改进的蚁群聚类算法在电信CRM客户细分中的应用

文章首先简要介绍了遗传算法和蚁群聚类算法,然后结合二者的优点,提出了一种改进的蚁群聚类算法（GACA）。试验证明,与传统的K—means算法以及GKM算法相比,GACA算法可明显提高聚类性能,能更为精确地进行客户细分,从而可为企业市场营销提供更为可靠的数据支持。     

一种新的计算机辅助逻辑设计算法--参数启发式扩展算法

提出了数字电路组合逻辑设计的一种算法。该算法是直接求取函数无冗余覆盖的算法,参数选择原则较为稳定,全过程以最终减少未被算法的最小项数目和降低蕴涵项造价为宗旨,比现有的求解函数覆盖问题的算法更为优越。     

基于QPSK的智能天线固定多波束基带DBF算法

射频(RF)波束形成算法,难以直接采用数字信号处理(DSP)技术,实时完成数字波束形成(DBF)计算.对于相移键控(PSK)调制方式,本文认为RF波束形成算法可等效在基带实现.针对四相相移键控(QPSK)调制方式,本文提出了一种新的智能天线固定多波束基带DBF算法.与RF波束形成算法相比,提出的算法可实现同样的辐射方向图,但需要的计算量却大幅度地降低.基带DBF算法,使智能天线的实现更为简单、应用更为灵活、性能更为优良,对推动智能天线技术实用化发展具有重大意义.     

基于多航迹处理的第三代电视跟踪算法

为提高低空、对地跟踪性能,本文提出了基于多航迹处理的新一代电视跟踪算法,较之以前算法,它利用了更为完备的信息.本文介绍了多航迹相关算法结构,解释了算法涉及的几个主要概念,给出了主要子模块的具体计算方法;最后分析了影响算法性能的因素,简述了应用该算法实验结果.     

OFDMA系统中功率分配新算法研究

通过对迭代注水算法的分析,提出了一种功率分配的新算法,该算法不需要多次迭代,并且能在一定功率约束的条件下,有效地提高系统吞吐量。给出了具体的算法实现步骤和复杂度分析,表明该算法复杂度明显低于迭代注水算法。最后,对该算法进行了仿真分析,并以信噪比为8 dB时为例,分析了两算法的吞吐量性能。仿真结果表明,在低信噪比环境中该算法对系统容量的改善更为显著,且所提算法的吞吐量性能完全逼近迭代注水算法。     

求解非负矩阵分解的有效集BB梯度算法

非负矩阵分解是在非负限制下的一种将一个高维矩阵分解为两个低维矩阵的分解技术。目前,存在的算法大部分是基于乘性迭代算法和交替最小二乘算法。针对交替最小二乘算法的子问题,文中提出了一种有效集BB梯度法,且该算法是全局收敛的。实验结果显示,该算法比投影梯度算法更为有效。     

多无人机协同搜索多目标的路径规划问题研究

多无人机协同搜索多目标的多旅行商航路规划问题(MTSP)是无人机协同作战的关键技术之一。在协同搜索背景下,多架无人机从同一个基地出发搜索附近的可疑目标,以最快速完成任务为目的,建立MTSP模型,提出一种聚类算法和遗传算法进行分步组合的优化算法。第一步,利用K-means聚类算法将MTSP问题分解成多个独立的TSP问题;第二步,改进遗传算法,引入2-opt算法作为优化算子,重新设计选择算子和交叉算子,分别求解多个TSP问题。通过具体算例验证了该算法的合理性,并同常用的分组遗传算法比较,分步组合优化算法具有更高的计算效率,求解结果更为可靠,尤其在求解大型MTSP问题时,优势更为明显。     

小波分析在图像消噪中的应用

针对图像消噪问题，提出了二维快速小波算法和改进小波包分析算法，通过对图像的消噪处理，二维快速小波算法消噪效果明显，但由于小波包分析算法对上一层的低频部分和高频部分同时进行细分，具有更为精确的局部分析能力，对小波包分析算法进行了改进，消除了频带混叠问题，其消噪效果更佳，可得到更为广泛的应用。     

彩色立体像对的自适应匹配

提出了一种自适应权值的色彩相似性度量方法,有效地抑制了噪声对立体匹配的影响,提出以立体像对左、右图像间的颜色直方图相关系数为标准来调整权值的方法,并通过实验推导出具体的计算公式.对以灰度变化为基准的自适应窗选择方法进行改进,在边界提取过程中加入了彩色信息,使得提取的物体轮廓更为完整;用"米"字型窗口来代替原来的矩形窗口也使得窗口增长更为灵活,获得的匹配更为准确.实验结果表明,本文算法能够生成精确度较高的视差图,是一种较好的立体匹配算法.     

