Computerized detection of malignant tumors on digital mammograms

This paper presents a tumor detection system for fully digital mammography. The processing scheme adopted in the proposed system focuses on the solution of two problems. One is how to detect tumors as suspicious regions with a very weak contrast to their background and another is how to extract features which characterize malignant tumors. For the first problem, a unique adaptive filter called the iris filter is proposed. It is very effective in enhancing approximately rounded opacities no matter what their contrasts might be. Clues for differentiation between malignant tumors and other tumors are believed to be mostly in their border areas. This paper proposes typical parameters which reflect boundary characteristics. To confirm the system performance for unknown samples, large scale experiments using 1212 CR images were performed. The results showed that the sensitivity of the proposed system was 90.5% and the average number of false positives per image was found to be only 1.3. These results show the effectiveness of the proposed system.     

Classification-driven watershed segmentation.

This paper presents a novel approach for creation of topographical function and object markers used within watershed segmentation. Typically, marker-driven watershed segmentation extracts seeds indicating the presence of objects or background at specific image locations. The marker locations are then set to be regional minima within the topological surface (typically, the gradient of the original input image), and the watershed algorithm is applied. In contrast, our approach uses two classifiers, one trained to produce markers, the other trained to produce object boundaries. As a result of using machine-learned pixel classification, the proposed algorithm is directly applicable to both single channel and multichannel image data. Additionally, rather than flooding the gradient image, we use the inverted probability map produced by the second aforementioned classifier as input to the watershed algorithm. Experimental results demonstrate the superior performance of the classification-driven watershed segmentation algorithm for the tasks of 1) image-based granulometry and 2) remote sensing.     

A downstream algorithm based on extended gradient vector flow field for object segmentation

For object segmentation, traditional snake algorithms often require human interaction; region growing methods are considerably dependent on the selected homogeneity criterion and initial seeds; watershed algorithms, however, have the drawback of over segmentation. A new downstream algorithm based on a proposed extended gradient vector flow (E-GVF) field model is presented in this paper for multiobject segmentation. The proposed flow field, on one hand, diffuses and propagates gradients near object boundaries to provide an effective guiding force and, on the other hand, presents a higher resolution of direction than traditional GVF field. The downstream process starts with a set of seeds scored and selected by considering local gradient direction information around each pixel. This step is automatic and requires no human interaction, making our algorithm more suitable for practical applications. Experiments show that our algorithm is noise resistant and has the advantage of segmenting objects that are separated from the background, while ignoring the internal structures of them. We have tested the proposed algorithm with several realistic images (e.g., medical and complex background images) and gained good results.     

基于梯度信息融合的海面红外目标实时检测

针对海面红外序列图像的特点,提出一种适用于海天背景和海岸背景的目标检测方法。该方法不需要进行特殊的预处理来滤除图像噪声,首先量化子图像的区域复杂度以及单元区域上下邻域的灰度差异,从而预测出海界限区域的位置;然后利用改进Canny算子提取轮廓边缘,突出海界线特征,降低其他纹理的干扰,再进行Hough变换,提取出海界线;最后提出泛化梯度概念,通过融合的梯度信息,联合海界线的位置,进行海面目标标记。实验表明,所提方法能准确地定位海界限区域以及水平或倾斜的海界线,并快速精确地提取红外海面目标。整个过程的平均花费时间约为60 ms,具备很好的鲁棒性和实时性。     

Multiple contour extraction from graylevel images using an artificial neural network.

For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of an edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.     

Adaptive Combination of Proportionate NSAF with the Tap-Weights Feedback for Acoustic Echo Cancellation

To obtain fast convergence rate and low steady-state error in acoustic echo cancellation, a convex combination scheme of the improved proportionate normalized subband adaptive filter algorithm is proposed. Instead of the gradient method in the conventional combination theory, the mixing parameter is adapted by using the normalized gradient method which makes it more robust to the variations of subband error signals. Also, to implement a smooth transition from the fast filter to the accurate filter, a cyclic feedback of the overall tap-weights giving to all component filters is applied.     

Multipath reduction using constant modulus conjugate gradienttechniques

A conjugate-gradient (CG) constant-modulus adaptive processor is proposed. For the generalized sidelobe canceler (GSC) signal processing configuration, this algorithm, CG-GSC, exhibits improved convergence over previous methods. Theoretical expressions are presented for convergence and weight update of a linearly constrained constant modulus generalized sidelobe canceler. Theoretical expressions are then derived for the conjugate direction vectors. These vectors are used to update the filter weights for a conjugate gradient adaptation rule. A simulation study of the conjugate adaptation rule reveals the increase in convergence rate for the generalized sidelobe canceler. Performance comparisons of the CG-GSC and a first-order gradient GSC for a BPSK signal with multipath and white noise interference indicate that the CG-GSC adaptation rule not only increases convergence by a factor of five compared to the first-order gradient GSC, but in some instances improves the bit error rate of the demodulated BPSK signal     

Nonlinear anisotropic filtering of MRI data

In contrast to acquisition-based noise reduction methods a postprocess based on anisotropic diffusion is proposed. Extensions of this technique support 3-D and multiecho magnetic resonance imaging (MRI), incorporating higher spatial and spectral dimensions. The procedure overcomes the major drawbacks of conventional filter methods, namely the blurring of object boundaries and the suppression of fine structural details. The simplicity of the filter algorithm permits an efficient implementation, even on small workstations. The efficient noise reduction and sharpening of object boundaries are demonstrated by applying this image processing technique to 2-D and 3-D spin echo and gradient echo MR data. The potential advantages for MRI, diagnosis, and computerized analysis are discussed in detail.     

