An adaptive density-weighted contrast enhancement filter for mammographic breast mass detection

Presents a novel approach for segmentation of suspicious mass regions in digitized mammograms using a new adaptive density-weighted contrast enhancement (DWCE) filter in conjunction with Laplacian-Gaussian (LG) edge detection. The DWCE enhances structures within the digitized mammogram so that a simple edge detection algorithm can be used to define the boundaries of the objects. Once the object boundaries are known, morphological features are extracted and used by a classification algorithm to differentiate regions within the image. This paper introduces the DWCE algorithm and presents results of a preliminary study based on 25 digitized mammograms with biopsy proven masses. It also compares morphological feature classification based on sequential thresholding, linear discriminant analysis, and neural network classifiers for reduction of false-positive detections.     

An evaluation of the effectiveness of adaptive histogram equalization for contrast enhancement

Adaptive histogram equalization (AHE) and intensity windowing have been compared using psychophysical observer studies. Experienced radiologists were shown clinical CT (computerized tomographic) images of the chest. Into some of the images, appropriate artificial lesions were introduced; the physicians were then shown the images processed with both AHE and intensity windowing. They were asked to assess the probability that a given image contained the artificial lesion, and their accuracy was measured. The results of these experiments show that for this particular diagnostic task, there was no significant difference in the ability of the two methods to depict luminance contrast; thus, further evaluation of AHE using controlled clinical trials is indicated.     

Image quality assessment for contrast enhancement evaluation

Although the contrast enhancement (CE) is a great challenge, few efforts have been conducted on evaluation of the contrast changes. In this paper, we propose a contrast-changed image quality (CCIQ) metric including a local index, named edge-based contrast criterion (ECC), and three global measures. In the global measures, entropy, correlation coefficient and mean intensity are exploited. Particle swarm optimization (PSO) algorithm is utilized for obtaining an optimal combination of these quantities. Although the presented method utilizes the original image, it cannot be considered as a full-reference metric, since the original image is not regarded to have the ideal quality. Hence, it can be concluded that it follows a new paradigm in image quality assessment. Experimental results on the three benchmark databases, CID2013, TID2013 and TID2008 demonstrate that the proposed metric outperforms the-state-of-the-art methods.     

Quantitative evaluation of enhancement techniques

Enhancement techniques are often used in image processing. In this paper, a quantitative measure of the image quality through evaluation of the coefficient of information content and the entropy has been suggested to evaluate the effect of enhancement. The image has been assumed to be a sample function of a homogeneous random field and the pixel values are estimated from the ‘past’ pixel values. The difference between the estimated value and the actual value of the pixel has been used as the criterion for defining the coefficient of information content. Also, the entropy obtained using the co-occurrance matrix of the image, has been used as a quantitative measure of the image quality. Measurements of the image quality through the evaluation of the coefficient of information content and the entropy have been carried out for the test images and the results of these measurements have been presented.     

一种自适应提升视频图像对比度的算法

面向数字电视视频后处理芯片中提高对比度的需求,针对传统直方图均衡技术偏离原始平均亮度,放大背景噪声的缺点,提出了一种基于改进的直方图均衡的视频图像对比度提升技术.首先改善统计直方图的方法,将得到的累积直方图限制在一定范围内;然后根据平均亮度计算均衡系数;最后根据累积直方图和均衡系数,得到变换函数曲线并进行映射.仿真结果表明,应用该方法处理后的视频图像,在提升图像对比度的同时,既保持了原始图像的平均亮度,又没有增强背景噪声.     

Gradient and texture analysis for the classification of mammographic masses

Computer-aided classification of benign and malignant masses on mammograms is attempted in this study by computing gradient-based and texture-based features. Features computed based on gray-level co-occurrence matrices (GCMs) are used to evaluate the effectiveness of textural information possessed by mass regions in comparison with the textural information present in mass margins. A method involving polygonal modeling of boundaries is proposed for the extraction of a ribbon of pixels across mass margins. Two gradient-based features are developed to estimate the sharpness of mass boundaries in the ribbons of pixels extracted from their margins. A total of 54 images (28 benign and 26 malignant) containing 39 images from the Mammographic Image Analysis Society (MIAS) database and 15 images from a local database are analyzed. The best benign versus malignant classification of 82.1%, with an area (Az) of 0.85 under the receiver operating characteristics (ROC) curve, was obtained with the images from the MIAS database by using GCM-based texture features computed from mass margins. The classification method used is based on posterior probabilities computed from Mahalanobis distances. The corresponding accuracy using jack-knife classification was observed to be 74.4%, with Az = 0.67. Gradient-based features achieved Az = 0.6 on the MIAS database and Az = 0.76 on the combined database. The corresponding values obtained using jack-knife classification were observed to be 0.52 and 0.73 for the MIAS and combined databases, respectively.     