指数对的k阶自适应窗口表示算法

给出了一种新的计算指数对g^ah^b的Straus-Shamir类算法,该算法基于整数对的一个新表示,即k阶自适应窗口表示(k-AWE).证明了k-AWE的平均联合Hamming密度为3／(3k+1),与同类算法相比,本文算法更为有效.明确分析了在512到2048比特密钥长度的密码学应用中,窗口宽度的最佳取值为k=3.     

Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

When lung nodules overlap with ribs or clavicles in chest radiographs, it can be difficult for radiologists as well as computer-aided diagnostic (CAD) schemes to detect these nodules. In this paper, we developed an image-processing technique for suppressing the contrast of ribs and clavicles in chest radiographs by means of a multiresolution massive training artificial neural network (MTANN). An MTANN is a highly nonlinear filter that can be trained by use of input chest radiographs and the corresponding "teaching" images. We employed "bone" images obtained by use of a dual-energy subtraction technique as the teaching images. For effective suppression of ribs having various spatial frequencies, we developed a multiresolution MTANN consisting of multiresolution decomposition/composition techniques and three MTANNs for three different-resolution images. After training with input chest radiographs and the corresponding dual-energy bone images, the multiresolution MTANN was able to provide "bone-image-like" images which were similar to the teaching bone images. By subtracting the bone-image-like images from the corresponding chest radiographs, we were able to produce "soft-tissue-image-like" images where ribs and clavicles were substantially suppressed. We used a validation test database consisting of 118 chest radiographs with pulmonary nodules and an independent test database consisting of 136 digitized screen-film chest radiographs with 136 solitary pulmonary nodules collected from 14 medical institutions in this study. When our technique was applied to nontraining chest radiographs, ribs and clavicles in the chest radiographs were suppressed substantially, while the visibility of nodules and lung vessels was maintained. Thus, our image-processing technique for rib suppression by means of a multiresolution MTANN would be potentially useful for radiologists as well as for CAD schemes in detection of lung nodules on chest radiographs.     

Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification

The task of segmenting the posterior ribs within the lung fields of standard posteroanterior chest radiographs is considered. To this end, an iterative, pixel-based, supervised, statistical classification method is used, which is called iterated contextual pixel classification (ICPC). Starting from an initial rib segmentation obtained from pixel classification, ICPC updates it by reclassifying every pixel, based on the original features and, additionally, class label information of pixels in the neighborhood of the pixel to be reclassified. The method is evaluated on 30 radiographs taken from the JSRT (Japanese Society of Radiological Technology) database. All posterior ribs within the lung fields in these images have been traced manually by two observers. The first observer's segmentations are set as the gold standard; ICPC is trained using these segmentations. In a sixfold cross-validation experiment, ICPC achieves a classification accuracy of 0.86 +/- 0.06, as compared to 0.94 +/- 0.02 for the second human observer.     

基于小波变换的多神经网络胸片骨骼分离

胸片中,因大量肺结点被锁骨或肋骨遮挡而被放射科医生忽略。为了从胸片图像中分割出骨骼结构,提出了一种基于小波变换的多分辨率人工神经网络,以获取去除骨骼结构的虚拟软组织胸片。该方法可有效保证肺结点与血管的清晰度,且分离出骨骼和软组织可有效地帮助放射医生检测肺结点。     

Semiautomatic detection of scoliotic rib borders from posteroanterior chest radiographs

3-D assessment of scoliotic deformities relies on an accurate 3-D reconstruction of bone structures from biplanar X-rays, which requires a precise detection and matching of anatomical structures in both views. In this paper, we propose a novel semiautomated technique for detecting complete scoliotic rib borders from PA-0° and PA-20° chest radiographs, by using an edge-following approach with multiple-path branching and oriented filtering. Edge-following processes are initiated from user starting points along upper and lower rib edges and the final rib border is obtained by finding the most parallel pair among detected edges. The method is based on a perceptual analysis leading to the assumption that no matter how bent a scoliotic rib is, it will always present relatively parallel upper and lower edges. The proposed method was tested on 44 chest radiographs of scoliotic patients and was validated by comparing pixels from all detected rib borders against their reference locations taken from the associated manually delineated rib borders. The overall 2-D detection accuracy was 2.64 ± 1.21 pixels. Comparing this accuracy level to reported results in the literature shows that the proposed method is very well suited for precisely detecting borders of scoliotic ribs from PA-0° and PA-20° chest radiographs.     

