Robust Watermarking Algorithm for Medical Images Based on Non-Subsampled Shearlet Transform and Schur Decomposition

With the development of digitalization in healthcare,more and more information is delivered and stored in digital form,facilitating people’s lives significantly.In the meanwhile,privacy leakage and security issues come along with it.Zero watermarking can solve this problem well.To protect the security of medical information and improve the algorithm’s robustness,this paper proposes a robust watermarking algorithm for medical images based on Non-Subsampled Shearlet Transform(NSST)and Schur decomposition.Firstly,the low-frequency subband image of the original medical image is obtained by NSST and chunked.Secondly,the Schur decomposition of low-frequency blocks to get stable values,extracting the maximum absolute value of the diagonal elements of the upper triangle matrix after the Schur decom-position of each low-frequency block and constructing the transition matrix from it.Then,the mean of the matrix is compared to each element’s value,creating a feature matrix by combining perceptual hashing,and selecting 32 bits as the feature sequence.Finally,the feature vector is exclusive OR(XOR)operated with the encrypted watermark information to get the zero watermark and complete registration with a third-party copyright certification center.Experimental data show that the Normalized Correlation(NC)values of watermarks extracted in random carrier medical images are above 0.5,with higher robustness than traditional algorithms,especially against geometric attacks and achieve watermark information invisibility without altering the carrier medical image.

基于NSCT结合显著图与区域能量的红外与可见光图像融合

针对传统的红外与可见光图像融合出现的清晰度和对比度偏低,目标不够突出的问题,本文提出了一种基于Non-subsampledContourlet(NSCT)变换结合显著图与区域能量的融合方法。首先,使用改进的频率调谐(Frequency-tuned,FT)方法求出红外图像显著图并归一化得到显著图权重,单尺度Retinex(Single-scale Retinex,SSR)处理可见光图像。其次,使用NSCT分解红外与可见光图像,并基于归一化显著图与区域能量设计新的融和权重来指导低频系数融合,解决了区域能量自适应加权容易引入噪声的问题;采用改进的“加权拉普拉斯能量和”指导高频系数融合。最后,通过逆NSCT变换求出融合图像。本文方法与7种经典方法在6组图像中进行对比实验,在信息熵、互信息、平均梯度和标准差指标中最优,在空间频率中第一组图像为次优,其余图像均为最优结果。融合图像信息量丰富、清晰度高、对比度高并且亮度适中易于人眼观察,验证了本文方法的有效性。

Digital watermarking algorithm based on scale-invariant feature regions in non-subsampled contourlet transform domain

Contraposing the need of the robust digital watermark for the copyright protection field, a new digital watermarking algorithm in the non-subsampled contourlet transform （NSCT） domain is proposed. The largest energy sub-band after NSCT is selected to embed watermark. The watermark is embedded into scaleinvariant feature transform （SIFT） regions. During embedding, the initial region is divided into some cirque sub-regions with the same area, and each watermark bit is embedded into one sub-region. Extensive simulation results and comparisons show that the algorithm gets a good trade-off of invisibility, robustness and capacity, thus obtaining good quality of the image while being able to effectively resist common image processing, and geometric and combo attacks, and normalized similarity is almost all reached.

Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an important role in clinical applications. In this paper, an image fusion technique for the fusion of multimodal medical images is proposed based on Non-Subsampled Contourlet Transform. The proposed technique uses the Non-Subsampled Contourlet Transform (NSCT) to decompose the images into lowpass and highpass subbands. The lowpass and highpass subbands are fused by using mean based and variance based fusion rules. The reconstructed image is obtained by taking Inverse Non-Subsampled Contourlet Transform (INSCT) on fused subbands. The experimental results on six pairs of medical images are compared in terms of entropy, mean, standard deviation, QAB/F as performance parameters. It reveals that the proposed image fusion technique outperforms the existing image fusion techniques in terms of quantitative and qualitative outcomes of the images. The percentage improvement in entropy is 0% - 40%, mean is 3% - 42%, standard deviation is 1% - 42%, QAB/Fis 0.4% - 48% in proposed method comparing to conventional methods for six pairs of medical images.