一种改进的低对比度图像增强算法

为提升图像对比度,增强图像细节,抑制图像噪声,在认真研究图像增强的基础上,对图像进行小波变换,低频子带系数采用广义模糊算子进行处理,能够更大程度地提升图像对比度和局部亮度.采用贝叶斯萎缩阈值算法将高频子带系数分为噪声和细节信息,通过非线性增益函数抑制噪声并放大细节信息.对传统非线性增益函数进行改进,引入调节因子α,以实现不同程度的细节增强.同时根据信息熵来选取非线性增益函数中参数c的值,以提高算法的自适应性.仿真结果表明,所提算法取得了较高的信息熵、峰值信噪比、清晰度和对比度,图像增强质量较好.     

Fused logarithmic transform for contrast enhancement

Presented is a new version of logarithmic transform for image enhancement: fused logarithmic transform (fLog). Based on multi-resolution spline fusion technology, a composite image with a significant improvement in image contrast can be synthesised by fusing the source image and its logarithmic version. Cascaded with other image enhancement techniques, such as histogram equalisation, the fused logarithmic transform could turn the enhanced contrast into more visible details.     

An efficient contrast enhancement method using repulsive force of edges

Multidimensional Systems and Signal Processing - This paper presents a novel algorithm which employs edge information to automatically improve the quality of low contrast images. This algorithm...     

A multiple circular path convolution neural network system for detection of mammographic masses

A multiple circular path convolution neural network (MCPCNN) architecture specifically designed for the analysis of tumor and tumor-like structures has been constructed. We first divided each suspected tumor area into sectors and computed the defined mass features for each sector independently. These sector features were used on the input layer and were coordinated by convolution kernels of different sizes that propagated signals to the second layer in the neural network system. The convolution kernels were trained, as required, by presenting the training cases to the neural network. In this study, randomly selected mammograms were processed by a dual morphological enhancement technique. Radiodense areas were isolated and were delineated using a region growing algorithm. The boundary of each region of interest was then divided into 36 sectors using 36 equi-angular dividers radiated from the center of the region. A total of 144 Breast Imaging-Reporting and Data System-based features (i.e., four features per sector for 36 sectors) were computed as input values for the evaluation of this newly invented neural network system. The overall performance was 0.78-0.80 for the areas (Az) under the receiver operating characteristic curves using the conventional feed-forward neural network in the detection of mammographic masses. The performance was markedly improved with Az values ranging from 0.84 to 0.89 using the MCPCNN. This paper does not intend to claim the best mass detection system. Instead it reports a potentially better neural network structure for analyzing a set of the mass features defined by an investigator.     

Layout-synthesis techniques for yield enhancement

Several yield-enhancement techniques are proposed for the last two stages of VLSI design, i.e., topological/symbolic and physical layout synthesis. Our approach is based on modifications of the symbolic/physical layout to reduce the sensitivity of the design to random point defects without increasing the area, rather than fault tolerance techniques. A layout compaction algorithm is presented and the yield improvement results of some industrial layout examples are shown. This algorithm has been implemented in a commercial CAD framework. Some routing techniques for wire length and via minimization are presented, and the results of wire length reduction in benchmark routing examples are shown. We demonstrate through topological optimization for PLA-based designs that yield enhancement can be applied even at a higher level of design abstraction. Experimental results show that it is possible to achieve significant yield improvements without increasing the layout area by applying the proposed techniques during layout synthesis     

Improvement of sensitivity of breast cancer diagnosis with adaptiveneighborhood contrast enhancement of mammograms

Mammograms are difficult to interpret, especially of cancers at their early stages. We analyze the effectiveness of our adaptive neighborhood contrast enhancement (ANCE) technique in increasing the sensitivity of breast cancer diagnosis. Seventy-eight screen-film mammograms of 21 difficult cases (14 benign and seven malignant), 222 screen-film mammograms of 28 interval cancer patients and six benign control cases were digitized with a high-resolution of about 4096×2048×10-bit pixels and then processed with the ANCE method. Unprocessed and processed digitized mammograms as well as the original films were presented to six experienced radiologists for a receiver operating characteristic (ROC) evaluation for the difficult case set and to three reference radiologists for the interval cancer set. The results show that the radiologists' performance with the ANCE-processed images is the best among the three sets of images (original, digitized, and enhanced) in terms of area under the ROC curve and that diagnostic sensitivity is improved by the ANCE algorithm. All of the 19 interval cancer cases not detected with the original films of earlier mammographic examinations were diagnosed as malignant with the corresponding ANCE-processed versions, while only one of the six benign cases initially labeled correctly with the original mammograms was interpreted as malignant after enhancement. This study demonstrates the potential for improvement of diagnostic performance in early detection of breast cancer with digital image enhancement     

An optimal adaptive thresholding based sub-histogram equalization for brightness preserving image contrast enhancement

Multidimensional Systems and Signal Processing - In this paper, a new adaptive thresholding based sub-histogram equalization (ATSHE) scheme is proposed for contrast enhancement and brightness...     

