基于小波变换的多分辨率医学图像融合

多模医学图像融合在医学图像分析和诊断上具有极为重要的应用价值。本文采用了基于小波变换的多分辨率分析对人脑MRI PET图像进行了融合。结果表明此方法能够充分有效地将解剖信息与功能信息集成在一起 ,并保留原始图像的边缘和纹理特征 ,具有广阔的应用前景     

Medical image compression based on a morphological representation of wavelet coefficients.

Image compression is fundamental to the efficient and cost-effective use of digital medical imaging technology and applications. Wavelet transform techniques currently provide the most promising approach to high-quality image compression which is essential for diagnostic medical applications. A novel approach to image compression based on the wavelet decomposition has been developed which utilizes the shape or morphology of wavelet transform coefficients in the wavelet domain to isolate and retain significant coefficients corresponding to image structure and features. The remaining coefficients are further compressed using a combination of run-length and Huffman coding. The technique has been implemented and applied to full 16 bit medical image data for a range of compression ratios. Objective peak signal-to-noise ratio performance of the compression technique was analyzed. Results indicate that good reconstructed image quality can be achieved at compression ratios of up to 15:1 for the image types studied. This technique represents an effective approach to the compression of diagnostic medical images and is worthy of further, more thorough, evaluation of diagnostic quality and accuracy in a clinical setting.     

Modulation transfer function measurement of scanning reflectance microscopes

Real-time medical imaging systems such as reflectance confocal microscopes and optical coherence microscopes are being tested in multiple-patient and multiple-center clinical trials. The modulation transfer function (MTF) of these systems at any given time influences the image information content and can affect the interpretation of the images. MTF is difficult to measure in real-time scanning systems when imaging at the Nyquist limit. We describe a measurement technique similar to the electronic imaging resolution standards ISO-12233 (electronic cameras) that can be applied to scanned spot imaging systems with asynchronous pixel clocks. This technique requires the acquisition of a single image of a reflective stripe object. An asynchronous pixel clock induces subpixel jitter in the edge location. The jitter is removed using a Fourier method, and an oversampled edge response function is calculated using algorithms developed in MATLAB. This technique provides fast, simple to use, and repeatable full-width at half maximum lateral resolution and MTF measurements based on only one test image. We present the results for reflectance confocal microscopes operating at 0.9 numerical aperture.     

图像处理技术在脊柱侧弯检查中的应用

脊柱侧弯是指脊柱偏离其中心线的畸形生长,是一种常发生在青少年人群中的特发性疾病。本文通过对现有的脊柱侧弯检查方法的分析,指出了利用图像处理技术对青少年脊柱侧弯检测的优越性。主要介绍了两类脊柱侧弯检测的图像处理方法:一类为提取莫尔图像的非对称特征,将其与Cobb角相联系进行检测;另一类为利用不同的光学成像方法对人体背部进行三维重建,然后根据提取的三维特征对脊柱侧弯进行检测。两类方法的相似点在于都涉及到人体背部的对称性的判别问题。最后,对图像处理技术在脊柱侧弯方面的进一步应用做了展望。     

一种基于多尺度分析的CT图像边缘检测方法

为了准确提取CT图像中解剖组织几何形态特征,提出了一种基于多尺度分析的CT图像边缘检测方法。本文应用多尺度分析中含有尺度因子的平滑函数的负导数作为小波,对CT图像实施小波变换,并检测小波变换的模局部极大值,完成基于模局部极大值的解剖组织轮廓特征表达。本文还讨论了一种模局部极大值点的简单筛选方法,针对CT图像噪声较大的特点,以模局部极大值的均方根乘以一个与尺度有关的因子作为模局部极大值的阈值,在不同尺度上获得了清晰的边缘信息。阈值处理后的模局部极大值图表明,不同尺度下的边缘检测能给出大小不同的物体的边缘信息。本方法能在有效抑制噪声的基础上,准确提取感兴趣解剖组织的几何轮廓特征。     

A method of using noise as a test pattern for determining image enhancement filters

Many digitally based medical imaging systems include both reconstruction algorithms and additional image filters designed to enhance certain image features. However, the manufacturers usually consider these algorithms and filters to be proprietory information. The purpose of this note is to describe a simple procedure for determining the spatial frequency response of these proprietary enhancement filters. The technique uses image noise as a test pattern. The procedure consists of acquiring a small number of noise-only data sets (say 10) of a uniform phantom and reconstructing the images using the different filters with repeated use of the noise data sets. A straightforward analysis then yields the enhancement filter frequency responses.     

