Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density (MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case–control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC = 0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC = 0.838. In the case–control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available.     

基于乳腺钼靶X线图像等值面特征的乳腺区域阈值分割算法

乳腺钼靶X线图像中乳腺区域的分割可以帮助对图像进行深入分析和处理,从而提高乳腺疾病的诊断准确率。提出一种能有效提取乳腺区域的算法。算法分析了乳腺钼靶X线图像等值面面积变化不连续的特征并将其用于分割阈值的精确计算。该算法使用基于扫描线的方法来获得含乳腺区域的连通区域,比种子填充法效率更高。为了获得更纯粹的乳腺区域,通过一些精细地处理对乳腺区域相连的未曝光图像边框作了剥离。实验结果表明算法在乳腺区域分割的精度和执行效率上都有更好的表现。     

Implementing automated diagnostic systems for breast cancer detection

This paper intends to an integrated view of implementing automated diagnostic systems for breast cancer detection. The major objective of the paper is to be a guide for the readers, who want to develop an automated decision support system for detection of breast cancer. Because of the importance of making the right decision, better classification procedures for breast cancer have been searched. The classification accuracies of different classifiers, namely multilayer perceptron neural network (MLPNN), combined neural network (CNN), probabilistic neural network (PNN), recurrent neural network (RNN) and support vector machine (SVM), which were trained on the attributes of each record in the Wisconsin breast cancer database, were compared. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. This research demonstrated that the SVM achieved diagnostic accuracies which were higher than that of the other automated diagnostic systems.     

Toward breast cancer diagnosis based on automated segmentation of masses in mammograms

This work explores the use of characterization features extracted based on breast-mass contours obtained by automated segmentation methods, for the classification of masses in mammograms according to their diagnosis (benign or malignant). Two sets of mass contours were obtained via two segmentation methods (a dynamic-programming-based method and a constrained region-growing method), and simplified versions of these contours (modeling the contours as ellipses) were employed to extract a set of six features designed for characterization of mass margins (contrast between foreground region and background region, coefficient of variation of edge strength, two measures of the fuzziness of mass margins, a measure of spiculation based on relative gradient orientation, and a measure of spiculation based on edge-signature information). Three popular classifiers (Bayesian classifier, Fisher's linear discriminant, and a support vector machine) were then used to predict the diagnosis of a set of 349 masses based on each of said features and some combinations of these. The systems (each system consists of a segmentation method, a featureset, and a classifier) were compared with each other in terms of their performance on the diagnosis of the set of breast masses. It was found that, although there was a percent difference of about 14% in the average segmentation quality between methods, this was translated into an average percent difference of only 4% in the classification performance. It was also observed that the spiculation feature based on edge-signature information was distinctly better than the rest of the features, although it is not very robust to changes in the quality of the segmentation. All systems were more efficient in predicting the diagnosis of benign masses than that of the malignant masses, resulting in low sensitivity and high specificity values (e.g. 0.6 and 0.8, respectively) since the positive class in the classification experiments is the set of malignant masses. It was concluded that features extracted from automated contours can contribute to the diagnosis of breast masses in screening programs by correctly identifying a majority of benign masses.     

基于自适应邻域的核密度动态目标分割方法

经典的核密度估计背景模型使用固定的背景样本邻域来抑制背景运动形成的伪目标,无法适应不同背景的运动规律,导致不能抑制同一拍摄场景中所有背景运动形成的伪目标。因此在经典核密度估计的背景建模基础上,使用图像配准技术,能实现对不同运动背景区域的邻域尺寸自适应选择,并且在同一拍摄场景中可适应更多的背景运动类型,抑制更多类型的伪目标。实验结果证明,该方法对大部分由背景运动导致的伪目标有很好的抑制作用。     

整合超像元分割和峰值密度的高光谱图像聚类

目的 传统图像聚类算法多利用像元的光谱信息,较少考虑图像的空间信息,容易受到噪声干扰。针对该问题,提出一种整合超像元分割（SLIC）和峰值密度（DP）的高光谱图像聚类算法。方法 首先,利用超像元分割技术对高光谱图像进行分割并提取超像元光谱特征;然后,根据提取的超像元光谱特征,计算其峰值密度信息,搜索超像元光谱簇,构建像元与类别间的隶属度关系。最后,利用高光谱模拟数据以及两组真实高光谱图像评价算法的鲁棒性和精度。结果 在不同信噪比的模拟数据中,SLIC-DP算法在调整芮氏指标（ARI）最优的条件下,较K-means和SLIC-Kmeans的方差降低61.86%和41.61%,体现优越的鲁棒性。在高光谱数据集Salinas-A和Indian Pines中,SLIC-DP算法的ARI为0.777 1和0.325 7,较K-Means和SLIC-KMeans聚类算法分别增长10.71%,5.01%与78.86%,25.27%。结论 本文算法抗噪声能力强,充分利用空间信息与光谱信息,有效提升高光谱图像聚类精度。经验证,能满足高光谱图像信息提取和分析的要求,可进一步推广和研究。     

