Optimal embedding for shape indexing in medical image databases

This paper addresses the problem of indexing shapes in medical image databases. Shapes of organs are often indicative of disease, making shape similarity queries important in medical image databases. Mathematically, shapes with landmarks belong to shape spaces which are curved manifolds with a well defined metric. The challenge in shape indexing is to index data in such curved spaces. One natural indexing scheme is to use metric trees, but metric trees are prone to inefficiency. This paper proposes a more efficient alternative.We show that it is possible to optimally embed finite sets of shapes in shape space into a Euclidean space. After embedding, classical coordinate-based trees can be used for efficient shape retrieval. The embedding proposed in the paper is optimal in the sense that it least distorts the partial Procrustes shape distance.The proposed indexing technique is used to retrieve images by vertebral shape from the NHANES II database of cervical and lumbar spine X-ray images maintained at the National Library of Medicine. Vertebral shape strongly correlates with the presence of osteophytes, and shape similarity retrieval is proposed as a tool for retrieval by osteophyte presence and severity.Experimental results included in the paper evaluate (1) the usefulness of shape similarity as a proxy for osteophytes, (2) the computational and disk access efficiency of the new indexing scheme, (3) the relative performance of indexing with embedding to the performance of indexing without embedding, and (4) the computational cost of indexing using the proposed embedding versus the cost of an alternate embedding. The experimental results clearly show the relevance of shape indexing and the advantage of using the proposed embedding.     

Multi-slice low-rank tensor decomposition based multi-atlas segmentation: Application to automatic pathological liver CT segmentation

Liver segmentation from abdominal CT images is an essential step for liver cancer computer-aided diagnosis and surgical planning. However, both the accuracy and robustness of existing liver segmentation methods cannot meet the requirements of clinical applications. In particular, for the common clinical cases where the liver tissue contains major pathology, current segmentation methods show poor performance. In this paper, we propose a novel low-rank tensor decomposition (LRTD) based multi-atlas segmentation (MAS) framework that achieves accurate and robust pathological liver segmentation of CT images. Firstly, we propose a multi-slice LRTD scheme to recover the underlying low-rank structure embedded in 3D medical images. It performs the LRTD on small image segments consisting of multiple consecutive image slices. Then, we present an LRTD-based atlas construction method to generate tumor-free liver atlases that mitigates the performance degradation of liver segmentation due to the presence of tumors. Finally, we introduce an LRTD-based MAS algorithm to derive patient-specific liver atlases for each test image, and to achieve accurate pairwise image registration and label propagation. Extensive experiments on three public databases of pathological liver cases validate the effectiveness of the proposed method. Both qualitative and quantitative results demonstrate that, in the presence of major pathology, the proposed method is more accurate and robust than state-of-the-art methods.     

基于图割与粗糙集的MRI脑部肿瘤图像检索方法

背景:基于内容的医学图像检索是一门涉及多领域的学科,由于各种医学图像的成像原理不同,产生的图像在颜色、纹理和形状等视觉特征方面存在差别,使得此方法的实现还存在许多需要解决的问题.目的:针对基于内容的医学图像检索中存在特征提取困难、检索时间长的问题,提出一种基于图割与粗糙集结合的相似图像检索方法.方法:为克服图割仅适用于较少象素的图像和倾向于小割集的缺陷,首先对图像进行聚类,然后构建图像的Gomory-Hu割树,按割值大小依次去掉值较小的边,提取出图像的特征子图并构建特征库.为实现快速检索,借助粗糙集对特征库中的特征进行约简,有效减少参与相似性比较的特征数量.并将此方法应用到MRI脑部肿瘤图像的检索.结果与结论:实验结果表明该方法能快速有效地检索出MRI脑部图像库中的肿瘤图像,检索的平均查准率为78.4%,平均查全率为62.9%.     

基于局部特征的医学图像融合方法

目的 介绍一种基于局部小波系数特征的多尺度医学图像融合方法.方法 首先对待融合的两幅医学图像做多尺度的小波分解,然后采用原始图像灰度的局部标准差作为小波系数选取的参考标准,最后再对选取的小波系数进行重构得到最终的融合图像.结果 成功将一幅MRI解剖图像和一幅SPECT功能图像融合在一起.结论 基于局部特征的医学图像融合方法是切实可行的,且简便灵活,图像融合效果较好.     

