Digital image watermarking method based on DCT and fractal encoding

Liu, Shuai; Zheng, Pan; Song, Houbing

doi:10.1049/iet-ipr.2016.0862

Cited by 132 publications

(60 citation statements)

References 40 publications

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“…The background error percentage of the proposed algorithm is 5.38% and 2.56% lower than that of C4.5 algorithm and SVM-DS algorithm,23,24 respectively. The correct target percentage of the proposed algorithm is 16.56% and 15.81% higher than the C4.5 algorithm and the SVM-DS algorithm.…”

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confidence: 79%

Fuzzy clustering recognition algorithm of medical image with multi‐resolution feature

Wang

Cui

2018

Concurrency and Computation

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Most current medical images are collected in the presence of interference by ignored interferences such as illumination, occlusion, etc. The recognition rate is low for multi-resolution images in the case of color distortion. Therefore, a novel fuzzy clustering recognition algorithm of multi-resolution medical image was proposed in this paper. First, medical images were analyzed from both acquisition time, shooting angle, resolution, natural light measurement, and background. Secondly, in order to avoid partial occlusion, region was recalculated by correlation between Fourier and Merlin transform. Moreover, Euclidean distance between samples were identified by standardized eigenvalues, where smallest distance obtained maximum degree of membership. Experiment results showed that the proposed method had higher recognition rate (accuracy 90.46% and sensitiveness 97.89%) and stronger anti-interference than current methods. KEYWORDS fuzzy clustering, image processing, image recognition, medical image, multi-resolution 1 INTRODUCTION Today, medical image recognition is based on features of images with pattern recognition method. 1 Medical image recognition is the core content of auto-diagnosis of medical images, which is a hotspot in medical research domain. 2 Medical images contain rich features and rules of humans, which are images with high resolution, massive amount, and complex features. Research of medical image recognition has always faced challenges recently. Research and exploration of suitable algorithms for automatic recognition of medical images have important and practical significance for both theoretical and practical study. It is of great practical value to assist doctors in the clinical diagnosis by medical images. 3 In order to analyze and classify images by using intelligent algorithms, computers can be used to process medical images and extract image features such as depth information by self-learning technology. These analysis and classification improve the accuracy of medical image recognition and assist doctors in diagnosis.An image recognition method combined multiple features to achieve image classification by collecting visual information such as image color, shape, and texture. 4 Recently, there were many algorithms for medical image recognition with multi-resolution. Though some research results had been reached a good result in some.Moeskops et al proposed a medical image recognition algorithm based on multiple modalities. 5 By using cameras to obtain medical information images, this method is easy to implement, but its recognition accuracy is very low because in the analysis of the patient's medical image, there will be some areas of image features overlap, occlusion and so on. Rebouças Filho et al proposed an automatic histologically-closer classification of skin lesions. The R, G, and B components of the collected medical image are converted into H, I, and S color models for description. The use of color features to identify, simple operation, high real-time, are not sensitive to image o...

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confidence: 79%

Fuzzy clustering recognition algorithm of medical image with multi‐resolution feature

Wang

Cui

2018

Concurrency and Computation

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“…Therefore, some novel techniques on fractal image compression and fractal encoding are proposed in [26,27]. In addition, a generalization of the Hurst estimation approach with q-th order moments of the distribution of the increments are used to characterize the statistical evolution of the series in [13].…”

Section: Fbmmentioning

confidence: 99%

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

2018

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Abstract:The separation of coal and gangue is an important process of the coal preparation technology. The conventional way of manual selection and separation of gangue from the raw coal can be replaced by computer vision technology. In the literature, research on image recognition and classification of coal and gangue is mainly based on the grayscale and texture features of the coal and gangue. However, there are few studies on characteristics of coal and gangue from the perspective of their outline differences. Therefore, the multifractal detrended fluctuation analysis (MFDFA) method is introduced in this paper to extract the geometric features of coal and gangue. Firstly, the outline curves of coal and gangue in polar coordinates are detected and achieved along the centroid, thereby the multifractal characteristics of the series are analyzed and compared. Subsequently, the modified local singular spectrum widths ∆h of the outline curve series are extracted as the characteristic variables of the coal and gangue for pattern recognition. Finally, the extracted geometric features by MFDFA combined with the grayscale and texture features of the images are compared with other methods, indicating that the recognition rate of coal gangue images can be increased by introducing the geometric features.

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“…14,15 This paper uses three features in recognition. In this paper, we use image feature selection to avoid it.…”

Section: Feature Extraction Of Multi-feature Ct Medical Imagesmentioning

confidence: 99%

“…Through the effective selection of the features of CT images, feature dimensions used in pattern recognition ARE reduced, and useless information for image recognition are discarded, which makes the accuracy and speed of recognition to improved. 14,15 This paper uses three features in recognition.…”

Section: Feature Extraction Of Multi-feature Ct Medical Imagesmentioning

confidence: 99%

Multi‐feature data mining for CT image recognition

Yong-lian

2018

Concurrency and Computation

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Recognition method of CT images based on color, morphology, and texture is inaccurate and unreliable recently. Therefore, a medical image recognition method based on data mining is proposed with a multi-feature fusion in this paper. First, image acquisition method is determined by analyzing the environment of CT image recognition. Then, acquired CT image is standardized and whitened to reduce redundant information. Moreover, based on feature of the color, texture, and height in preprocessed CT images, a deep neural network is trained by using a large amount of image data in normal scene. The deep learning classifier is fine-tuned by using the marked multi-feature CT image data. Finally, output recognition result is obtained according to the classification decision threshold. Experimental results show that the correct recognition rate of the proposed method can reach more than 98%. The accuracy rate is higher and the stability of proposed method is better by comparing with traditional CT image recognition methods. KEYWORDSCT images, data mining, deep learning, image recognition, multiple feature INTRODUCTIONIn recent years, with the rapid development of medical imaging technology, it improved to big medical image analysis. Today, many big database are constructed. How we can extract useful information from massive medical image data in the database has brought great challenges to medical image recognition since traditional database technology cannot provide enough service for the large database. Data mining is used to obtain effective information in medical image database. CT image recognition is a multidisciplinary cross-domain of comprehensive medical imaging, mathematical modeling, and computer technology. 1,2 Today, there are two new issues that are brought because of large scale of multi-feature CT image. One issue is CT image data that has a higher dimension and requires a model with stronger learning adaptability. The other is big CT image data that is more fragmented with more complex data structure, which requires integration of huge different information. 3 Such requirements cannot be solved by traditional data analysis methods. Therefore, how we extract useful information from massive CT image data has become a research hotspot. With the rapid development and popularization of CT imaging equipment, CT image data have increased explosively. How we can efficiently and accurately analyze CT image has become a major challenge. 4 Furthermore, computer-assisted CT medical image analysis improves the efficiency and accuracy of the analysis and diagnosis of CT image. Therefore, computer-aided detection and diagnosis are becoming a cross-sectional research area that receives more and more attention.A multi-feature CT medical image recognition method was proposed based on polarization spectrum. 5 The method used indoor imaging spectrometer FISS-P equipped with polarizers to collect the polarization spectrum images, including general images and five kinds of CT medical images indoors. They recognized CT images by ...

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Digital image watermarking method based on DCT and fractal encoding

Cited by 132 publications

References 40 publications

Fuzzy clustering recognition algorithm of medical image with multi‐resolution feature

Fuzzy clustering recognition algorithm of medical image with multi‐resolution feature

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

Multi‐feature data mining for CT image recognition

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