Counting of Bacterial Colonies of the Low Quality Image Using Perona-Malik Diffusion Filters and Image Morphology Operators

Hamdani, Ibnu Mansyur; Anam, Syaiful; Shofianah, Nur

doi:10.1088/1742-6596/1490/1/012058

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…Prinsip dari pendeteksian tepi dari Sobel Mask adalah mendeteksi tepian dari arah horizontal (𝑇 ! ) dan arah vertikal (𝑇 " ) dari gradien citra [8]. Mask kernel dari kedua arah tersebut sebagai berikut:…”

Section: Sobel Maskunclassified

Pendeteksian Tepi Citra Digital Berwarna Menggunakan Sobel Mask Yang Ditingkatkan dan Median Filter

Hamdani,

Adhalia Hamdani

2021

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Edge detection is a fundamentally important thing to do in its application to digital images. This is because edge detection can produce the edges of an image in a short time. One of the methods commonly used in edge detection is the Sobel Mask. However, Sobel Mask only convoluted in horizontal and vertical directions. Besides that, Sobel Mask is also sensitive to noise. Therefore, the Sobel Mask is improved by adding a new kernel mask and combined with the Median filter. The new kernel mask is obtained by rotating the horizontal and vertical kernel masks by 45 degrees. In addition, edge detection is carried out on RGB images which are generally carried out on grayscale images. The improved Sobel Mask provides more detailed edge detection with firmer lines compared to the regular Sobel Mask.

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Section: Sobel Maskunclassified

Pendeteksian Tepi Citra Digital Berwarna Menggunakan Sobel Mask Yang Ditingkatkan dan Median Filter

Hamdani,

Adhalia Hamdani

2021

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“…For example, the bacterial colonies counting in medicine can be used to estimate the level of infection experienced by a patient [2]. The studies about counting bacterial colony had been done before where counting bacterial colony consumes times and a tedious task [2], [3], [4], [5]. Bacterial colony counting is carried out by creating a growth chart to estimate the number of cells.…”

Section: Introductionmentioning

confidence: 99%

Counting Bacterial Colony and Reducing noise on Low-Quality Image Using Modified Perona-Malik Diffusion Filter with Sobel Mask Fractional Order

Hamdani¹,

Anam

Shofianah

et al. 2023

SISFOKOM

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In the field of microbiology, the counting of bacterial colonies is fundamental and mandatory. This is done to estimate the number of bacterial cells in every 1 milliliter or gram of sample. The counting takes a long time and is tedious, so it requires an accurate and fast counting method. The image quality used is very low and contains noise. Therefore, a preprocessing method is needed to reduce the noise. The Perona-Malik filter method is known to be able to remove noise well. However, it is difficult to determine the appropriate gradient threshold parameter ( ) for each different image. To find the appropriate value of , the original Sobel Mask method and Sobel Mask Fractional-Order are used to estimate the value of . The experimental results show the results of noise reduction using PMD with a value of from the original Sobel Mask and Sobel Mask Fractional-Order. The results of the accuracy of determining the value of k with the Sobel Mask Fractional-Order (α=1.0) show higher results based on the F-Measure values for samples 1, 2, and 3 respectively 97%, 98%, and 90%.

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“…Among the PDE denoising methods [6], the Perona-Malik model, the focus of present scientific research, is one of the most classic nonlinear anisotropic diffusion filtering models, in which the principle and calculation are relatively simple. At present, some scholars have created a lot of algorithms based on the Perona-Malik model [12][13][14][15][16][17][18][19]. And the application of these algorithms in image processing and other fields has achieved incomparable effects with classical algorithms.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Vibration Signal Denoising Based on Adaptive Morphological Wavelet Perona–Malik Filter Algorithm

Tan

Yun

2021

Shock and Vibration

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This paper proposes an adaptive Perona–Malik filtering algorithm based on the morphological Haar wavelet, which is used for vibration signal denoising in rolling bearing fault diagnosis with strong noise. First, the morphological Haar wavelet operator is utilized to presmooth the noisy signal, and the gradient of the presmooth signal is estimated. Second, considering the uncertainty of gradient at the strong noise point, a strong noise point recognition operator is constructed to adaptively identify the strong noise point. Third, the two-step gradient average value of the strong noise point in the same direction is used to substitute, and the second derivative is introduced into the diffusion coefficient. Finally, diffusion filtering is performed based on the improved Perona–Malik model. The simulation experiment result indicated that not only the algorithm can denoise effectively, but also the average gradient and second derivative in the same direction can effectively suppress the back diffusion of strong noise points to improve the denoising signal-to-noise ratio. The experimental results of rolling bearing show that the algorithm can adaptively filter out strong noise points and keep the information of peak in the signal well, which can improve the accuracy of rolling bearing fault diagnosis.

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Counting of Bacterial Colonies of the Low Quality Image Using Perona-Malik Diffusion Filters and Image Morphology Operators

Cited by 3 publications

References 10 publications

Pendeteksian Tepi Citra Digital Berwarna Menggunakan Sobel Mask Yang Ditingkatkan dan Median Filter

Pendeteksian Tepi Citra Digital Berwarna Menggunakan Sobel Mask Yang Ditingkatkan dan Median Filter

Counting Bacterial Colony and Reducing noise on Low-Quality Image Using Modified Perona-Malik Diffusion Filter with Sobel Mask Fractional Order

Rolling Bearing Fault Vibration Signal Denoising Based on Adaptive Morphological Wavelet Perona–Malik Filter Algorithm

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