Skin Lesion Classification System Using Shearlets

The main cause of skin cancer is the ultraviolet radiation of the sun.It spreads quickly to other body parts.Thus,early diagnosis is required to decrease the mortality rate due to skin cancer.In this study,an automatic system for Skin Lesion Classification(SLC)using Non-Subsampled Shearlet Transform(NSST)based energy features and Support Vector Machine(SVM)classifier is proposed.Atfirst,the NSST is used for the decomposition of input skin lesion images with different directions like 2,4,8 and 16.From the NSST’s sub-bands,energy fea-tures are extracted and stored in the feature database for training.SVM classifier is used for the classification of skin lesion images.The dermoscopic skin images are obtained from PH^(2) database which comprises of 200 dermoscopic color images with melanocytic lesions.The performances of the SLC system are evaluated using the confusion matrix and Receiver Operating Characteristic(ROC)curves.The SLC system achieves 96%classification accuracy using NSST’s energy fea-tures obtained from 3^(rd) level with 8-directions.

Multimodal Medical Image Fusion Based on Parameter Adaptive PCNN and Latent Low-rank Representation

Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning.Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods,a new multimodal medical image fusion method is proposed.This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients,then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients,and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients.Finally,based on the automatic setting of parameters,the optimization method configuration of the time decay factorαe is carried out.The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images,and at the same time,it has achieved great improvement in visual quality and objective evaluation indicators.

Multi-focus image fusion based on block matching in 3D transform domain

Fusion methods based on multi-scale transforms have become the mainstream of the pixel-level image fusion. However,most of these methods cannot fully exploit spatial domain information of source images, which lead to the degradation of image.This paper presents a fusion framework based on block-matching and 3D(BM3D) multi-scale transform. The algorithm first divides the image into different blocks and groups these 2D image blocks into 3D arrays by their similarity. Then it uses a 3D transform which consists of a 2D multi-scale and a 1D transform to transfer the arrays into transform coefficients, and then the obtained low-and high-coefficients are fused by different fusion rules. The final fused image is obtained from a series of fused 3D image block groups after the inverse transform by using an aggregation process. In the experimental part, we comparatively analyze some existing algorithms and the using of different transforms, e.g. non-subsampled Contourlet transform(NSCT), non-subsampled Shearlet transform(NSST), in the 3D transform step. Experimental results show that the proposed fusion framework can not only improve subjective visual effect, but also obtain better objective evaluation criteria than state-of-the-art methods.

Image edge detection based on pulse coupled neural network and modulus maxima in non-subsampled contourlet domain

Edge is the intrinsic geometric structure of an image. Edge detection methods are the key technologies in the lleld of image processing. In this paper, a multi-scale image edge detection method is proposed to effectively extract image geometric features. A source image is decomposed into the high frequency directional sub-bands coefficients and the low frequency sub-bands coefficients by non-subampled contourlet transform （NSCT）. The high frequency sub-bands coefficients are used to detect the abundant details of the image edges by the modulus maxima （MM） algorithm. The low frequency sub-band coefficients are used to detect the basic contour line of the image edges by the pulse coupled neural network （PCNN）. The final edge detection image is reconstructed with detected edge information at different scales and different directional sub-bands in the NSCT domain. Experimental results demonstrate that the proposed method outperforms several state-of-art image edge detection methods in both visual effects and objective evaluation.

Deformable Registration Algorithm via Non-subsampled Contourlet Transform and Saliency Map

Medical image registration is widely used in image-guided therapy and image-guided surgery to estimate spatial correspondence between planning and treatment images.However,most methods based on intensity have the problems of matching ambiguity and ignoring the influence of weak correspondence areas on the overall registration.In this study,we propose a novel general-purpose registration algorithm based on free-form deformation by non-subsampled contourlet transform and saliency map,which can reduce the matching ambiguities and maintain the topological structure of weak correspondence areas.An optimization method based on Markov random fields is used to optimize the registration process.Experiments on four public datasets from brain,cardiac,and lung have demonstrated the general applicability and the accuracy of our algorithm compared with two state-of-the-art methods.

