一种基于ITK脑部MRI混合分割方法的设计与实现

背景：由于脑部MR图像中信息对比度不高,各种脑部组织的形状复杂等特点,分割方法的选择比较困难,单一的算法很难获得满意的分割结果。 目的：针对脑部MRI的特点综合利用现有的算法开发和定制有效的分割应用算法。 方法：根据邻域连接和Canny水平集分割算法的优缺点,结合图像特征,用邻域连接方法的分割结果作为Canny水平集分割算法的先验分割模型,借以确定出Canny算法的下限阈值,从而完成两种算法的混合分割。 结果与结论：采用实验所用混合方法得到的白质和灰质的分割结果,经与专家手工分割结果对比,证明该方法取得了较好的分割效果,从而证明综合利用现有的算法,不仅避免了重复劳动,还能开发和定制出更加有效的分割应用算法,具备很好的应用潜力。     

一种基于概率配分和最大模糊熵的CT图像分割方法

为了从CT图像中提取到多个组织的解剖特征,克服运算速度快与运算结果不稳定的矛盾,提出了一种基于概率分布和模糊熵的CT图像分割方法。为了找到分割灰度图象的最佳阈值,根据模糊聚类和概率配分之间的关系,以及模糊熵有最大值的必要条件,从而得到各类的概率配分,因此在搜索阈值组合时,先搜索满足各类概率配分的阈值,然后从这些阈值中搜索使模糊熵最大的阈值。实验结果表明该方法能很好地完成CT图象的分割。此算法运算速度较快;与用遗传算法、模拟退火算法相比较,运算结果稳定,分割更准确。     

一种基于自适应最小模糊熵的CT图像分割方法

为了从CT图像中提取到多个组织的解剖特征,解决运算速度的提高与运算结果不稳定的矛盾,我们提出了一种基于自适应最小模糊熵的图像分割算法.为了找到分割灰度图像的最佳阈值.首先利用迭代公式以及图像的直方图来计算出每个模糊子集的隶属函数中指数参数的值以及阈值的搜索范围,然后在已确定的搜索范围内用穷举法搜索出能使模糊熵最小的最佳阈值.实验表明该方法能较好的完成CT图像的分割.此算法运算速度较快;与用遗传算法、模拟退火算法相比较,运算结果稳定,重复性更好,得到的图像细节成分要更多些.     

改进模糊C-均值分割算法在多发性硬化症MR脑部图像中的应用

背景：脑部MR图像是一种无纹理的图像,未被噪声污染的脑部MR图像的灰度值具有分片为常数的特点。因此,在聚类过程中灰度值有趋向于在同一个分割区域中相对接近的性质。 目的：寻找一个能够自动分割多发性硬化症病灶的模糊C-均值改进方法,为临床对于多发性硬化症的判断提供更方便的工具。 方法：考虑到脑部MR图像相邻象素属于同一分类的概率相近的特性,在迭代过程中对8邻域数据集进行滤波以降低噪声对聚类精度的影响,提出了一种改进的模糊C-均值聚类算法。就是将模糊C-均值聚类算法迭代过程中得到的灰度值看作一个数据集,用每个象素邻域象素的灰度值修正该象素的模糊隶属度取值,从而达到利用空间信息抑制噪声的目的。 结果与结论：选取了10个多发性硬化症患者的脑部MRI图像进行试验。通过对多发性硬化症患者MR T1脑部图像和T2液体衰减反转回复脑部图像的分割实验,结果显示该算法能够有效分割多发性硬化症病灶,与其他方法所做的多发性硬化症病灶分割相比,本算法更易于实现,运算时间短,同时结果与临床医生的勾画比较重叠率较高,对其临床辅助诊断具有重要作用。     