Authentication and Data Hiding Using a Hybrid ROI-Based Watermarking Scheme for DICOM Images

Authenticating medical images using watermarking techniques has become a very popular area of research, and some works in this area have been reported worldwide recently. Besides authentication, many data-hiding techniques have been proposed to conceal patient’s data into medical images aiming to reduce the cost needed to store data and the time needed to transmit data when required. In this paper, we present a new hybrid watermarking scheme for DICOM images. In our scheme, two well-known techniques are combined to gain the advantages of both and fulfill the requirements of authentication and data hiding. The scheme divides the images into two parts, the region of interest (ROI) and the region of non-interest (RONI). Patient’s data are embedded into ROI using a reversible technique based on difference expansion, while tamper detection and recovery data are embedded into RONI using a robust technique based on discrete wavelet transform. The experimental results show the ability of hiding patient’s data with a very good visual quality, while ROI, the most important area for diagnosis, is retrieved exactly at the receiver side. The scheme also shows some robustness against certain levels of salt and pepper and cropping noise.     

基于小波变换和一致性检验的多模态医学图像融合算法

目的选择已配准后的多聚焦医学图像以及MRI/CT灰度图像为实验素材,以探究不同的融合策略对图像融合效果的影响。方法在对多模态医学图像融合时,低频融合分别采取了加权平均、取极大值法、区域能量以及区域方差的对比实验。高频融合分别采取了区域能量、区域方差以及滤波后基于邻域窗口的一致性检验的对比实验。结果通过对融合图像主观(融合效果)与客观(灰度直方图、边缘提取、性能评价)对比分析,找到了多模态医学图像融合的最优融合策略。结论当低频选择局部区域方差融合,融合后的图像轮廓清晰、边缘较完整;而高频选择滤波后基于邻域窗口的一致性检验,融合后的图像更好地保留和加强了源图像的细节信息。     

Dual-energy digital mammography for calcification imaging: noise reduction techniques

We have previously developed a dual-energy digital mammography (DEDM) technique for calcification imaging under full-field imaging conditions using a commercially available flat-panel based digital mammography system. Although dual-energy (DE) imaging could suppress the obscuration of calcifications by tissue-structure background, it also increases the intrinsic noise in the DE images. Here we report on the effects of three different noise reduction techniques on DE calcification images: a simple smoothing (boxcar) filter applied to the DE image, a median filter applied to the HE image prior to the computation of the DE image and an adaptation of the Kalender's correlated-noise reduction (KNR) technique for DEDM. We compared the different noise reduction techniques by evaluating their effects on DE calcification images of a 5 cm thick breast-tissue-equivalent slab with continuously varying glandular-tissue ratio superimposed with calcium carbonate crystals of various sizes that simulate calcifications. Evaluations of different noise reducing techniques were performed by comparison of the root-mean-square signal in background regions (no calcifications present) of the DE calcification images and the contrast-to-noise ratios (CNR) of the calcifications in the DE calcification images. Amongst the different noise reduction techniques evaluated in this study, the KNR method was found to be most effective in reducing the image noise and increasing the calcification visibility (or CNR), closely followed by the HE median filter technique. Although the simple smoothing (boxcar) filter reduced the noise, it did not improve calcification visibility. The visible calcification threshold size with DEDM over smoothly varying background at screening mammography doses, assuming a CNR threshold of 4, was estimated to be around 250 microm with both the HE median filter and the KNR techniques. The quality of DE images with noise reduction techniques based on phantom studies were verified with DE images of an animal-tissue phantom that consisted of calcifications superimposed over more realistic tissue structures.     

