Intuitionistic fuzzy approach for enhancement of low contrast mammogram images

Mammogram image enhancement is very much necessary in diagnosing breast cancer or tumor at an early stage. Nonuniform illumination and low contrast images are commonly encountered in mammogram images. Conventional enhancement algorithms produce either some artifacts or cannot highlight minute details present in the images, particularly when dealing with mammogram images. In this article, we propose a new mammogram image enhancement scheme using Atanassov's intuitionistic fuzzy set (IFS) theory. IFS considers two uncertainties—membership and nonmembership degree apart from membership degree as in fuzzy set theory. As mammogram images are low contrast images and many of the image definitions are vague/unclear, so IFS theory may be suitable for better image enhancement. Initially, the image is transformed to an intuitionistic fuzzy image using a novel intuitionistic fuzzy generator. Hesitation degree is computed and using the hesitation degree, two membership levels are computed to form an interval type 2 fuzzy set. These two membership functions are then combined using Zadeh's fuzzy t-conorm to form a new membership function. Threshold of interval type 2 fuzzy image is obtained using restricted equivalence function. Using the threshold, modified fuzzy hyperbolization is carried out. Real data experiments demonstrate that the proposed algorithm has better performance on contrast and visual quality of the images both quantitatively and qualitatively when compared with different existing methods.     

一种有效的机场安检X光手提行李图像两级增强方法

针对低对比度X光手提行李图像在机场安检中容易产生高虚警或高漏警的问题,提出了一种两级X光图像增强方法.首先,应用离散小波变换和独立分量分析方法对低能和高能X光图像去噪并融合,实现一级增强.然后,利用本文提出的自适应正弦灰度变换实现二级增强.实验结果表明,本文方法能够有效地改善图像质量,优于文中给出的其它增强方法,更有利...     

A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

Assuring medical images protection and robustness is a compulsory necessity nowadays. In this paper, a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform (DWT) with the energy compaction of the Discrete Wavelet Transform (DCT). The multi-level Encryption-based Hybrid Fusion Technique (EbhFT) aims to achieve great advances in terms of imperceptibility and security of medical images. A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT transform. Afterwards, a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the watermark. Lastly, a PN-sequence key is formed along with a supplementary key in the third layer of the EbHFT. Thus, the watermarked image is generated by enclosing both keys into DWT and DCT coefficients. The fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT methods. In order to validate the proposed technique, a standard dataset of medical images is used. Simulation results show higher performance of the visual quality (i.e., 57.65) for the watermarked forms of all types of medical images. In addition, EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation (NC). Finally, extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.     

Fuzzy Based Hybrid Focus Value Estimation for Multi Focus Image Fusion

Due to limited depth-of-field of digital single-lens reflex cameras, the scene content within a limited distance from the imaging plane remains in focus while other objects closer to or further away from the point of focus appear as blurred (out-of-focus) in the image. Multi-Focus Image Fusion can be used to reconstruct a fully focused image from two or more partially focused images of the same scene. In this paper, a new Fuzzy Based Hybrid Focus Measure (FBHFM) for multi-focus image fusion has been proposed. Optimal block size is very critical step for multi-focus image fusion. Particle Swarm Optimization (PSO) algorithm has been used to find optimal size of the block of the images for extraction of focus measure features. After finding optimal blocks, three focus measures Sum of Modified Laplacian, Gray Level Variance and Contrast Visibility has been extracted and combined these focus measures by using intelligent fuzzy technique. Fuzzy based hybrid intelligent focus values were estimated using contrast visibility measure to generate focused image. Different sets of multi-focus images have been used in detailed experimentation and compared the results with state-of-the-art existing techniques such as Genetic Algorithm (GA), Principal Component Analysis (PCA), Laplacian Pyramid discrete wavelet transform (DWT), and aDWT for image fusion. It has been found that proposed method performs well as compare to existing methods.

     

一种小波和余弦变换组合域内鲁棒的自适应零水印算法

克服了传统数字水印方案在嵌入水印时要对原始图像数据进行一定修改从而导致嵌入水印图像质量下降的局限性,提出了一个基于离散小波变换和离散余弦变换组合域内鲁棒的自适应零水印算法.算法首先对原始数字图像进行适当层次的离散小波变换,并对得到的小波逼近子图进行离散余弦变换,然后依据待嵌入二值字符水印的大小和相应的系数差值要求自适应...     