Contour extraction in medical images using initial boundary pixel selection and segmental contour following

In today??s health care, an imaging system plays an important role throughout the entire clinical process from diagnosis and treatment planning to surgical procedures and follow-up studies of disease. Boundary detection is a technique used to segment an object within a region of interest in the medical image for further clinical applications. Contour extraction is one of the most important boundary detection methods. In this paper, an object contour extraction for gray-level medical images using automatic initial boundary pixel selection and tracing of a segmental contour based on the boundary pixels obtained by the initial boundary pixel selection is proposed. Experimental results on artificial images of convex and deep concave objects, and real CT and MRI images show that, in comparing with other existing methods, a more detailed and accurate contour can be obtained using the proposed object contour extraction method. This has low computational complexity, which will benefit applications to clinical diagnosis, treatment, surgery, and follow up studies.     

基于显著性映射与兴趣点凸壳的图像融合算法

提出了一种基于显著性特征的可见光与红外图像融合算法来改善目标的融合质量.引入显著检测器对红外图像进行处理,生成显著映射;进一步分析红外图像并检测兴趣点,提取图像中的显著兴趣点;通过计算显著兴趣点的凸壳确定显著区域;利用显著兴趣点凸壳对初始显著映射进行优化,使目标定位更加精确.根据区域映射获取可见光图像的背景区域;根据不同的融合准则对目标、背景区域进行融合,获得最终的融合图像.结果表明与当前可见光图像融合技术相比,所提算法在标准差、联合熵与边缘信息因子等指标方面具有优势,其融合图像的细节纹理更清晰.     

Fast segmentation of porcelain images based on texture features

This study aims to segment objects within images of porcelain artifacts to help users retrieve the images in an efficient and convenient manner. Through digital archiving, a tremendous number of porcelain images have been created. To avoid interference due to the image’s background during the retrieval process, it is necessary to segment objects in advance to accommodate high-precision image retrieval. In the proposed segmentation process, four texture features, including coarseness, contrast, directionality, and gradient, are first obtained. The morphological processing, which involves PCA (principal component analysis), Otsu’s method, and object filter for opening and closing operation, is applied. Finally, regarding the objects selected by object filter, boundary extraction and watershed segmentation are performed to segment the porcelain objects from the background. In our image segmentation experiment using images of Chinese porcelain from various dynasties, featuring various shapes and colors, complete and accurate segmentation results are produced. The results can be used as a reference for future identification of the era to which the artifacts belong, and also to lay a foundation for future development of porcelain image retrieval techniques as a benefit to academic research.     

Region-based contrast enhancement of mammograms

Diagnostic features in mammograms vary widely in size and shape. Classical image enhancement techniques cannot adapt to the varying characteristics of such features. An adaptive method for enhancing the contrast of mammographic features of varying size and shape is presented. The method uses each pixel in the image as a seed to grow a region. The extent and shape of the region adapt to local image gray-level variations, corresponding to an image feature. The contrast of each region is calculated with respect to its individual background. Contrast is then enhanced by applying an empirical transformation based on each region's seed pixel value, its contrast, and its background. A quantitative measure of image contrast improvement is also defined based on a histogram of region contrast and used for comparison of results. Using mammogram images digitized at high resolution (less than 0.1 mm pixel size), it is shown that the validity of microcalcification clusters and anatomic details is considerably improved in the processed images.     

Image segmentation and analysis via multiscale gradient watershedhierarchies

Multiscale image analysis has been used successfully in a number of applications to classify image features according to their relative scales. As a consequence, much has been learned about the scale-space behavior of intensity extrema, edges, intensity ridges, and grey-level blobs. We investigate the multiscale behavior of gradient watershed regions. These regions are defined in terms of the gradient properties of the gradient magnitude of the original image. Boundaries of gradient watershed regions correspond to the edges of objects in an image. Multiscale analysis of intensity minima in the gradient magnitude image provides a mechanism for imposing a scale-based hierarchy on the watersheds associated with these minima. This hierarchy can be used to label watershed boundaries according to their scale. This provides valuable insight into the multiscale properties of edges in an image without following these curves through scale-space. In addition, the gradient watershed region hierarchy can be used for automatic or interactive image segmentation. By selecting subtrees of the region hierarchy, visually sensible objects in an image can be easily constructed.     

