Fractal Dimension Estimation for Developing Pathological Brain Detection System Based on Minkowski-Bouligand Method

Zhang, Yudong; Chen, Xianqing; Zhan, Tianming; Jiao, Zhuqing; Sun, Yi; Chen, Zhimin; Yao, Yu; Fang, Lanting; Lv, Yiding; Wang, Shuihua

doi:10.1109/access.2016.2611530

Cited by 69 publications

(24 citation statements)

References 72 publications

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“…Fractal characteristics mainly refer to the self-similarity of objective things, which is embodied in fractal dimension, fractal error and multifractal index. In recent years, fractal features have been widely applied in texture analysis [36,37], Image sampling [38,39] and segmentation of medical signals (one-dimensional (1D), two-dimensional (2D), or three-dimensional (3D)) [40,41]. The study shows that most of the natural objects in nature have strong fractal characteristics, which can be consistent with the fractal model.…”

Section: Introductionmentioning

confidence: 70%

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Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

et al. 2020

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In earth observation systems, especially in the detection of small and weak targets, the detection and recognition of long-distance infrared targets plays a vital role in the military and civil fields. However, there are a large number of high radiation areas on the earth’s surface, in which cirrus clouds, as high radiation areas or abnormal objects, will interfere with the military early warning system. In order to improve the performance of the system and the accuracy of small target detection, the method proposed in this paper uses the suppression of the cirrus cloud as an auxiliary means of small target detection. An infrared image was modeled and decomposed into thin parts such as the cirrus cloud, noise and clutter, and low-order background parts. In order to describe the cirrus cloud more accurately, robust principal component analysis (RPCA) was used to get the sparse components of the cirrus cloud, and only the sparse components of infrared image were studied. The texture of the cirrus cloud was found to have fractal characteristics, and a random fractal based infrared image signal component dictionary was constructed. The k-cluster singular value decomposition (KSVD) dictionary was used to train the sparse representation of sparse components to detect cirrus clouds. Through the simulation test, it was found that the algorithm proposed in this paper performed better on the the receiver operating characteristic (ROC) curve and Precision-Recall (PR) curve, had higher accuracy rate under the same recall rate, and its F-measure value and Intersection-over-Union (IOU) value were greater than other algorithms, which shows that it has better detection effect.

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Section: Introductionmentioning

confidence: 70%

“…The key problem is to find the appropriate s value. In this paper, the s value was set to 8,15,20,30,40,45 to find the best s value.…”

Section: Parameter Settingsmentioning

confidence: 99%

Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

et al. 2020

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“…Drawn the ln e –ln N ( e ) graph by using a series of N ( e ) and e data to get a straight line by origin 8 data processing software. Herein, there was a relationship between N ( e ) and e :

\ln N ((), e) = - D \ln e

where D is the fractal dimension of the graph. As shown in Figure , the fractal dimension of the graph is 1.117.…”

Section: Methodsmentioning

confidence: 99%

Low fractal dimension modified drilling‐hole wall for PTFE high‐frequency board copper plating with plasma treatment

Zhou

Gou

Jin

et al. 2019

J of Applied Polymer Sci

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The development of fifth generation communication has raised the demand of advanced polytetrafluoroethylene (PTFE) board in high-frequency signal transmission. Drilling holes are fabricated on PTFE substrate for subsequent copper plating to achieve interlayer electrical connection between the thin dielectric layers. In this article, we propose O 2 /CF 4 plasma treatment to clean the after-drilling PTFE hole, achieving highly activated surface for following copper plating. The plating results, in contrast with control experiments which were performed without plasma treatment, demonstrate multiple advantages of O 2 /CF 4 plasma treatment including qualified copper plating, excellent thermal shock reliability, and improved wettability. X-ray photoelectron spectroscopy analysis for plasma-treated samples confirms the emergence of hydrophilic groups such C O and COO/COF on PTFE hole wall. In addition, optimized parameters with particle gas flow combination and process time were obtained through parameter sweeping. The best qualified sample was associated with the lowest roughness (fractal dimension) of PTFE hole wall, indicating its promising application in high-frequency signal transmission in PTFE board.

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“…Fractal-based pattern recognition has been paid increasing attention, because it can effectively deal with feature information in a complex electromagnetic environment. One of the first studies of fractal theory [15,16] was conducted by B.B. Mandelbrot in 1975, as a branch of modern mathematical theory.…”

Section: Related Workmentioning

confidence: 99%

Signal Pattern Recognition Based on Fractal Features and Machine Learning

Shi

2018

Applied Sciences

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As a typical pattern recognition method, communication signal modulation involves many complicated factors. Fractal theory can be used for signal modulation feature extraction and recognition because of its good ability to express complex information. In this paper, we conduct a systematic research study by using the fractal dimension as the feature of modulation signals. Box fractal dimension, Katz fractal dimension, Higuchi fractal dimension, Petrosian fractal dimension, and Sevcik fractal dimension are extracted from eight different modulation signals for signal pattern recognition. Meanwhile, the anti-noise function, box-diagram, and running time are used to evaluate the noise robustness, separability, and computational complexity of five different fractal features. Finally, Bback-Propagation (BP) neural network, grey relation analysis, random forest, and K-nearest neighbor are proposed to classify the different modulation signals based on these fractal features. The confusion matrices and recognition results are provided in the experimental section. They indicate that random forest had a better recognition performance, which could reach 96% in 10 dB.

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Fractal Dimension Estimation for Developing Pathological Brain Detection System Based on Minkowski-Bouligand Method

Cited by 69 publications

References 72 publications

Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

Cirrus Detection Based on RPCA and Fractal Dictionary Learning in Infrared imagery

Low fractal dimension modified drilling‐hole wall for PTFE high‐frequency board copper plating with plasma treatment

Signal Pattern Recognition Based on Fractal Features and Machine Learning

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