基于显著性密度修正的均值漂移分割算法

针对固定空间和色彩带宽的均值漂移分割算法无法解决的错分割问题,提出一种基于显著性特征进行密度修正的均值漂移分割算法。首先基于密度估计的主颜色量化结果计算区域视觉显著性;其次,将区域视觉显著性融合像素级显著性作为色彩特征空间聚类的密度修正因子,将密度修正后的融合图像作为输入执行均值漂移分割;最后进行小区域合并获得最终分割结果。实验结果显示,所提分割算法在四种尺度上的真实边界准确率和召回率平均值达到0.64和0.78,与其他方法相比,分割精度有显著的提高;同时,在视觉上有效提高了目标完整性,增强了自然图像中目标分割的鲁棒性。     

A novel method for automated human behavior segmentation

In order to conveniently classify, retrieve, and synthesize human motion, motion capture (MoCap) data need to be properly segmented into distinct behaviors. In this paper, we propose a novel automated segmentation method based on posture histograms in sliding window. Firstly, a set of new posture features are proposed and defined to construct the posture histogram, which is a new compact representation of behavioral features. Then, by executing the sliding window, especially in this paper, the behavior features are analyzed in subsequence level to reduce noise sensitivity. We open up a novel way to tune sliding window by studying steady states of human behaviors, so that conspicuous and stable behavioral features can be obtained. Finally, by analyzing the clustering property of posture histograms of the subsequences, the behavior segmentation problem is tactfully simplified to the detection of outlier subsequence. In particular, the local outlier factor algorithm is adopted to solve outlier subsequence detection, and good results are achieved. Extensive experiments are conducted on 14 pieces of multi‐bcase, † † CMU Graphics Lab Motion Capture Database: http://mocap.cs.cmu.edu
and the experimental results demonstrate that our proposed method outperforms other state‐of‐the‐art ones. Copyright © 2016 John Wiley & Sons, Ltd.     

基于自动阈值的CT图像快速肺实质分割算法

首先给出了一个肺实质分割的基本框架,结合最佳阈值法、数学形态学方法,对图像进行了粗分割;然后,针对左右肺未完全分离的情况,提出了快速自适应的优化细分割方法。在实际临床胸部CT图像上的实验表明,该方法的分割结果和专家的人工分割结果很接近,特别是对于左右肺的分离有很好的实验效果,成功分割率达到94.8%。     

基于直方图帧差的自适应镜头分割算法

提出一种改进的基于亮度直方图帧差的自适应镜头分割算法,该方法包括突变镜头的检测过程和渐变镜头的渐变过程,这两个处理过程由相邻两帧的亮度直方图帧差与自适应阈值的比较来自动进行选择,在突变镜头的检测过程中加入隔帧帧差法检测闪光灯,渐变镜头的检测采用基于帧间差方差的方法。实验结果表明,改进算法具有很好的检测效果,且计算复杂度低,易于实现。     

基于骨架树的荧光共焦图像神经树突棘自动分割与检测方法*

提出一个荧光共焦图像中神经树突棘自动分割与检测方法。该方法采用新的自适应区域生长法对神经树突棘目标进行预分割,基于种子点的路径规划算法,以计算给定点到目标点的最短路径来获取初始主骨架;通过建立最小生成树描述模型对骨架进行修剪,利用种子点间的矢量角度变化及顶点距离值对突棘进行检测提取。实验结果表明,该方法能很好地提取树突骨架,并取得了较好的突棘检测效果。     

结合流形密度的聚集行为模式分割算法

目的 在视频监控和人群模式行为理解的重要应用中,识别分割场景中的集体行为仍然是一个极具挑战性的问题。在这项研究中,提出一种基于流形密度的集体聚类算法,能够识别具有任意形状和不同密度条件下的集体行为的局部和全局模式。方法 受群体运动行为的流形拓扑结构启发,首先提出一种新的流形距离度量方式用于挖掘群体运动的深层行为模式。进一步定义了集体聚集密度的概念,并通过基于聚集密度的聚类算法识别具有局部一致性行为的群组,这种策略更适用于识别具有任意形状的聚类。同时考虑到子群组之间的复杂交互作用,引入层次聚集合并算法得到全局集体行为模式,可以有效地表征全局一致性关系。结果 针对不同情况下的复杂场景,本文算法在集体视频监控数据集下的实验结果表明了其有效性和鲁棒性,相比于传统的聚类方法和标准经典算法,以平均误差（AD）和方差（VAR）作为评价指标来评价算法性能,本文方法将识别分割聚集行为群组的误差率结果控制在了0.81和0.99以内,相比许多经典方法有较大提升。同时在具有复杂流形结构及任意密度条件下的人群场景中能够取得精确有效的识别结果,解决了经典方法在该特殊场景下存在的缺点。结论 本文针对已有方法在流形结构场景识别集体行为流向缺乏精确性和稳定性的描述和分析这一问题,提出了基于流形密度的群组聚集聚类识别算法,在多个复杂真实视频数据集中进行实验,证明了所提方法的有效性,并相比于已有方法具有更高的识别精度。     