Automatic detection of rib borders in chest radiographs

An algorithm for detection of posterior rib borders in chest radiographs is presented. The algorithm first determines the thoracic cage boundary to restrict the area of search for the ribs. It then finds approximate rib borders using a knowledge-based Hough transform. Finally, the algorithm localizes the rib borders using an active contour model. Results of the proposed rib finding algorithm on 10 chest radiographs are presented.     

低对比度胸片中管线检测方法

一般在重症监护病房中常用便携式胸片机来辅助医生监控患者的病情进展,了解各种医用管线在病人体内的具体位置。但便携式X光机得到的胸片有着低对比度、噪声多的缺陷,且胸片中的管线并不清晰,使得医生不便于观测管线的位置。文中提出一种在ICU病房中胸片的管线检测方法,在得到病人的胸片后,使用对比度限制的自适应直方图均衡化处理方法来调整对比度,再对其进行双边滤波来去除噪声,同时增强管线的细节信息,然后再做管线检测。将文中方法应用在100余张便携式胸片中,结果显示所提方法可有效检测医用管线,且有效准确率接近90%。     

A Shift-Invariant Neural Network for the Lung Field Segmentation in Chest Radiography

We have developed a computerized method using a neural network for the segmentation of lung fields in chest radiography. The lung is the primary region of interest in routine chest radiography diagnosis. Since computer is expected to perform disease pattern search automatically, it is important to design appropriate algorithms to delineate the region of interest. A reliable segmentation method is essential to facilitate subsequent searches for image patterns associated with lung diseases. In this study, we employed a shift invariant neural network coupled with error back-propagation training method to extract the lung fields. A set of computer algorithms were also developed for smoothing the initially detected edges of lung fields. Our preliminary results indicated that 86% of the segmented lung fields globally matched the original chest radiographs. We also found that the method facilitates the development of computer algorithms in the field of computer-aided diagnosis.     

Automated Radiographic Diagnosis via Feature Extraction and Classification of Cardiac Size and Shape Descriptors

One goal of digital processing of radiographic images is to provide the radiologist with quantitative measurements of human anatomy as well as an indication as to whether or not this anatomy is within normal limits. A computer algorithm is described, designed to automatically detect, extract quantitative measurements from, and diagnose the cardiac projection present in full-size anteriorview chest radiographs. A normal-abnormal diagnosis is demonstrated utilizing abnormal data from five classes of heart disease. In addition, normal-abnormal as well as normal-differential diagnoses are demonstrated for the rheumatic heart disease class. A feature extraction algorithm is developed using several ad hoc techniques, some of which were adapted from other feature extraction uses. The extracted features are classified into diagnostic classes using linear and quadratic discriminant functions. A concurrent study of physician diagnostic accuracy is also undertaken using the averaged diagnostic rates of ten radiologists on a representative subset of the radiographs used in the computer study.     

Local contralateral subtraction based on bilateral symmetry of lung for reduction of false positives in computerized detection of pulmonary nodules

A novel method called local contralateral subtraction has been developed for the removal of normal anatomic structures in chest radiographs based on the symmetry between the left and right lung regions. The method was oriented to the reduction of false positives reported by a computer-aided diagnosis (CAD) scheme for detection of lung nodules in chest radiographs. In our method, two regions of interest (ROIs) are extracted, one from the position where a nodule candidate is located, and the other from the anatomically corresponding location in the opposite lung, which contains similar normal structures. A wavelet-based, multiresolution image registration method is employed for matching the two ROIs, and subtraction is performed. If no structure remains in the subtracted ROI, then the original ROI is identified as negative (i.e., it contains only normal structures); otherwise, it is regarded as positive (i.e., it contains a nodule). A measure that quantifies the remaining structures was developed to distinguish between nodules and false positives. Application of the method to clinical chest radiographs showed that it was effective in eliminating normal anatomic structures and reducing the number of false detections in the CAD scheme for detection of lung nodules.