Adaptive Kuwahara filter

Bartyzel, Krzysztof

doi:10.1007/s11760-015-0791-3

Cited by 49 publications

(11 citation statements)

References 16 publications

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“…The pixel level classifier was trained using a total of 226 training features from TWS. The classifier was trained using a set of TWS training features which included: (i) Noise Reduction: The Kuwahara [37] and Bilateral filters [38] in the TWS toolkit were used to train the classifier on noise removal. These have been reported to be excellent filters for removing noise whilst preserving the edges [38], (ii) Edge Detection: A Sobel filter [39], Hessian matrix [40] and Gabor filter [41] were used for training the classifier on boundary detection in an image, and (iii) Texture filtering: The mean, variance, median, maximum, minimum and entropy filters were used for texture filtering.…”

Section: Methodsmentioning

confidence: 99%

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

et al. 2019

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BackgroundCervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images.MethodScene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm.ResultsThe evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%’, ‘97.64%, 98.08% and 97.16%’ and ‘95.00%, 100% and 90.00%’ were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab.ConclusionsThe major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.

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Section: Methodsmentioning

confidence: 99%

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

et al. 2019

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“…This stage involves (i) manual extraction of particles from the background (epoxy) with blow/lasso tool embedded in Fiji-ImageJ software, and (ii) elimination of the noise and other artifacts on particle images with the adaptive Kuwahara [20] and Median [21] filters on Fiji-ImageJ. Since x-ray imaging is capable to eliminate noise and other artifacts on the tomographic slices, crosssectional images were not processed before classification.…”

Section: Pre-processing the Imagesmentioning

confidence: 99%

“…The post-processing stage for the microscopic images included the application of the Kuwahara [20] and Median [21] filters to remove the residual noise and artifacts on the 2D maps. The final 2D map (Figure 8a) after the post-processing of an example image of a polished section (Figure 8b) was provided for a fair assessment of the classification accuracy on microscopic images.…”

Section: Post-processing the 2d Maps Of Particlesmentioning

confidence: 99%

Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map

Camalan

Çavur²

2021

Politeknik Dergisi

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“…To increase the accuracy of the next classification step, BSE images were preprocessed by removing the noise on them while exposing the boundaries between different objects (mineral surfaces and epoxy). This stage was achieved by successive application of the Kuwahara [36] and sharpen [37] filters to the BSE images. Kuwahara, an edge-preserving filter, was applied on 5 × 5 pixel-window subregions to remove the noise on the BSE images without blurring the boundaries between different image features.…”

Section: Experimental Materials and Proceduresmentioning

confidence: 99%

Development of a supervised classification method to construct 2D mineral mapson backscattered electron images

Camalan¹,

Çavur²

2020

Turk J Elec Eng & Comp Sci

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The Mineral Liberation Analyzer (MLA) can be used to obtain mineral maps from backscattered electron (BSE) images of particles. This paper proposes an alternative methodology that includes random forest classification, a prospective machine learning algorithm, to develop mineral maps from BSE images. The results show that the overall accuracy and kappa statistic of the proposed method are 97% and 0.94, respectively, proving that random forest classification is accurate. The accuracy indicators also suggest that the proposed method may be applied to classify minerals with similar appearances under BSE imaging. Meanwhile, random forest predicts fewer middling particles with binary and ternary composition, but the MLA predicts more middling particles only with ternary composition. These discrepancies may arise because the MLA, unlike random forest, may also measure the elemental compositions of mineral surfaces below the polished section.

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Adaptive Kuwahara filter

Cited by 49 publications

References 16 publications

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

Using Random Forest Tree Classification for Evaluating Vertical Cross-Sections in Epoxy Blocks to Get Unbiased Estimates for 3D Mineral Map

Development of a supervised classification method to construct 2D mineral mapson backscattered electron images

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