Progress report of the TUDAB project for automated cancer cell detection.

Two methods for high resolution cell image data acquisition are applied routinely. Cells are either scanned by a computer controlled fast scanning microscope photometer (SMP) or a TV-camera. The software system for digital image analysis was completely revised and implemented on the PR 330 minicomputer. The system contains codes for primary cell data acquisition, segmentation of cells, cell feature extraction and statistical cell analysis. With this system, SMP and TV scanned cell data bases of PAP stained cells in vaginal smears, grouped into several classes, have been built up. Each data base contains 34 primary features and 20 feature combinations for each cell. A linear discriminant analysis is applied routinely for cell classification. The present state of the system and its operation are described, cell features and classification results are shown, and future steps for a prescreening strategy are discussed.     

Local Coding Based Matching Kernel Method for Image Classification

This paper mainly focuses on how to effectively and efficiently measure visual similarity for local feature based representation. Among existing methods, metrics based on Bag of Visual Word (BoV) techniques are efficient and conceptually simple, at the expense of effectiveness. By contrast, kernel based metrics are more effective, but at the cost of greater computational complexity and increased storage requirements. We show that a unified visual matching framework can be developed to encompass both BoV and kernel based metrics, in which local kernel plays an important role between feature pairs or between features and their reconstruction. Generally, local kernels are defined using Euclidean distance or its derivatives, based either explicitly or implicitly on an assumption of Gaussian noise. However, local features such as SIFT and HoG often follow a heavy-tailed distribution which tends to undermine the motivation behind Euclidean metrics. Motivated by recent advances in feature coding techniques, a novel efficient local coding based matching kernel (LCMK) method is proposed. This exploits the manifold structures in Hilbert space derived from local kernels. The proposed method combines advantages of both BoV and kernel based metrics, and achieves a linear computational complexity. This enables efficient and scalable visual matching to be performed on large scale image sets. To evaluate the effectiveness of the proposed LCMK method, we conduct extensive experiments with widely used benchmark datasets, including 15-Scenes, Caltech101/256, PASCAL VOC 2007 and 2011 datasets. Experimental results confirm the effectiveness of the relatively efficient LCMK method.     

Application of LANDSAT MSS Digital Image Processing Techniques in Investigation of Aquatic Plant Distribution in Honghu Lake

In this paper an experiment in the application of LANDSAT MSS digital image proces. sing technique to classify the aquatic plants in the Honghu Lake, China, as well as the necessary ground feature spectrum measurement and plant chlorophyll content determination techniques are introduced. In order to obtain the expected computer aided classification result different methods of digital image feature extraction have been tried, namely the ratio transformation, the biomass index transformation and the linear stretching of image intensity. The data from spectrum measurement and chlorophyll content determination were used to distinguish the attributions of different classes of aquatic plant associations from one another by comparing with their spectrum response intensity values on different LANDSAT band images. The original LANDSAT MSS images were digitally rectified by control points to a certain map projection system using a general polynomial approach scheme before the classifying activities, so that the classification result may be transfered to an existing map. In this paper the resulting colourassigued image and the thematic map of Honghu Lakes aquatic plant (association) distribution are included, the areas of different classes of aquatic plants are listed and the reliability of the resulting classification and pattern recognition are analysed. These results will provide useful informations for the investigation of the present situation as well as the historical succession of the Honghu Lake and its aquatic plant distribution, and also for the research works of the lake management.     

Wood Recognition Using Image Texture Features

Inspired by theories of higher local order autocorrelation (HLAC), this paper presents a simple, novel, yet very powerful approach for wood recognition. The method is suitable for wood database applications, which are of great importance in wood related industries and administrations. At the feature extraction stage, a set of features is extracted from Mask Matching Image (MMI). The MMI features preserve the mask matching information gathered from the HLAC methods. The texture information in the image can then be accurately extracted from the statistical and geometrical features. In particular, richer information and enhanced discriminative power is achieved through the length histogram, a new histogram that embodies the width and height histograms. The performance of the proposed approach is compared to the state-of-the-art HLAC approaches using the wood stereogram dataset ZAFU WS 24. By conducting extensive experiments on ZAFU WS 24, we show that our approach significantly improves the classification accuracy.     

使用Radon变换进行二维MRI图像配准

使用了一种基于Radon变换的技术来进行二维的MRI图像配准。MRI的图像配准一般使用灰度配准,而Radon变换一般用于CT图像的重建,虽然现已经存在使用Radon变换进行图像配准,但是比较繁琐,我们对这一配准算法进行了简化。     

An Efficient Approach for Differentiating Alzheimer's Disease from Normal Elderly Based on Multicenter MRI Using Gray-Level Invariant Features