基于区域分割的红外与可见光图像融合算法的研究(英文)

This article proposes a novel method to fuse infrared and visible light images based on region segmentation. Region segmen-tation is used to determine important regions and background information in the input image. The non-subsampled contourlet transform (NSCT) provides a flexible multiresolution,local and directional image expansion,and also a sparse representation for two-dimensional (2-D) piecewise smooth signal building images,and then different fusion rules are applied to fuse the NSCT coefficients fo...

基于PCNN图像分割的医学图像融合算法

为充分提取源图像间的互补信息,改进传统的图像融合算法在亮度维持、能量保留、边缘信息保持等方面的不足,本文提出了基于脉冲耦合神经网络(pulse coupled neural network, PCNN)图像分割的医学图像融合算法。该算法综合了非下采样剪切波变换(non-subsampled shearlet transform, NSST)与PCNN。首先,选取标准差较大的源图像作为被分割图像,标准差较小的源图像作为参照图像,将源图像进行NSST分解,获取源图像低频子带系数和高频子带系数;在低频融合中,利用参数自适应的PCNN对被分割图像的低频子带进行分割,根据分割结果获取融合低频子带系数;在高频融合中,采用以区域能量和与拉普拉斯能量和两者的乘积作为判断函数,获取融合高频子带系数;利用NSST逆变换获取融合图像。最后,应用本文提出的算法,对脑萎缩、急性中风和高血压性脑病等3组电脑断层扫描/磁共振成像(computerized tomography/magnetic resonance imaging, CT/MRI)图像进行了融合仿真,并将仿真结果与2018年后国际刊上提出的5种算法的融合图像进行比较。结果表明,应用本文提出的融合算法得到的图像,有效地增强了不同模态间的信息互补,保持了融合图像与源图像具有相同明亮程度,又保留了源图像低亮度部分的边缘信息,更加符合人眼视觉特性,具有更高的客观评价指标。

De-scattering and edge-enhancement algorithms for underwater image restoration

Image restoration is a critical procedure for underwater images, which suffer from serious color deviation and edge blurring. Restoration can be divided into two stages: de-scattering and edge enhancement. First, we introduce a multi-scale iterative framework for underwater image de-scattering, where a convolutional neural network is used to estimate the transmission map and is followed by an adaptive bilateral filter to refine the estimated results. Since there is no available dataset to train the network, a dataset which includes 2000 underwater images is collected to obtain the synthetic data. Second, a strategy based on white balance is proposed to remove color casts of underwater images. Finally, images are converted to a special transform domain for denoising and enhancing the edge using the non-subsampled contourlet transform. Experimental results show that the proposed method significantly outperforms state-of-the-art methods both qualitatively and quantitatively.

Low-light color image enhancement based on NSST

In order to improve the visibility and contrast of low-light images and better preserve the edge and details of images,a new low-light color image enhancement algorithm is proposed in this paper.The steps of the proposed algorithm are described as follows.First,the image is converted from the red,green and blue(RGB)color space to the hue,saturation and value(HSV)color space,and the histogram equalization(HE)is performed on the value component.Next,non-subsampled shearlet transform(NSST)is used on the value component to decompose the image into a low frequency sub-band and several high frequency sub-bands.Then,the low frequency sub-band and high frequency sub-bands are enhanced respectively by Gamma correction and improved guided image filtering(IGIF),and the enhanced value component is formed by inverse NSST transform.Finally,the image is converted back to the RGB color space to obtain the enhanced image.Experimental results show that the proposed method not only significantly improves the visibility and contrast,but also better preserves the edge and details of images.