A Robust Image Watermarking Scheme Using Z-Transform,Discrete Wavelet Transform and Bidiagonal Singular Value Decomposition

Watermarking is a widely used solution to the problems of authentication and copyright protection of digital media especially for images, videos, and audio data. Chaos is one of the emerging techniques adopted in image watermarking schemes due to its intrinsic cryptographic properties. This paper proposes a new chaotic hybrid watermarking method combining Discrete Wavelet Transform (DWT), Z-transform (ZT) and Bidiagonal Singular Value Decomposition (BSVD). The original image is decomposed into 3-level DWT, and then, ZT is applied on the HH3 and HL3 sub-bands. The watermark image is encrypted using Arnold Cat Map. BSVD for the watermark and transformed original image were computed, and the watermark was embedded by modifying singular values of the host image with the singular values of the watermark image. Robustness of the proposed scheme was examined using standard test images and assessed against common signal processing and geometric attacks. Experiments indicated that the proposed method is transparent and highly robust.     

小波分析及其在刀具监控系统中的应用

应用离散小波变换进行刀具破损信号分析及确定刀具状态的研究。离散小波变换具有时－频分析特性，能将瞬短信号的细微变化显现出来。对钻削电机电流信号作离散小波变换的结果表明：离散小波变换对刀具破损信号来说比ＦＦＴ更为敏感，用于刀具监控系统可明显地提高系统的可靠性。     

抗亮度调整攻击的小波域图像数字水印方法

现有的许多小波变换域图像数字水印方法无法抵抗亮度调整攻击,为此,本丈提出了一个抗亮度调整攻击的小波变换域图像数字水印新方法.该方法在原始图像小波变换域的低频系数中嵌入水印,而在含水印图像小波变换域低频系数抗亮度调整修正后的值中提取水印.该方法简单、快速、有效,在提取水印时无需原始图像.实验结果表明:该方法具有很好的水印透明性,对亮度调整攻击非常稳健,并且对常见的其他图像处理攻击(如重采样、颜色抖动、平滑、加噪声和有损压缩等)具有很强的稳健性.     

Efficient Landsat image fusion using fuzzy and stationary discrete wavelet transform

Image fusion is the concept to integrate multiple same scene images while drawing out maximum radiometric information from them by avoiding noise and fictional data. The main objective is to improve the radiometric quality of fused image compared to individual images of the same scene. Existing methods are found to be efficient, but if the similar radiometric information is fused into every image, it produces redundant high frequency of pixels. Therefore, to overcome this issue, in this paper a fuzzy and stationary discrete wavelet transform (FSDWT)-based image fusion technique is proposed. It decomposes Landsat image into stationary values, and then it preserves the radiometric data by using fuzzy if-then rules. In the last phase, FSDWT injects high-frequency blocks from input images and returns a single Landsat image with maximum radiometric data. Quantitative analysis has clearly demonstrated that FSDWT has better structural detail, spatial resolution and spectral information than existing methods.     

Improving medical image fusion method using fuzzy entropy and nonsubsampling contourlet transform

Many types of medical images must be fused, as single‐modality medical images can only provide limited information due to the imaging principles and the complexity of human organ structures. In this paper, a multimodal medical image fusion method that combines the advantages of nonsubsampling contourlet transform (NSCT) and fuzzy entropy is proposed to provide a basis for clinical diagnosis and improve the accuracy of target recognition and the quality of fused images. An image is initially decomposed into low‐ and high‐frequency subbands through NSCT. The corresponding fusion rules are adopted in accordance with the different characteristics of the low‐ and high‐frequency components. The membership degree of low‐frequency coefficients is calculated. The fuzzy entropy is also computed and subsequently used to guide the fusion of coefficients to preserve image details. High‐frequency components are fused by maximizing the regional energy. The final fused image is obtained by inverse transformation. Experimental results show that the proposed method achieves good fusion effect based on the subjective visual effect and objective evaluation criteria. This method can also obtain high average gradient, SD, and edge preservation and effectively retain the details of the fused image. The results of the proposed algorithm can provide effective reference for doctors to assess patient condition.     