Directional wavelet based features for colonic polyp classification

In this work, various wavelet based methods like the discrete wavelet transform, the dual-tree complex wavelet transform, the Gabor wavelet transform, curvelets, contourlets and shearlets are applied for the automated classification of colonic polyps. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax’s i-Scan technology combined with or without staining the mucosa), 2 NBI high-magnification databases and one database with chromoscopy high-magnification images.To evaluate the suitability of the wavelet based methods with respect to the classification of colonic polyps, the classification performances of 3 wavelet transforms and the more recent curvelets, contourlets and shearlets are compared using a common framework. Wavelet transforms were already often and successfully applied to the classification of colonic polyps, whereas curvelets, contourlets and shearlets have not been used for this purpose so far.We apply different feature extraction techniques to extract the information of the subbands of the wavelet based methods. Most of the in total 25 approaches were already published in different texture classification contexts. Thus, the aim is also to assess and compare their classification performance using a common framework. Three of the 25 approaches are novel. These three approaches extract Weibull features from the subbands of curvelets, contourlets and shearlets. Additionally, 5 state-of-the-art non wavelet based methods are applied to our databases so that we can compare their results with those of the wavelet based methods.It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transform and the Shearlet transform. These three wavelet based transforms in combination with Weibull features even outperform the state-of-the-art methods on most of the databases. We will also show that the Weibull distribution is better suited to model the subband coefficient distribution than other commonly used probability distributions like the Gaussian distribution and the generalized Gaussian distribution.So this work gives a reasonable summary of wavelet based methods for colonic polyp classification and the huge amount of endoscopic polyp databases used for our experiments assures a high significance of the achieved results.     

A deep community based approach for large scale content based X-ray image retrieval

A computer assisted system for automatic retrieval of medical images with similar image contents can serve as an efficient management tool for handling and mining large scale data, and can also be used as a tool in clinical decision support systems. In this paper, we propose a deep community based automated medical image retrieval framework for extracting similar images from a large scale X-ray database. The framework integrates a deep learning-based image feature generation approach and a network community detection technique to extract similar images. When compared with the state-of-the-art medical image retrieval techniques, the proposed approach demonstrated improved performance. We evaluated the performance of the proposed method on two large scale chest X-ray datasets, where given a query image, the proposed approach was able to extract images with similar disease labels with a precision of 85%. To the best of our knowledge, this is the first deep community based image retrieval application on large scale chest X-ray database.     

MRI图像的小波融合处理

背景:小波图像融合是将两幅图像融合在一起,以获取对同一场景的更为精确、全面、可靠的图像描述.目的:用小波变换图像融合技术融合MRI脑梗死图像,以恢复缺损图像.方法:图像融合的主要机制是利用二维小波分析法对MRI脑梗死图像进行小波分解,并对高低频信号采用多种融合方式进行融合.通过对比不同融合方式后的效果图,找出最适合本部位MRI图像的融合方法.结果与结论:不同方式的融合技术能成功修复不同的缺损部位,多种融合方式的合适组合能完全修复多处缺失部位.对于文中给出的MRI脑梗死图像,采用最小值融合方式的融合效果最好.提示使用二维小波分析法处理医学图像,简便快捷,能有效改善图像的视觉效果,辅助临床诊断.     

MRI图像的小波融合处理

背景：小波图像融合是将两幅图像融合在一起,以获取对同一场景的更为精确、全面、可靠的图像描述。目的：用小波变换图像融合技术融合MRI脑梗死图像,以恢复缺损图像。方法：图像融合的主要机制是利用二维小波分析法对MRI脑梗死图像进行小波分解,并对高低频信号采用多种融合方式进行融合。通过对比不同融合方式后的效果图,找出最适合本部位MRI图像的融合方法。结果与结论：不同方式的融合技术能成功修复不同的缺损部位,多种融合方式的合适组合能完全修复多处缺失部位。对于文中给出的MRI脑梗死图像,采用最小值融合方式的融合效果最好。提示使用二维小波分析法处理医学图像,简便快捷,能有效改善图像的视觉效果,辅助临床诊断。     

k-means based hybrid wavelet and curvelet transform approach for denoising of remotely sensed images