Contrast-to-gradient method for the evaluation of image resolution in scanning electron microscopy

In a previous study, we proposed the contrast-to-gradient (CG) method for evaluating image resolution. Here, the CG resolution is defined as a weighted harmonic mean of the local resolution, which is proportional to the quotient of the threshold contrast divided by the local gradient. The local gradient is calculated from the quadratic function that best fits the local pixel intensities over the region of interest (ROI) of 3 x 3 or 5 x 5 pixels in size. To refine the CG method, some modifications are carried out in the present study. Directional resolutions are employed to evaluate images, including astigmatism or strongly directional patterns as well as isotropic patterns. Here, CG resolution is redefined so as to keep the same value even for the image reversed in black-and-white contrast, because of no difference in the image information during contrast reversing. Besides, CG resolution is characterized to be independent of the brightness/contrast change unless these changes do not bring about both cut-off and saturation in the pixel intensities. Dependencies of the denoising effect and the resolution accuracy on ROI size are demonstrated as a function of image-noise.     

Target signature-constrained mixed pixel classification forhyperspectral imagery

Linear spectral mixture analysis has been widely used for subpixel detection and mixed pixel classification. When it is implemented as constrained LSMA, the constraints are generally imposed on abundance fractions in the mixture. In this paper, we consider an alternative approach, which imposes constraints on target signature vectors rather than target abundance fractions. The idea is to constrain directions of target signature vectors of interest in two different ways. One, referred to as linearly constrained minimum variance approach develops a linear filter to constrain these target signature vectors along preassigned directions using a set of specific filter gains while minimizing the filter output variance. Another, referred to as the linearly constrained discriminant analysis (LCDA), is derived from Fisher's linear discriminant analysis, but constrains the Fisher's discriminant vectors along predetermined directions to improve classification performance. Recently, Bowles et al. introduced another target signature-constrained approach, referred to as filter-vectors method, which requires a linear mixture model to implement constraints on target signature vectors. Interestingly, it turns out that the filter-vectors method can be considered as a special version of both linearly constrained minimum variance and linearly constrained discriminant analysis approaches     

A novel iris segmentation using radial-suppression edge detection

Iris segmentation is a key step in the iris recognition system. The conventional methods of iris segmentation are based on the assumption that the inner and outer boundaries of an iris can be taken as circles. The region of the iris is segmented by detecting the circular inner and outer boundaries. However, we investigate the iris boundaries in the CASIA-IrisV3 database, and find that the actual iris boundaries are not always circular. In order to solve this problem, a new approach for iris segmentation based on radial-suppression edge detection is proposed in this paper. In the radial-suppression edge detection, a non-separable wavelet transform is used to extract the wavelet transform modulus of the iris image. Then, a new method of radial non-maxima suppression is proposed to retain the annular edges and simultaneously remove the radial edges. Next, a thresholding operation is utilized to remove the isolated edges and produce the final binary edge map. Based on the binary edge map, a self-adaptive method of iris boundary detection is proposed to produce final iris boundaries. Experimental results demonstrate that the proposed iris segmentation is desirable.     

An enhanced NAS-RIF algorithm for blind image deconvolution

We enhance the performance of the nonnegativity and support constraints recursive inverse filtering (NAS-RIF) algorithm for blind image deconvolution. The original cost function is modified to overcome the problem of operation on images with different scales for the representation of pixel intensity levels. Algorithm resetting is used to enhance the convergence of the conjugate gradient algorithm. A simple pixel classification approach is used to automate the selection of the support constraint. The performance of the resulting enhanced NAS-RIF algorithm is demonstrated on various images.     

Region growing: a new approach

A new region growing method for finding the boundaries of blobs is presented. A unique feature of the method is that at each step, at most one pixel exhibits the required properties to join the region. The method uses two novel discontinuity measures, average contrast and peripheral contrast, to control the growing process.     

基于协方差矩阵的复杂背景中目标检测

在视频的运动目标检测中,有很多经典方法,但是大部分都不能完整地从复杂背景中检测出运动目标。提出了一种运动目标检测的新算法——协方差矩阵算法,它集成了像素级和区域级2种不同的层次。在像素级中,空间的属性主要来之于像素的坐标值、亮度、纹理和梯度等。在区域级中,通过计算一个像素周围矩形区域的协方差矩阵来表示像素间的特征相关性。实验结果表明,该方法具有较强的鲁棒性,明显优于传统的目标检测算法。     

虹膜图像的质量评价研究

对虹膜识别系统来说,所采集虹膜图像的质量好坏直接影响识别是否成功。本文针对散焦模糊和眼睑睫毛遮挡问题提出了一种新的虹膜图像质量评价方法:判断图像是否散焦模糊采用了LoG算子提取瞳孔两侧局部虹膜的高频能量,判断虹膜是否有眼睑严重遮挡采用了计算瞳孔上方指定矩形区域的灰度均值,同时计算该区域的水平梯度来衡量虹膜受睫毛遮挡的程度。仿真实验表明,算法的平均处理速度0.020 9s/帧,满足实时虹膜识别系统的需求,是一种快速有效的虹膜图像质量评价算法。