A new method for segmentation of microscopic images on activated sludge

Boztoprak, Halime; Özbay, Yüksel

doi:10.3906/elk-1307-9

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…It is usually used with wavelet transform in image fusion studies [26,27]. In [28], wavelet transform and spatial frequency was used to calculate iterative value of cellular neural network. The spatial frequency needs to be decreased if the images are blurred more.…”

Section: Proposed Methodsmentioning

confidence: 99%

An alternative image quality assessment method for blurred images

Boztoprak¹

2016

Balkan Journal of Electrical and Computer Engineering

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This study proposes a no-reference image quality assessment method for blurred images. In this approach, first, a discrete wavelet transform was applied to the sample images and then the results were decomposed into four subbands. This was followed by the calculation of the spatial frequencies of high-high (HH2) and low-low (LL2) subbands. Then the ratio of spatial frequencies of HH2 and LL2 subbands was calculated. Information about the image quality was obtained by using this ratio, with lower values indicative of better image quality. The study aims to investigate whether the proposed method is capable of measuring the image quality. The proposed technique was tested on the standard images. Three different images were used, of which each one was distorted with the same type and amount of noise. Motion noise, blurring and sharpening was applied to distort the images. The performance of the proposed method was evaluated and compared with eight representative image quality measures. This provides a meaningful comparison across different types of image distortions. Then, the cameraman image was also blurred with two different noises: Gaussian and disk-shaped blur. The varying amount of blur was compared with Universal Image Quality Index (UIQI) values of the cameraman image. The method gives good results in different resolutions as well. Its computation is easy, independent of viewing conditions.

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Section: Proposed Methodsmentioning

confidence: 99%

An alternative image quality assessment method for blurred images

Boztoprak¹

2016

Balkan Journal of Electrical and Computer Engineering

Self Cite

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Segmentation of wood cell in cross-section using deep convolutional neural networks

Ergun

2021

IFS

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Fiber and vessel structures located in the cross-section are anatomical features that play an important role in identifying tree species. In order to determine the microscopic anatomical structure of these cell types, each cell must be accurately segmented. In this study, a segmentation method is proposed for wood cell images based on deep convolutional neural networks. The network, which was developed by combining two-stage CNN structures, was trained using the Adam optimization algorithm. For evaluation, the method was compared with SegNet and U-Net architectures, trained with the same dataset. The losses in these models trained were compared using IoU (Intersection over Union), accuracy, and BF-score measurements on the test data. The automatic identification of the cells in the wood images obtained using a microscope will provide a fast, inexpensive, and reliable tool for those working in this field.

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A new method for segmentation of microscopic images on activated sludge

Cited by 2 publications

References 15 publications

An alternative image quality assessment method for blurred images

An alternative image quality assessment method for blurred images

Segmentation of wood cell in cross-section using deep convolutional neural networks

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