%

An overview of median and stack filtering

Coyle

Gallagher

Gabbouj

1992

Circuits Systems and Signal Process

108

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Abstract. Within the last two decades a small group of researchers has built a useful, nontrivial theory of nonlinear signal processing around the median-related filters known as rank-order filters, order-statistic filters, weighted median filters, and stack filters. This required significant effort to overcome the bias, both in education and research, toward linear theory, which has been dominant since the days of Fourier, Laplace, and "Convolute."We trace the development of this theory of nonlinear filtering from its beginnings in the study of noise-removal properties and structural behavior of the median filter to the recently developed theory of optimal stack filtering.The theory of stack filtering provides a point of view which unifies many different filter classes, including morphological filters, so it is discussed in detail. Of particular importance is the way this theory has brought together, in a single analytical framework, both the estimation-based and the structural-based approaches to the design of these filters.Some recent applications of median and stack filters are provided to demonstrate the effectiveness of this approach to nonlinear filtering. They include: the design of an optimal stack filter for image restoration; the use of vector median filters to attenuate impulsive noise in color images and to eliminate cross luminance and cross color in TV images; and the use of median-based filters for image sequence coding, reconstruction, and scan rate conversion in normal TV and HDTV systems.

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The Effect of Median Filtering on Edge Estimation and Detection

Cited by 182 publications

References 9 publications

Evaluating the Effect of Image Preprocessing on an Information-Theoretic CAD System in Mammography

Evaluating the Effect of Image Preprocessing on an Information-Theoretic CAD System in Mammography

Concurrent Whole-Genome Haplotyping and Copy-Number Profiling of Single Cells

An overview of median and stack filtering

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