Adaptive contrast enhancement method for typical histogramconfiguration

An adaptive contrast enhancement technique for improving particle image velocimetry (PIV) images with a dense background and poor contrast is presented. This enhancement is based on a dynamic transformation function that deals with all PIV images. This technique can also be extended to any application images that produce a typical histogram configuration     

A multivariate contrast enhancement technique for multispectralimages

Multispectral and true-color images are often enhanced using histogram-based methods, usually by adjustment of color components after transformation to a selected secondary color system. Enhancement aimed toward the preservation of certain important perceptual qualities generally calls for the secondary coordinate system to be perceptually based. However, independent modification of the secondary components seldom uses the full extent of the RGB gamut unless some color values are clipped at the RGB boundaries. Preserving perceptual attributes is sometimes less important than obtaining the greatest possible color contrast improvement. This is especially true for color composites derived from multispectral images, which have no significant basis in human perception. A new multivariate enhancement technique the authors have named “histogram explosion” is able to exploit nearly the full RGB extent without clipping. While not generally based upon a perceptual model, the method can preserve original hue values when parameters are chosen properly. Experimental results of histogram explosion are presented, along with an analysis of its computational complexity     

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.     

On techniques for detecting circumscribed masses in mammograms

A method for detecting one type of breast tumor, circumscribed masses, in mammograms is presented. It relies on a combination of criteria used by experts, including the shape, brightness contrast, and uniform density of tumor areas. The method uses modified median filtering to enhance mammogram images and template matching to detect the tumors. In the template matching step, suspicious areas are identified by thresholding the cross-correlation values, and a percentile method is used to determine a threshold for each film. In addition, two tests are used to remove false alarms from the resulting candidates. The results obtained by applying these techniques to a set of test images are described. They are judged encouraging.     

Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization.

Mass segmentation is used as the first step in many computer-aided diagnosis (CAD) systems for classification of breast masses as malignant or benign. The goal of this paper was to study the accuracy of an automated mass segmentation method developed in our laboratory, and to investigate the effect of the segmentation stage on the overall classification accuracy. The automated segmentation method was quantitatively compared with manual segmentation by two expert radiologists (R1 and R2) using three similarity or distance measures on a data set of 100 masses. The area overlap measures between R1 and R2, the computer and R1, and the computer and R2 were 0.76 +/- 0.13, 0.74 +/- 0.11, and 0.74 +/- 0.13, respectively. The interobserver difference in these measures between the two radiologists was compared with the corresponding differences between the computer and the radiologists. Using three similarity measures and data from two radiologists, a total of six statistical tests were performed. The difference between the computer and the radiologist segmentation was significantly larger than the interobserver variability in only one test. Two sets of texture, morphological, and spiculation features, one based on the computer segmentation, and the other based on radiologist segmentation, were extracted from a data set of 249 films from 102 patients. A classifier based on stepwise feature selection and linear discriminant analysis was trained and tested using the two feature sets. The leave-one-case-out method was used for data sampling. For case-based classification, the area Az under the receiver operating characteristic (ROC) curve was 0.89 and 0.88 for the feature sets based on the radiologist segmentation and computer segmentation, respectively. The difference between the two ROC curves was not statistically significant.     

Flexible color contrast enhancement method for red-green deficiency

Multidimensional Systems and Signal Processing - Color images are widely used to disseminate information via websites and smartphone applications. Red-green deficients may have difficulty...     

一种有效的低对比度水下图像增强算法

针对水下图像光照不均且对比度低的问题,提出一种新的水下图像增强算法。该算法首先利用去背景法获得照明均匀的前景图像;然后对传统的Butterworth同态滤波器进行改进,提高其高频增益并与相角结合,使得前景图像经改进的Butterworth同态滤波器滤波后,低频成分得到很好的抑制而高频成分被有效放大,从而获得高对比度的水下图像。实验结果表明,与其他算法相比,本文算法能有效地提高图像对比度。