Adaptive fractional differential approach and its application to medical image enhancement

Li, Bo; Xie, Wei

doi:10.1016/j.compeleceng.2015.02.013

Cited by 120 publications

(69 citation statements)

References 18 publications

(6 reference statements)

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“…It could be enhanced blob‐ and tubular‐like structures in two‐dimensional images. Li and Xie presented a medical image enhancement algorithm that adjusted the fractional order according to the dynamic gradient feature of the image while preserving smooth areas and weak textures. Shanthakumar and Kumar applied histogram equalization to enhance the brain images.…”

Section: Introductionmentioning

confidence: 99%

Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

Kuo

2019

Int J Imaging Syst Tech

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In order to enhance the pathological features of medical images and aid the medical diagnosis, the image enhancement is a necessary process. This study presented the Gaussian probability model combining with bi‐histogram equalization to enhance the contrast of pathological features in medical images. There are five different bi‐histogram equalizations, namely, bi‐histogram equalization (BBHE), dualistic sub‐image histogram equalization (DSIHE), bi‐histogram equalization with a plateau limit (BHEPL), bi‐histogram equalization median plateau limit (BHEPL‐D), and bi‐histogram equalization with modified histogram bins (BHEMHB). The entropy, contrast, absolute mean brightness error (AMBE), and skewness difference are used to quantize the enhancement results. From the experimental result, it is observed that the entropy and contrast of the images can be effectively enhanced by using Gaussian probability bi‐histogram equalizations, and the Gaussian probability bi‐histogram equalization median plateau limit (GPBHEPL‐D) has the best enhanced result. The proposed GPBHEPL‐D method is effective in strengthening the pathological features in medical images, so as to increase the efficiency of doctors' diagnoses and computer‐aided detection.

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Section: Introductionmentioning

confidence: 99%

Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

Kuo

2019

Int J Imaging Syst Tech

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“…Fractional differential could preserve image details and keep the source image histogram envelope [24]. Fractional differential operator has weak derivative nature, namely fractional differential operator can enhance the high frequency signal components, while low-frequency components of a signal are nonlinear reserved [25].…”

Section: Fractional Image Enhancementmentioning

confidence: 99%

Infrared Polarization and Intensity Image Fusion Algorithm Based on Visual Brightness and Contrast Response Function

Zhang¹,

Yang²,

Ji³

et al. 2017

dtetr

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Abstract. In this paper, a novel fusion algorithm is proposed based on visual brightness and contrast response function, and it can better retain the difference features between infrared and polarization images to improve the visual effect of fused image. Firstly, the source images are enhanced by fractional differentia operator to improve the edge, texture, and brightness. Second, the low frequency and high frequency features of enhanced infrared polarization and intensity image are described by local mean and local skewness. Thirdly, the visual brightness and contrast response function are constructed by using local mean and local skewness based on human visual model. Next, the source images are fused by using the visual brightness and contrast response function, and then two kinds of fused images that are respectively are the low frequency feature fused image and detail feature fused image are gotten. Finally, the fused image is gotten by superposition of the low frequency feature fused image and detail feature fused image. The experiment shows that the result obtained by proposed algorithm performs better in both subjective and objective qualities.

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“…Fractional calculus is a theory branch of mathematics and generalizes the classic integer derivative to arbitrary (noninteger) order [31,32]. Fractional derivative has different definitions, that is, Grümwald-Letnikov (G-L), Riemann-Liouville (R-L), and Capotu.…”

Section: Spectral Processing and Datamentioning

confidence: 99%

“…The real sample information was inevitably contaminated by the instrument noise [31,32]. In order to reduce the noise, the ViewSpecPro software version 6.0 was applied to correct and eliminate the breakpoints and remove the marginal wavebands with large noise (350~400 nm and 2401~2500 nm).…”

Section: Spectral Processing and Datamentioning

confidence: 99%

Quantitative Estimation of Organic Matter Content in Arid Soil Using Vis-NIR Spectroscopy Preprocessed by Fractional Derivative

Wang¹,

Tiyip²,

Ding³

et al. 2017

Journal of Spectroscopy

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Soil organic matter (SOM) content is an important index to measure the level of soil function and soil quality. However, conventional studies on estimation of SOM content concerned about the classic integer derivative of spectral data, while the fractional derivative information was ignored. In this research, a total of 103 soil samples were collected in the Ebinur Lake basin, Xinjiang Uighur Autonomous Region, China. After measuring the Vis-NIR (visible and near-infrared) spectroscopy and SOM content indoor, the raw reflectance and absorbance were treated by fractional derivative from 0 to 2nd order (order interval 0.2). Partial least squares regression (PLSR) was applied for model calibration, and five commonly used precision indices were used to assess the performance of these 22 models. The results showed that with the rise of order, these parameters showed the increasing or decreasing trends with vibration and reached the optimal values at the fractional order. A most robust model was calibrated based on 1.8 order derivative of R, with the lowest RMSEC (3.35 g kg

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Adaptive fractional differential approach and its application to medical image enhancement

Cited by 120 publications

References 18 publications

Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

Gaussian probability bi‐histogram equalization for enhancement of the pathological features in medical images

Infrared Polarization and Intensity Image Fusion Algorithm Based on Visual Brightness and Contrast Response Function

Quantitative Estimation of Organic Matter Content in Arid Soil Using Vis-NIR Spectroscopy Preprocessed by Fractional Derivative

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