Machine learning techniques, along with imaging markers extracted from structural magnetic resonance images, have been shown to increase the accuracy to differentiate patients with Alzheimer''s disease (AD) from normal elderly controls. Several forms of anatomical features, such as cortical volume, shape, and thickness, have demonstrated discriminative capability. These approaches rely on accurate non-linear image transformation, which could invite several nuisance factors, such as dependency on transformation parameters and the degree of anatomical abnormality, and an unpredictable influence of residual registration errors. In this study, we tested a simple method to extract disease-related anatomical features, which is suitable for initial stratification of the heterogeneous patient populations often encountered in clinical data. The method employed gray-level invariant features, which were extracted from linearly transformed images, to characterize AD-specific anatomical features. The intensity information from a disease-specific spatial masking, which was linearly registered to each patient, was used to capture the anatomical features. We implemented a two-step feature selection for anatomic recognition. First, a statistic-based feature selection was implemented to extract AD-related anatomical features while excluding non-significant features. Then, seven knowledge-based ROIs were used to capture the local discriminative powers of selected voxels within areas that were sensitive to AD or mild cognitive impairment (MCI). The discriminative capability of the proposed feature was measured by its performance in differentiating AD or MCI from normal elderly controls (NC) using a support vector machine. The statistic-based feature selection, together with the knowledge-based masks, provided a promising solution for capturing anatomical features of the brain efficiently. For the analysis of clinical populations, which are inherently heterogeneous, this approach could stratify the large amount of data rapidly and could be combined with more detailed subsequent analyses based on non-linear transformation.     

A comparison of seven methods of within-subjects rigid-body pedobarographic image registration

Image registration, the process of transforming images such that homologous structures optimally overlap, provides the pre-processing foundation for pixel-level functional image analysis. The purpose of this study was to compare the performances of seven methods of within-subjects pedobarographic image registration: (1) manual, (2) principal axes, (3) centre of pressure trajectory, (4) mean squared error, (5) probability-weighted variance, (6) mutual information, and (7) exclusive OR. We assumed that foot-contact geometry changes were negligibly small trial-to-trial and thus that a rigid-body transformation could yield optimum registration performance. Thirty image pairs were randomly selected from our laboratory database and were registered using each method. To compensate for inter-rater variability, the mean registration parameters across 10 raters were taken as representative of manual registration. Registration performance was assessed using four dissimilarity metrics (#4-7 above). One-way MANOVA found significant differences between the methods (p<0.001). Bonferroni post-hoc tests revealed that the centre of pressure method performed the poorest (p<0.001) and that the principal axes method tended to perform more poorly than remaining methods (p<0.070). Average manual registration was not different from the remaining methods (p=1.000). The results suggest that a variety of linear registration methods are appropriate for within-subjects pedobarographic images, and that manual image registration is a viable alternative to algorithmic registration when parameters are averaged across raters. The latter finding, in particular, may be useful for cases of image peculiarities resulting from outlier trials or from experimental manipulations that induce substantial changes in contact area or pressure profile geometry.     

医学图像融合配准技术

图像融合技术在现代医学中扮演着极其重要的角色,是现代医学图像技术研究的重点。图像融合技术中,图像的配准又是其中的重点、难点和热点。本文按照图像变换特性对图像配准进行了分类,对每个类别的不同配准方法(特征点的获取、图像配准的变换等)进行介绍。但是,图像配准是一个尚处在发展阶段的学科,实现配准的精确化、快速化、自动化仍需要进一步的努力。     

基于图像特征的实蝇自动识别初步研究

针对实蝇图象识别,提出了基于局部模板匹配和分层约束的虫身局部特征检测的算法,该算法将虫身局部特征搜索分为三个层次:首先是图像中的实蝇虫身检测,找到实蝇图像上虫身的大概位置;其次是用虫身明显特征模板作虫身检测;最后根据不同实蝇的典型特征区别出这些实蝇所属的不同类别.在图像预处理时采用主成分分析(PCA)方法进行主轴定位并旋转归一化处理.在对虫身翅膀上的明显特征进行检测时,提出了翅膀斑纹模板匹配算法.     

Compounding Local Invariant Features and Global Deformable Geometry for Medical Image Registration

Using deformable models to register medical images can result in problems of initialization of deformable models and robustness and accuracy of matching of inter-subject anatomical variability. To tackle these problems, a novel model is proposed in this paper by compounding local invariant features and global deformable geometry. This model has four steps. First, a set of highly-repeatable and highly-robust local invariant features, called Key Features Model (KFM), are extracted by an effective matching strategy. Second, local features can be matched more accurately through the KFM for the purpose of initializing a global deformable model. Third, the positional relationship between the KFM and the global deformable model can be used to precisely pinpoint all landmarks after initialization. And fourth, the final pose of the global deformable model is determined by an iterative process with a lower time cost. Through the practical experiments, the paper finds three important conclusions. First, it proves that the KFM can detect the matching feature points well. Second, the precision of landmark locations adjusted by the modeled relationship between KFM and global deformable model is greatly improved. Third, regarding the fitting accuracy and efficiency, by observation from the practical experiments, it is found that the proposed method can improve % of the fitting accuracy and reduce around 50% of the computational time compared with state-of-the-art methods.     