Multimodal Medical Image Registration and Fusion for Quality Enhancement

For the last two decades, physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body. However, most of the time, medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited information. To overcome this problem, a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early stages. In the proposed method, a Multi-resolution Rigid Registration (MRR) technique is used for multimodal image registration while Discrete Wavelet Transform (DWT) along with Principal Component Averaging (PCAv) is utilized for image fusion. The proposed MRR method provides more accurate results as compared with Single Rigid Registration (SRR), while the proposed DWT-PCAv fusion process adds-on more constructive information with less computational time. The proposed method is tested on CT and MRI brain imaging modalities of the HARVARD dataset. The fusion results of the proposed method are compared with the existing fusion techniques. The quality assessment metrics such as Mutual Information (MI), Normalize Cross-correlation (NCC) and Feature Mutual Information (FMI) are computed for statistical comparison of the proposed method. The proposed methodology provides more accurate results, better image quality and valuable information for medical diagnoses.     

A novel key-frame selection-based sign language recognition framework for the video data

ABSTRACT

Sign language is a medium of communication for people with hearing disabilities. Static and dynamic gestures are identified in a video-based sign language recognition and translated them into humanly understandable phrases to achieve the communication objective. However, videos contain redundant Key-frames which require additional processing. Number of such Key-frames can be reduced. The selection of particular Key-frames without losing the required information is a challenging task. The Key-frame extraction algorithm is used which helps to speed-up the sign language recognition process by extracting essential key-frames. The proposed framework eliminates the computation overhead by picking up the distinct Key-frames for the recognition process. Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Histograms of Oriented Gradient (HOG) are used for unique features extraction. We used the bagged tree, boosted tree ensemble method, Fine KNN, and SVM for classification. We tested methodology on video-based datasets of Pakistani Sign Language. It achieved an overall 97.5% accuracy on 37 Urdu alphabets and 95.6% accuracy on 100 common words.     

Performance analysis of computer aided brain tumor detection system using ANFIS classifier

The abrupt changes in brain cells due to the environmental effects or genetic disorders leads to form the abnormal lesions in brain. These abnormal lesions are combined as mass and known as tumor. The detection of these tumor cells in brain image is a complex task due to the similarities between normal cells and tumor cells. In this paper, an automated brain tumor detection and segmentation methodology is proposed. The proposed method consists of feature extraction, classification and segmentation. In this paper, Grey Level Co‐Occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT) and Law's texture features are used as features. These features are fed to Adaptive Neuro Fuzzy Inference System (ANFIS) classifier as input pattern, which classifies the brain image. Morphological operations are now applied on the classified abnormal brain image to segment the tumor regions. The proposed system achieves 95.07% of sensitivity, 99.84% of specificity and 99.80% of accuracy for tumor segmentation.     

基于模糊Choquet积分的图像融合效果评价

针对多源图像的融合效果评价问题,提出了一种基于模糊Choquet积分的图像融合效果评价方法。该方法采用融合图像熵值、交叉熵平均值、交叉熵均方根值、平均梯度值构成单因素评价指标集,基于知识确定F测度和各单因素评价指标的隶属度函数,应用Choquet积分综合各单因素指标得到一个综合评价指标。该方法综合利用了多个单因素指标的信息,并将人的知识引入到对图像融合效果的评价中,其评价结果更为全面客观。将该方法应用于CCD/SAR图像的融合效果评价,评价结果与理论分析结果和目视效果是一致的,表明了该评价方法的有效性。     

Multimodal medical image fusion based on discrete Tchebichef moments and pulse coupled neural network

Multimodal medical image fusion plays a vital role in clinical diagnoses and treatment planning. In many image fusion methods‐based pulse coupled neural network (PCNN), normalized coefficients are used to motivate the PCNN, and this makes the fused image blur, detail loss, and decreases contrast. Moreover, they are limited in dealing with medical images with different modalities. In this article, we present a new multimodal medical image fusion method based on discrete Tchebichef moments and pulse coupled neural network to overcome the aforementioned problems. First, medical images are divided into equal‐size blocks and the Tchebichef moments are calculated to characterize image shape, and energy of blocks is computed as the sum of squared non‐DC moment values. Then to retain edges and textures, the energy of Tchebichef moments for blocks is introduced to motivate the PCNN with adaptive linking strength. Finally, large firing times are selected as coefficients of the fused image. Experimental results show that the proposed scheme outperforms state‐of‐the‐art methods and it is more effective in processing medical images with different modalities. © 2017 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 27, 57–65, 2017     

Cross‐band fusion by energy weight as solution to illumination and arch restrictions in palm‐print recognition