Image reconstruction of anisotropic conductivity tensor distribution in MREIT: computer simulation study

We describe a novel method of reconstructing images of an anisotropic conductivity tensor distribution inside an electrically conducting subject in magnetic resonance electrical impedance tomography (MREIT). MREIT is a recent medical imaging technique combining electrical impedance tomography (EIT) and magnetic resonance imaging (MRI) to produce conductivity images with improved spatial resolution and accuracy. In MREIT, we inject electrical current into the subject through surface electrodes and measure the z-component Bz of the induced magnetic flux density using an MRI scanner. Here, we assume that z is the direction of the main magnetic field of the MRI scanner. Considering the fact that most biological tissues are known to have anisotropic conductivity values, the primary goal of MREIT should be the imaging of an anisotropic conductivity tensor distribution. However, up to now, all MREIT techniques have assumed an isotropic conductivity distribution in the image reconstruction problem to simplify the underlying mathematical theory. In this paper, we firstly formulate a new image reconstruction method of an anisotropic conductivity tensor distribution. We use the relationship between multiple injection currents and the corresponding induced Bz data. Simulation results show that the algorithm can successfully reconstruct images of anisotropic conductivity tensor distributions. While the results show the feasibility of the method, they also suggest a more careful design of data collection methods and data processing techniques compared with isotropic conductivity imaging.     

Bone Age Assessment in Young Children Using Automatic Carpal Bone Feature Extraction and Support Vector Regression

Boundary extraction of carpal bone images is a critical operation of the automatic bone age assessment system, since the contrast between the bony structure and soft tissue are very poor. In this paper, we present an edge following technique for boundary extraction in carpal bone images and apply it to assess bone age in young children. Our proposed technique can detect the boundaries of carpal bones in X-ray images by using the information from the vector image model and the edge map. Feature analysis of the carpal bones can reveal the important information for bone age assessment. Five features for bone age assessment are calculated from the boundary extraction result of each carpal bone. All features are taken as input into the support vector regression (SVR) that assesses the bone age. We compare the SVR with the neural network regression (NNR). We use 180 images of carpal bone from a digital hand atlas to assess the bone age of young children from 0 to 6 years old. Leave-one-out cross validation is used for testing the efficiency of the techniques. The opinions of the skilled radiologists provided in the atlas are used as the ground truth in bone age assessment. The SVR is able to provide more accurate bone age assessment results than the NNR. The experimental results from SVR are very close to the bone age assessment by skilled radiologists.     

An evaluation of intelligent prognostic systems for colorectal cancer

In this paper we describe attempts at building a robust model for predicting the length of survival of patients with colorectal cancer. The aim of the research, reported in this paper, is to study the effective utilisation of artificial intelligence techniques in the medical domain. We suggest that an important research objective of proponents of intelligent prognostic systems must be to evaluate the additionality that AI techniques can bring to an already well-established field of medical prognosis. Towards this end, we compare a number of different AI techniques that lend themselves to the task of predicting survival in colorectal cancer patients. We describe the pros and cons of each of these methods using the usual metrics of accuracy and perspicuity. We then present the notion of intelligent hybrid systems and evaluate the role that they may potentially play in developing robust prognostic models. In particular we evaluate a hybrid system that utilises the k Nearest Neighbour technique in conjunction with Genetic Algorithms. We describe a number of innovations used within this hybrid paradigm used to build the prognostic model. We discuss the issue of censored patients and how this issue can be tackled within the various models used. In keeping with our objective of studying the additionality that AI techniques bring to building prognostic models, we use Cox's regression as a standard and compare each AI technique with it, attempting to discover their capabilities in enhancing prognostic methods in medicine. In doing so we address two main questions--which model fits the data best?, and are the results obtained by the various AI techniques significantly different from those of Cox's regression? We conclude this paper by discussing future enhancements to the work presented and lessons learned from the study to date.     

Basic setup for breast conductivity imaging using magnetic resonance electrical impedance tomography

We present a new medical imaging technique for breast imaging, breast MREIT, in which magnetic resonance electrical impedance tomography (MREIT) is utilized to get high-resolution conductivity and current density images of the breast. In this work, we introduce the basic imaging setup of the breast MREIT technique with an investigation of four different imaging configurations of current-injection electrode positions and pathways through computer simulation studies. Utilizing the preliminary findings of a best breast MREIT configuration, additional numerical simulation studies have been carried out to validate breast MREIT at different levels of SNR. Finally, we have performed an experimental validation with a breast phantom on a 3.0 T MREIT system. The presented results strongly suggest that breast MREIT with careful imaging setups could be a potential imaging technique for human breast which may lead to early detection of breast cancer via improved differentiation of cancerous tissues in high-resolution conductivity images.     

Selective medical image compression techniques for telemedical and archiving applications

Selective Image Compression (SeLIC) is a compression technique where explicitly defined regions of interest (RoI) are compressed in a lossless way whereas image regions containing unimportant information are compressed in a lossy manner. Such techniques are of great interest in telemedicine or medical imaging applications with large storage requirements. In this paper we introduce and compare techniques with different functionalities. Moreover, we investigate the impact of using wavelet transforms and JPEG as underlying lossy compression algorithm.     

