Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels

Accurate segmentation of lungs in pathological thoracic computed tomography (CT) scans plays an important role in pulmonary disease diagnosis. However, it is still a challenging task due to the variability of pathological lung appearances and shapes. In this paper, we proposed a novel segmentation algorithm based on random forest (RF), deep convolutional network, and multi-scale superpixels for segmenting pathological lungs from thoracic CT images accurately. A pathological thoracic CT image is first segmented based on multi-scale superpixels, and deep features, texture, and intensity features extracted from superpixels are taken as inputs of a group of RF classifiers. With the fusion of classification results of RFs by a fractional-order gray correlation approach, we capture an initial segmentation of pathological lungs. We finally utilize a divide-and-conquer strategy to deal with segmentation refinement combining contour correction of left lungs and region repairing of right lungs. Our algorithm is tested on a group of thoracic CT images affected with interstitial lung diseases. Experiments show that our algorithm can achieve a high segmentation accuracy with an average DSC of 96.45% and PPV of 95.07%. Compared with several existing lung segmentation methods, our algorithm exhibits a robust performance on pathological lung segmentation. Our algorithm can be employed reliably for lung field segmentation of pathologic thoracic CT images with a high accuracy, which is helpful to assist radiologists to detect the presence of pulmonary diseases and quantify its shape and size in regular clinical practices.

     

A novel intelligent verification platform based on a structured analysis model

An intelligent verification platform based on a structured analysis model is presented.Using an abstract model mechanism with specific signal interfaces for user callback,the unified structured analysis data,shared by the electronic system level design,functional verification,and performance evaluation,enables efficient management review,auto-generation of code,and modeling in the transaction level.We introduce the class tree,flow parameter diagram,structured flow chart,and event-driven finite state machine as structured analysis models.As a sand table to carry maps from different perspectives and levels via an engine,this highly reusable platform provides the mapping topology to search for unintended consequences and the graph theory for comprehensive coverage and smart test cases.Experimental results show that the engine generates efficient test sequences,with a sharp increase in coverage for the same vector count compared with a random test.     

低温核供热堆保护系统的研究

高可靠性的保护系统是核供热堆安全可靠运行的重要保证。本文介绍了围绕核供热堆保护系统所开展的设计和研究工作。着重讨论了核供热堆的安全特性;保护系统的功能,保护参数的选择以及保护系统的基本结构。最后简要介绍了目前正在进行的一种新型保护系统的设计方案。     

碱性氨基酸辐射保护机理的电子自旋共振研究

本文研究了组氨酸和精氨酸对胸腺嘧啶的辐射保护机理。用ESR方法和分子轨道计算确证其辐射保护是由于电子清除和从胸腺嘧啶阴离子向碱性氨基酸的转移。碱性氨基酸的亲电子性能随质子化程度而异,在pH=7的条件下,组氨酸对胸腺嘧啶的辐射保护机理可能是电子转移与氢原子转移的协同过程。     

铜冶炼渣的资源化利用

介绍了铜冶炼渣的资源现状,分析了其化学组成和矿物学性质,从化学利用和物理利用两方面阐述了铜冶炼渣的资源化利用方法和途径,根据当前铜冶炼渣的资源化现状,对其利用前景和利用中存在的问题进行了分析。     

选矿浓缩过程矿浆浓度的模糊控制

针对治金行业选矿浓缩过程的工艺特点,介绍了基于变频技术的矿浆浓度模糊控制,较好地控制了浓度指标,同时也稳定了矿浆流量并大幅度节能降耗。该设计已在宝钢、武钢等选矿厂中得到成功应用,效益显著。     

用于星上图像压缩的JPEG2000算法的改进

本文修改了 JPEG2000 算法,通过改变编码顺序节约了大量的计算,提高了运行速度.用此算法对空间太阳望远镜的目标图像进行了实验,结果表明在相同压缩比的情况下,修改后的算法对图像质量没有明显的影响,在运行时间和压缩质量方面都能够满足空间太阳望远镜的压缩任务要求.