Palm‐print image is often influenced by illumination and the arch of the palm‐print results in a great deal of noise during the course of palm‐print image acquisition. This article presents palm‐print recognition based on cross‐band fusion by energy weight and comprehensively takes into account the noisy properties of various sub‐bands in single wavelet level and decomposition coefficient of the adjacent sub‐band, thus the method of energy weight based on palm‐print image is employed. Then two‐dimensional principal component analysis (2DPCA) is employed for dimension reduction and decorrelated. Finally, a nearest neighbor classifier is used for palm‐print recognition. Experimental results on Hong Kong PolyU palm‐print database show that correct recognition rate can reach 100% by the method presented within this article. Correct recognition rate and recognition efficiency is higher than that of 2DPCA and WT + DCT + 2DPCA (Wavelet Transform, Discrete Cosine Transform and 2DPCA, WT + DCT + 2DPCA). © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 350–355, 2009     

Enhancing security of DICOM images during storage and transmission in distributed environment

Digital watermarking is proposed as a method to enhance medical data security. Medical image watermarking requires extreme care when embedding additional information within medical images, because the additional information should not degrade medical image quality. Doctors diagnose medical images by seeing Region of Interest (ROI). A ROI of a medical image is an area including important information and must be stored without any distortion. If a medical image is illegally obtained or if its content is changed, it may lead to wrong diagnosis. In our scheme a well-known technique least significant bit substitution (LSB) is adapted to fulfill the requirements of data hiding and authentication in medical images. The scheme divide Digital Imaging and Communications in Medicine (DICOM) image into two parts ROI and non-ROI (non-region of interest). The DS (Digital Signature) which include patient data and SOP Instance UID are embedded randomly into the LSB border of the image. The DWT (Discrete Wavelet Transform) of ROI-MSB (most significant bit) embedded into the LSB middle of the image. The Result shows the ability to hide and retrieve DS in non-ROI, while ROI, the most important area for diagnosis, is retrieved exactly at the receiver side.     

Segmentation of glioma tumors using convolutional neural networks

The abnormal development of cells in brain leads to the formation of tumors in brain. In this article, image fusion based brain tumor detection and segmentation methodology is proposed using convolutional neural networks (CNN). This proposed methodology consists of image fusion, feature extraction, classification, and segmentation. Discrete wavelet transform (DWT) is used for image fusion and enhanced brain image is obtained by fusing the coefficients of the DWT transform. Further, Grey Level Co‐occurrence Matrix features are extracted and fed to the CNN classifier for glioma image classifications. Then, morphological operations with closing and opening functions are used to segment the tumor region in classified glioma brain image.     

一种基于小波变换的目标融合跟踪方法

本文在分析小波变换的基础上，将小波分析应用到目标图像的融合跟踪技术上，利用小波的多尺度和多分辨特性，不仅能够获得不同分辨力下的图像序列，进行目标图像融合；还能有效地从信号中提取突变信号。对函数或信号进行多尺度的细化分析。图像边缘用小波变换进行处理和提取并对图像形心进行计算。能够得到较好的轮廓提取效果和形心定位精度，进而说明了小波变换可能成为目标跟踪中较好的数学方法。     

Failure mode and effect analysis using the hesitant intuitionistic fuzzy hybrid GRP approach with ordered comprehensive weights

Failure mode and effect analysis (FMEA), a multidisciplinary reliability analysis tool based on team evaluations, has been widely used in various industries. There are three critical issues in FMEA: the conversion of linguistic evaluations, the weights of risk factors, and the ranking mechanism of failure modes. Scholars have used various fuzzy theories and multi-attribute decision-making (MADM) methods to improve traditional FMEA, but there are still deficiencies. In this paper, the hesitant intuitionistic fuzzy set (HIFS), a concept that combines the intuitionistic fuzzy set (IFS) and the hesitant fuzzy set (HFS), is introduced into FMEA to convert linguistic evaluations. Some operators based on HIFS are proposed to process the converted data. Among them, a hesitant intuitionistic fuzzy comprehensive weighted Hamming distance (HIFCWHD) operator is proposed to compute the ordered comprehensive weight, effectively weakening the effect of extreme scores on results. The gray relational projection (GRP) method is adopted to determine the risk priority order of the failure modes. Finally, we give an illustrative case to demonstrate the effectiveness of the proposed FMEA method.