This article presents a new technique for denoising of remotely sensed images based on multi-resolution analysis (MRA). Multi-resolution techniques provide a coarse-to-fine and scale-invariant decomposition of images for image processing and analysis. The multi-resolution image analysis methods have the ability to analyse the image in an adaptive manner, capturing local as well as global information. Further, noise, as one of the biggest obstacles for image analysis and for further processing, is effectively handled by multi-resolution methods. The article aims at the analysis of noise filtering of image using wavelets and curvelets methods on multispectral images acquired by the QuickBird and medium-resolution Landsat Thematic Mapper satellite systems. To improve the performance of noise filtering, an iterative thresholding scheme and a hybrid approach based on wavelet and curvelet transforms are proposed for restoring the image from its noisy version. Two comparative measures are used for evaluation of the performance of the methods for denoising. One of them is the peak signal-to-noise ratio and the second is the ability of the noise filtering scheme to preserve the sharpness of the edges. By both of these comparative measures, the hybrid approach of curvelet and wavelet for heterogeneous and homogeneous areas with iterative threshold has proved to be better than the others. Results are illustrated using QuickBird and Landsat images for proposed methods and compared with wavelets and curvelet-based denoising.     

Case-based similar image retrieval for weakly annotated large histopathological images of malignant lymphoma using deep metric learning

In the present study, we propose a novel case-based similar image retrieval (SIR) method for hematoxylin and eosin (H&E) stained histopathological images of malignant lymphoma. When a whole slide image (WSI) is used as an input query, it is desirable to be able to retrieve similar cases by focusing on image patches in pathologically important regions such as tumor cells. To address this problem, we employ attention-based multiple instance learning, which enables us to focus on tumor-specific regions when the similarity between cases is computed. Moreover, we employ contrastive distance metric learning to incorporate immunohistochemical (IHC) staining patterns as useful supervised information for defining appropriate similarity between heterogeneous malignant lymphoma cases. In the experiment with 249 malignant lymphoma patients, we confirmed that the proposed method exhibited higher evaluation measures than the baseline case-based SIR methods. Furthermore, the subjective evaluation by pathologists revealed that our similarity measure using IHC staining patterns is appropriate for representing the similarity of H&E stained tissue images for malignant lymphoma.     

Neighbourhood approximation using randomized forests

Leveraging available annotated data is an essential component of many modern methods for medical image analysis. In particular, approaches making use of the “neighbourhood” structure between images for this purpose have shown significant potential. Such techniques achieve high accuracy in analysing an image by propagating information from its immediate “neighbours” within an annotated database. Despite their success in certain applications, wide use of these methods is limited due to the challenging task of determining the neighbours for an out-of-sample image. This task is either computationally expensive due to large database sizes and costly distance evaluations, or infeasible due to distance definitions over semantic information, such as ground truth annotations, which is not available for out-of-sample images.This article introduces Neighbourhood Approximation Forests (NAFs), a supervised learning algorithm providing a general and efficient approach for the task of approximate nearest neighbour retrieval for arbitrary distances. Starting from an image training database and a user-defined distance between images, the algorithm learns to use appearance-based features to cluster images approximating the neighbourhood structured induced by the distance. NAF is able to efficiently infer nearest neighbours of an out-of-sample image, even when the original distance is based on semantic information. We perform experimental evaluation in two different scenarios: (i) age prediction from brain MRI and (ii) patch-based segmentation of unregistered, arbitrary field of view CT images. The results demonstrate the performance, computational benefits, and potential of NAF for different image analysis applications.     

Detecting the brain surface in sparse MRI using boundary models

We introduce a framework for the detection of the brain boundary (arachnoid) within sparse MRI. We use the term sparse to describe volumetric images in which the sampling resolution within the imaging plane is far higher than that of the perpendicular direction. Generic boundary detection schemes do not provide good results for such data. In the scheme we propose, the boundary is extracted using a constrained mesh surface which iteratively approximates a 3D point set consisting of detected boundary points. Boundary detection is based on a database of piecewise constant models, which represent the idealised MR intensity profile of the underlying boundary anatomy. A non-linear matching scheme is introduced to estimate the location of the boundary points using only the intensity data within each image plane. Results are shown for a number of images and are discussed in detail.     