A blind video watermarking scheme based on ICA and shot segmentation

With the development of network and multimedia techniques, the digital video copyright protection issues have become more and more important as digital video can be easily pro- duced, replicated, accessed and distributed. Digital video watermark is the ma…     

基于密度的K-Means算法及在客户细分中的应用研究

针对K-Means算法所存在的问题进行了深入研究,提出了基于密度的K-Means算法（KMAD算法）。该算法采用聚类对象区域空间的密度分布方法来确定聚类个数K的值,然后用高密度区域的质心作为K-Means算法的初始聚类中心。理论分析与实验结果表明了改进算法的有效性和稳定性,并将改进的算法应用于客户细分研究中。     

基于深度学习的实例分割研究综述

深度学习在计算机视觉领域已经取得很大发展,虽然基于深度学习的实例分割研究近年来才成为研究热点,但其技术可广泛应用在自动驾驶,辅助医疗和遥感影像等领域。实例分割作为计算机视觉的基础问题之一,不仅需要对不同类别目标进行像素级别分割,还要对不同目标进行区分。此外,目标形状的灵活性,不同目标间的遮挡和繁琐的数据标注问题都使实例分割任务面临极大的挑战。本文对实例分割中一些具有价值的研究成果按照两阶段和单阶段两部分进行了系统性的总结,分析了不同算法的优缺点并对比了模型在COCO数据集上的测试性能,归纳了实例分割在特殊条件下的应用,简要介绍了常用数据集和评价指标。最后,对实例分割未来可能的发展方向及其面临的挑战进行了展望。     

A novel white blood cell segmentation scheme based on feature space clustering

This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods.     

Fully automatic segmentation of breast ultrasound images based on breast characteristics in space and frequency domains

Due to the complicated structure of breast and poor quality of ultrasound images, accurately and automatically locating regions of interest (ROIs) and segmenting tumors are challenging problems for breast ultrasound (BUS) computer-aided diagnosis systems. In this paper, we propose a fully automatic BUS image segmentation approach for performing accurate and robust ROI generation, and tumor segmentation. In the ROI generation step, the proposed adaptive reference point (RP) generation algorithm can produce the RPs automatically based on the breast anatomy; and the multipath search algorithm generates the seeds accurately and fast. In the tumor segmentation step, we propose a segmentation framework in which the cost function is defined in terms of tumor?s boundary and region information in both frequency and space domains. First, the frequency constraint is built based on the newly proposed edge detector which is invariant to contrast and brightness; and then the tumor pose, position and intensity distribution are modeled to constrain the segmentation in the spatial domain. The well-designed cost function is graph-representable and its global optimum can be found. The proposed fully automatic segmentation method is applied to a BUS database with 184 cases (93 benign and 91 malignant), and the performance is evaluated by the area and boundary error metrics. Compared with the newly published fully automatic method, the proposed method is more accurate and robust in segmenting BUS images.     

Liver segmentation in MRI: A fully automatic method based on stochastic partitions

There are few fully automated methods for liver segmentation in magnetic resonance images (MRI) despite the benefits of this type of acquisition in comparison to other radiology techniques such as computed tomography (CT). Motivated by medical requirements, liver segmentation in MRI has been carried out. For this purpose, we present a new method for liver segmentation based on the watershed transform and stochastic partitions. The classical watershed over-segmentation is reduced using a marker-controlled algorithm. To improve accuracy of selected contours, the gradient of the original image is successfully enhanced by applying a new variant of stochastic watershed. Moreover, a final classifier is performed in order to obtain the final liver mask. Optimal parameters of the method are tuned using a training dataset and then they are applied to the rest of studies (17 datasets). The obtained results (a Jaccard coefficient of 0.91 ± 0.02) in comparison to other methods demonstrate that the new variant of stochastic watershed is a robust tool for automatic segmentation of the liver in MRI.     

基于边缘检测与分裂合并的图像分割算法

针对传统分裂合并算法容易产生方块效应与过分割的缺点,提出了一种结合边缘检测和分裂合并的图像分割算法.该算法直接利用图像的边缘信息进行分裂,不断将图像分裂为一些不规则形状的一致性区域,然后根据一定规则将相似的区域合并.实验表明,该算法能大幅减少分裂次数,并有效克服方块效应和过分割等缺点,图像分割效果较好.     

A universal texture segmentation and representation scheme based on ant colony optimization for iris image processing

This paper proposes a novel scheme for texture segmentation and representation based on Ant Colony Optimization (ACO). Texture segmentation and texture characteristic expression are two important areas in image pattern recognition. Nevertheless, until now, how to find an effective way for accomplishing these tasks is still a major challenge in practical applications such as iris image processing. We propose a framework for ACO based image processing methods. Considering the specific characteristics of various tasks, such a framework possesses the flexibility of only defining different criteria for ant behavior correspondingly. By defining different kinds of direction probability and movement difficulty for artificial ants, an ACO based image segmentation algorithm and a texture representation method are then presented for automatic iris image processing. Experimental results demonstrated that the ACO based image processing methods are competitive and quite promising, with excellent effectiveness and practicability especially for images with complex local texture situations.