基于超声心动图的二叶式主动脉瓣钙化病灶自动分割算法

目的 提出一种基于超声心动图（echocardiography, ECHO）的二叶式主动脉瓣钙化病灶（bicuspid aortic valve calcification,BAVC）自动分割算法，以提高BAVC识别效率。方法 选取BAVC患者的ECHO，应用迭代均值滤波器、一二阶全变分和指定直方图均衡化算法对其进行降噪、灰度平衡化的图像预处理；根据钙化阈值特征，应用阈值滤波器、8邻域原理获取钙化病灶的初始轮廓，并根据ECHO中主动脉瓣的大小和位置特征，自适应地选择初始轮廓作为初始种子区域；根据钙化阈值、迭代终止条件、自适应调整迭代次数等相关参数，自动分割BAVC区域。将自动分割的结果与手动分割结果进行比较，并分析ECHO中不同的椒盐噪声（salt and pepper noise,SPN）及斑点噪声（speckle noise,SN）对分割结果的影响。结果 所建立的自动分割算法的平均处理时间、平均迭代次数分别达到了4.747 s、31，像素精确度（pixel accuracy,PA）、交并比（intersection-over-union,IoU）、Dice系数（Dice coeff...     

基于统计阈值的脑肿瘤MRI图像的分割方法

我们提出了一种基于统计阈值的脑肿瘤MRI图像的分割方法.首先,通过预处理操作去除脉冲噪声和头骨影像,然后利用统计阈值分割方法对肿瘤进行了分割.为了克服经典阈值分割方法存在不完全分割的问题,利用分割后两个类的方差和概率定义了一个新的阈值选择准则,并通过最小化准则确定最佳分割阈值.实验结果证明,本方法分割效果好,解决了采用经典阈值分割方法对脑肿瘤的不完全分割问题.     

基于数学形态学和Otsu方法的VHP数据心脏图像分割

针对可视人计划的数据提取心脏图像存在自身的多样性以及局部边界不清晰所造成的分割难问题,提出了一种分阶段的分割方法。即在第一阶段引入数学形态学的方法,提出一种基于形态重构的开闭运算与O tsu阈值分割相结合的方法,对原始图像中的感兴趣区域进行预提取,有效的解决了单一阈值分割方法中存在的缺陷;第二阶段利用形态学变换对预提取图像进行精确分割,最终得到心脏图像。实验结果表明,这种分阶段的分割方法在运算效率以及分割精度上都取到了较理想的结果,对可视人计划的实现具有较高的实用价值。     

用于MRI脑组织分割的自动模糊连接方法

本研究提出了一种自动化的模糊连接（fuzzy connectedness，FC）方法，用于3维核磁共振（MRI）图像脑组织分割。方法的主要创新在于提出了FC方法中各项参数的自动指定方法，包括：利用灰质、白质各自的体素尺度（scale）值大小差异，自动估计组织的灰度概率密度函数；根据估计得到的组织灰度概率密度函数，自动指定种子点。从而避免了人工干预，保证了分割过程的自动化和可重复性。所提方法在IBSR（the Internet Brain Segmentation Repository）数据库所提供的MRI图像上进行了测试，并和同类研究进行了对比，分割精度优于同类研究。作为一种完全自动化的方法，该方法能够被广泛应用到3维可视化、放疗手术计划和医学数据库构造中。     

基于模糊均值聚类算法的灰度不均匀脑MR图像的分割

磁共振图像经常被含有缓慢变化的灰度不均匀场所破坏,不均匀场会造成同一组织的灰度发生变化,从而影响计算机辅助诊断的准确性.传统的基于灰度信息的分割方法对具有不均匀场的磁共振图像分割效果往往并不理想.文章改进了基于灰度信息的模糊C均值(FCM)算法,将偏移场模型、代表图像空间信息的邻域控制信息和最小二乘曲面拟合方法有机结合,能同时实现图像的校正和聚类,适用于灰度不均匀脑部磁共振图像的分割,分割精度明显优于已有的基于FCM的分割方法.     