Morphological classification of brains via high-dimensional shape transformations and machine learning methods

A high-dimensional shape transformation posed in a mass-preserving framework is used as a morphological signature of a brain image. Population differences with complex spatial patterns are then determined by applying a nonlinear support vector machine (SVM) pattern classification method to the morphological signatures. Significant reduction of the dimensionality of the morphological signatures is achieved via wavelet decomposition and feature reduction methods. Applying the method to MR images with simulated atrophy shows that the method can correctly detect subtle and spatially complex atrophy, even when the simulated atrophy represents only a 5% variation from the original image. Applying this method to actual MR images shows that brains can be correctly determined to be male or female with a successful classification rate of 97%, using the leave-one-out method. This proposed method also shows a high classification rate for old adults' age classification, even under difficult test scenarios. The main characteristic of the proposed methodology is that, by applying multivariate pattern classification methods, it can detect subtle and spatially complex patterns of morphological group differences which are often not detectable by voxel-based morphometric methods, because these methods analyze morphological measurements voxel-by-voxel and do not consider the entirety of the data simultaneously.     

基于小波变换的医学超声图像去噪及增强方法

目的探求一种基于小波变换的医学超声图像去噪及增强方法。方法提出了一种基于小波分析理论的医学超声图像噪声的综合抑制方法,首先对医学超声图像进行对数变换,将乘性噪声变成加性噪声;然后进行多尺度小波变换,将图像分解成一系列不同尺度上的小波系数,对变换后不同尺度的高频子图像进行非线性小波软阈值处理,阈值处理后的高频子图像进行增强;最后,经小波逆变换和指数变换恢复去噪后图像。结果原图像中斑纹噪声被有效去除,图像边缘细节得以保留。结论该方法可有效保留细节信号,极大限度地去除斑纹噪声。     

SAR image classification based on alpha-stable distribution

We propose a classification algorithm that utilizes the alpha-stable distribution to model the texture features of synthetic aperture radar (SAR) images. The SAR image is first decomposed by stationary wavelet transform (SWT). After that, the alpha-stable distribution is applied to model the high-frequency subband coefficients of the image at each decomposition scale. A regression-type method is then used to estimate the alpha-stable distribution parameters, which form a feature vector that fully describes the texture. Finally, a SAR image classification algorithm is derived by exploiting this feature vector based on the support vector machines (SVM) approach. Because different combinations of alpha-stable distribution parameters contribute to differences in classification precision, a multilevel SVM (MSVM) classification algorithm is also presented to address the issue. Experimental results indicate that the proposed SAR image classification algorithm is effective and the MSVM algorithm improves the classification performance. Moreover, our proposed algorithm has low computational cost as only a small number of the alpha-stable distribution parameters are processed.     

3D actin network centerline extraction with multiple active contours

Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and actin cables. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we propose a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D Total Internal Reflection Fluorescence Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy. Quantitative evaluation of the method using synthetic images shows that for images with SNR above 5.0, the average vertex error measured by the distance between our result and ground truth is 1 voxel, and the average Hausdorff distance is below 10 voxels.     

基于小波变换的PET/CT图像融合算法研究进展

随着图像融合技术在影像学领域的不断发展,越来越多新的影像技术及成像设备出现。PET/CT将PET和CT两种模态的医学图像进行融合,优势互补,提高了医师诊断疾病的效率及准确率。小波变换在医学图像融合中有重要作用,基于小波变换的算法使融合后图像的细节更加清晰而易于诊断,已成为近年来医学图像融合领域中研究的重点。本文对基于小波变换的PET/CT图像融合算法的研究进展进行综述。     

ImUnity: A generalizable VAE-GAN solution for multicenter MR image harmonization

ImUnity is an original 2.5D deep-learning model designed for efficient and flexible MR image harmonization. A VAE-GAN network, coupled with a confusion module and an optional biological preservation module, uses multiple 2D slices taken from different anatomical locations in each subject of the training database, as well as image contrast transformations for its training. It eventually generates ‘corrected’ MR images that can be used for various multi-center population studies. Using 3 open source databases (ABIDE, OASIS and SRPBS), which contain MR images from multiple acquisition scanner types or vendors and a large range of subjects ages, we show that ImUnity: (1) outperforms state-of-the-art methods in terms of quality of images generated using traveling subjects; (2) removes sites or scanner biases while improving patients classification; (3) harmonizes data coming from new sites or scanners without the need for an additional fine-tuning and (4) allows the selection of multiple MR reconstructed images according to the desired applications. Tested here on T1-weighted images, ImUnity could be used to harmonize other types of medical images.     

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms

Purpose

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms.

Methods

A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details.

Results

The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations.

Conclusion

In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.