Entropy improvement by the Temporal-Window method for Alternating and Non-Alternating 3D wavelet transform over angiographies

The three-dimensional wavelet transform (3D-WT) has been proposed for volumetric data coding, since it can provide lossless coding and top-quality reconstruction: two key features highly relevant to medical imaging applications. In this paper, we present experimental results for four new algorithms based on the Classic 3D-WT. The proposed algorithms are capable of obtaining the wavelet coefficients after the spatial and, mainly, the temporal decomposition processes, reducing most redundancies in the video sequence and getting lower entropy values than the Classic algorithm. The new algorithms are based on the Temporal-Window method for carrying out the temporal decomposition. We have conducted a set of experimental evaluations for a representative data set of a modality of intrinsically volumetric medical imaging: angiography sequences. This work has been jointly supported by the Spanish MEC and European Comission FEDER funds under grants “Consolider Ingenio-2010 CSD2006-00046" and “TIN2006-15516-C04-02".     

Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data

Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in the creation of image databases, as well as picture archiving and communication systems. These repositories now contain images from a diverse range of modalities, multidimensional (three-dimensional or time-varying) images, as well as co-aligned multimodality images. These image collections offer the opportunity for evidence-based diagnosis, teaching, and research; for these applications, there is a requirement for appropriate methods to search the collections for images that have characteristics similar to the case(s) of interest. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. Medical CBIR is an established field of study that is beginning to realize promise when applied to multidimensional and multimodality medical data. In this paper, we present a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieval, and retrieval from diverse datasets. We use these categories as a framework for discussing the state of the art, focusing on the characteristics and modalities of the information used during medical image retrieval.     

Image Texture Characterization Using the Discrete Orthonormal S-Transform

We present a new efficient approach for characterizing image texture based on a recently published discrete, orthonormal space-frequency transform known as the DOST. We develop a frequency-domain implementation of the DOST in two dimensions for the case of dyadic frequency sampling. Then, we describe a rapid and efficient approach to obtain local spatial frequency information for an image and show that this information can be used to characterize the horizontal and vertical frequency patterns in synthetic images. Finally, we demonstrate that DOST components can be combined to obtain a rotationally invariant set of texture features that can accurately classify a series of texture patterns. The DOST provides the computational efficiency and multi-scale information of wavelet transforms, while providing texture features in terms of Fourier frequencies. It outperforms leading wavelet-based texture analysis methods.     

A Modified Undecimated Discrete Wavelet Transform Based Approach to Mammographic Image Denoising

In this work, the authors present an effective denoising method to attempt reducing the noise in mammographic images. The method is based on using hierarchical correlation of the coefficients of discrete stationary wavelet transforms. The features of the proposed technique include iterative use of undecimated multi-directional wavelet transforms at adjacent scales. To validate the proposed method, computer simulations were conducted, followed by its applications to clinical mammograms. Mutual information originating from information theory was used as an evaluation measure for selection of an optimal wavelet basis function. We examined the performance of the proposed method by comparing it with the conventional undecimated discrete wavelet transform (UDWT) method in terms of processing time-consuming and image quality. Our results showed that with the use of the proposed method the computation time can be reduced to approximately 1/10 of the conventional UDWT method consumed. The results of visual assessment indicated that the images processed with the proposed UDWT method showed statistically significant superior image quality over those processed with the conventional UDWT method. Our research results demonstrate the superiority and effectiveness of the proposed approach.     

Ultrasound and angio image compression by cosine and wavelet transforms.

The investigation results for improving lossy compression techniques for ultrasound and angio images are presented. The goal was to determine where the compression process could be improved for the medical application, and to make efforts to improve it. It is proved that the wavelet transform outperforms the discrete cosine transform applied to ultrasound and angio images. A lot of wavelet classes were tried for choosing the best one suited for corresponding image classes, which were characterised by a content complexity criterion. The analysis of international image compression standards was carried out. Special attention was paid to an algorithmical and high level service structure of a new still image compression standard JPEG2000. Its open architecture enables including some wavelet classes which we would like to suggest for medical images. A set of recommendations for acceptable compression ratio for different medical image modalities was developed. It was carried out on the base of compression study performed by the group of angiologists and cardiologists.