Automated multidimensional single molecule fluorescence microscopy feature detection and tracking

Characterisation of multi-protein interactions in cellular networks can be achieved by optical microscopy using multidimensional single molecule fluorescence imaging. Proteins of different species, individually labelled with a single fluorophore, can be imaged as isolated spots (features) of different colour light in different channels, and their diffusive behaviour in cells directly measured through time. Challenges in data analysis have, however, thus far hindered its application in biology. A set of methods for the automated analysis of multidimensional single molecule microscopy data from cells is presented, incorporating Bayesian segmentation-based feature detection, image registration and particle tracking. Single molecules of different colours can be simultaneously detected in noisy, high background data with an arbitrary number of channels, acquired simultaneously or time-multiplexed, and then tracked through time. The resulting traces can be further analysed, for example to detect intensity steps, count discrete intensity levels, measure fluorescence resonance energy transfer (FRET) or changes in polarisation. Examples are shown illustrating the use of the algorithms in investigations of the epidermal growth factor receptor (EGFR) signalling network, a key target for cancer therapeutics, and with simulated data.     

A Practical Approach Based on Analytic Deformable Algorithm for Scenic Image Registration

Background

Image registration is to produce an entire scene by aligning all the acquired image sequences. A registration algorithm is necessary to tolerance as much as possible for intensity and geometric variation among images. However, captured image views of real scene usually produce unexpected distortions. They are generally derived from the optic characteristics of image sensors or caused by the specific scenes and objects.

Methods and Findings

An analytic registration algorithm considering the deformation is proposed for scenic image applications in this study. After extracting important features by the wavelet-based edge correlation method, an analytic registration approach is then proposed to achieve deformable and accurate matching of point sets. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based Levenberg-Marquardt (FLM) method. It converges evidently faster than most other methods because of its feature-based characteristic.

Conclusions

We validate the performance of proposed method by testing with synthetic and real image sequences acquired by a hand-held digital still camera (DSC) and in comparison with an optical flow-based motion technique in terms of the squared sum of intensity differences (SSD) and correlation coefficient (CC). The results indicate that the proposed method is satisfactory in the registration accuracy and quality of DSC images.     

Hierarchical Rigid Registration of Femur Surface Model Based on Anatomical Features

Existing model registration of individual bones does not have a high certainly of success due to the lack of anatomic semantic. In light of the surface anatomy and functional structure of bones, we hypothesized individual femur models would be aligned through feature points both in geometrical level and in anatomic level, and proposed a hierarchical approach for the rigid registration (HRR) of point cloud models of femur with high resolution. Firstly, a coarse registration between two simplified point cloud models was implemented based on the extraction of geometric feature points (GFPs); and then, according to the anatomic feature points (AFPs) in two level namely shape features and structure features, the fine weight-based registration was performed to achieve anatomical alignment; finally, the origin source model was automatically transformed by applying the obtained coarse matrix and fine one in sequence. Experimental results show that the hierarchical registration method can rapidly and accurately register point clouds of individual femurs, and achieves the medical semantic alignment, and provides a basic tool for the understanding and comparison of femur anatomy and structure.     

A Novel Method of Object Detection from a Moving Camera Based on Image Matching and Frame Coupling

A new method based on image matching and frame coupling to handle the problems of object detection caused by a moving camera and object motion is presented in this paper. First, feature points are extracted from each frame. Then, motion parameters can be obtained. Sub-images are extracted from the corresponding frame via these motion parameters. Furthermore, a novel searching method for potential orientations improves efficiency and accuracy. Finally, a method based on frame coupling is adopted, which improves the accuracy of object detection. The results demonstrate the effectiveness and feasibility of our proposed method for a moving object with changing posture and with a moving camera.     

Interpretability of bi-level variable selection methods

Variable selection is usually performed to increase interpretability, as sparser models are easier to understand than full models. However, a focus on sparsity is not always suitable, for example, when features are related due to contextual similarities or high correlations. Here, it may be more appropriate to identify groups and their predictive members, a task that can be accomplished with bi-level selection procedures. To investigate whether such techniques lead to increased interpretability, group exponential LASSO (GEL), sparse group LASSO (SGL), composite minimax concave penalty (cMCP), and least absolute shrinkage, and selection operator (LASSO) as reference methods were used to select predictors in time-to-event, regression, and classification tasks in bootstrap samples from a cohort of 1001 patients. Different groupings based on prior knowledge, correlation structure, and random assignment were compared in terms of selection relevance, group consistency, and collinearity tolerance. The results show that bi-level selection methods are superior to LASSO in all criteria. The cMCP demonstrated superiority in selection relevance, while SGL was convincing in group consistency. An all-round capacity was achieved by GEL: the approach jointly selected correlated and content-related predictors while maintaining high selection relevance. This method seems recommendable when variables are grouped, and interpretation is of primary interest.     

Spatial registration of data on gene expression in situ]

For registering data on the in situ expression of segmentation genes, a method of image registration was developed basing on the spline approximation. The reference points for the registration were the coordinates of extrema in one-dimensional patterns of gene expression. This registration method is characterized by a very high accuracy. A method of creating a generalized pattern of gene expression in single cells is proposed. Such patterns were constructed for nine segmentation genes belonging to the gap and pair-rule classes of genes.