A fractional-order edge detection operator for medical image structure feature extraction

Tian, Donghai; Wu, Jingfei; Yang, Yajie

doi:10.1109/ccdc.2014.6853103

Cited by 18 publications

(6 citation statements)

References 9 publications

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“…This lack can be overcome by using the Fractional-Order Sobel Mask method in determining the value of the threshold gradient (𝑘) parameter. Fractional-Order Sobel Mask in edge detection can consider more information from neighbor pixel than the original Sobel Mask [18]. Based on this, the 𝑘 value determined using the Fractional-Order Sobel Mask is more accurate than the 𝑘 value determined using the original Sobel Mask.…”

Section: Noise Reductionmentioning

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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Section: Noise Reductionmentioning

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 addition to all the previous applications, the fractional-order edge detection masks exhibited a higher hand in the medical imaging field in the literature [33]. The image processing of medical images in the fractional order domain was also tackled in [34], while in [35], a genetic algorithm was used to optimize the fractional order edge detection to diagnose Alzheimer disease (AD).…”

Section: Survey On Fractional-order Image Edge Detectionmentioning

confidence: 99%

Fractional-Order Edge Detection Masks for Diabetic Retinopathy Diagnosis as a Case Study

et al. 2021

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Edge detection is one of the main steps in the image processing field, especially in biomedical imaging, to diagnose a disease or trace its progress. The transfer of medical images makes them more susceptible to quality degradation due to any imposed noise. Hence, the protection of this data against noise is a persistent need. The efficiency of fractional-order filters to detect fine details and their high noise robustness, unlike the integer-order filters, it renders them an attractive solution for biomedical edge detection. In this work, two novel central fractional-order masks are proposed with their detailed mathematical proofs. The fractional-order parameter gives an extra degree of freedom in designing different masks. The noise performance of the proposed masks is evaluated upon applying Salt and Pepper noise and Gaussian noise. Numerical results proved that the proposed masks outperform the integer-order masks regarding both types of noise, achieving higher Peak Signal to Noise Ratio. As a practical application, the proposed fractional-order edge detection masks are employed to enhance the Diabetic Retinopathy disease diagnosis.

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“…Their application to image processing began in 2002 with the CRONE method [10]. Since then, a few already extant image treatment methods have been adapted to include fractional derivatives (instead of usual, integer order derivatives), such as the fractional Roberts operator [11], the fractional Sobel method [12,13], the fractional Canny method [6], or the fractional Laplacian of Gaussian method [14]. These methods will be addressed below in Section 2 and details can be found in [5].…”

Section: Previous Work On Fractional Image Processingmentioning

confidence: 99%

“…. 2021, 1, 1 2 of 20 derivatives (instead of usual, integer order derivatives), such as the fractional Roberts operator [11], the fractional Sobel method [12,13], the fractional Canny method [6], or the fractional Laplacian of Gaussian method [14]. These methods will be addressed below in section 2 and details can be found in [5].…”

Section: Satellite Image Datamentioning

confidence: 99%

Fractional Order Processing of Satellite Images

2021

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Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using conventional techniques in satellite images. It also shows that it is possible to find a fixed set of parameters, allowing automatic detection while maintaining high performance.

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A fractional-order edge detection operator for medical image structure feature extraction

Cited by 18 publications

References 9 publications

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

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

Fractional-Order Edge Detection Masks for Diabetic Retinopathy Diagnosis as a Case Study

Fractional Order Processing of Satellite Images

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