基于3D医学图像的血管三维分割

血管三维分割在血管疾病(如狭窄或畸形)诊断、手术规划和手术引导等许多实际应用中发挥重要作用。但三维分割的实时性仍是一个难题。本研究提出一种基于水平集的快速三维血管分割方法,该方法用内、外邻域曲面来描述被分割目标的边界,并定义水平集函数为简单的整数符号距离函数。通过扫描内外邻域曲面上的点,使之在速度场的作用下向目标边界移动。该方法的不同之处在于利用简单的模型极大地减小了计算量,分割速度快,大容量的MS-CTA[2563体素]图像可在20s内处理完毕。同时,展示了一些三维血管的分割实例。特别需要指出的是,对于人体的大血管,可在不经血管造影的情况下,直接从CT等三维图像中分割出来。     

Gene selection for tumor classification using neighborhood rough sets and entropy measures

With the development of bioinformatics, tumor classification from gene expression data becomes an important useful technology for cancer diagnosis. Since a gene expression data often contains thousands of genes and a small number of samples, gene selection from gene expression data becomes a key step for tumor classification. Attribute reduction of rough sets has been successfully applied to gene selection field, as it has the characters of data driving and requiring no additional information. However, traditional rough set method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, we propose a novel gene selection method based on the neighborhood rough set model, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. Moreover, this paper addresses an entropy measure under the frame of neighborhood rough sets for tackling the uncertainty and noisy of gene expression data. The utilization of this measure can bring about a discovery of compact gene subsets. Finally, a gene selection algorithm is designed based on neighborhood granules and the entropy measure. Some experiments on two gene expression data show that the proposed gene selection is an effective method for improving the accuracy of tumor classification.     

Influence of education and neighborhood poverty on pressor responses to phenylephrine in African-Americans and Caucasian-Americans

Although neighborhood disadvantage has been linked to the development of cardiovascular disease, the mechanism through which living in impoverished neighborhoods is associated with poor cardiovascular health is not well understood. Additionally, it is not clear whether individual socioeconomic status (SES) interacts with neighborhood factors to influence cardiovascular outcomes. Using multilevel modeling, we examined the interaction between neighborhood poverty and individual SES on pressor responses to an alpha agonist, phenylephrine (PE), in an adult sample of 105 African-Americans and 106 Caucasian-Americans. Neighborhood poverty was assessed using census block data gathered from the Census Bureau. Education and occupation were used to assess individual SES. Pressor responsiveness was calculated as the systolic and diastolic blood pressure (BP) response to a 100-μg PE bolus administered intravenously. There was a significant interaction between education and neighborhood poverty on pressor responses. Higher education was associated with smaller BP responses to PE; but only in individuals who lived in neighborhoods in which less than 5% of the residents lived below the poverty line. Occupation was unrelated to pressor responses to PE. These results suggest that neighborhood characteristics play an important role in cardiovascular functioning.     

Comparing efficient data structures to represent geometric models for three-dimensional virtual medical training

Data structures have been explored for several domains of computer applications in order to ensure efficiency in the data store and retrieval. However, data structures can present different behavior depending on applications that they are being used. Three-dimensional interactive environments offered by techniques of Virtual Reality require operations of loading and manipulating objects in real time, where realism and response time are two important requirements. Efficient representation of geometrical models plays an important part so that the simulation may become real. In this paper, we present the implementation and the comparison of two topologically efficient data structures – Compact Half-Edge and Mate-Face – for the representation of objects for three-dimensional interactive environments. The structures have been tested at different conditions of processors and RAM memories. The results show that both these structures can be used in an efficient manner. Mate-Face structure has shown itself to be more efficient for the manipulation of neighborhood relationships and the Compact Half-Edge was more efficient for loading of the geometric models. We also evaluated the data structures embedded in applications of biopsy simulation using virtual reality, considering a deformation simulation method applied in virtual human organs. The results showed that their use allows the building of applications considering objects with high resolutions (number of vertices), without significant impact in the time spent in the simulation. Therefore, their use contributes for the construction of more realistic simulators.     

Eye movements and smart technology

From a cognitive science/artificial intelligence perspective, this paper identifies the scanpath concept as the instantiation of a general sequencing principle that permeates the organization of spatial scene knowledge throughout the levels of mental processing. As such, it helps create methodologies to open up windows onto higher-level cognitive processes, particularly by relating shifts of visual focus to shifts of attention in mental reasoning. The paper argues that these methodologies form a robust basis for smart applications that employ eye movements to assess and to assist in diagrammatic problem solving.     

Semi-automatic Segmentation of Brain Tumors Using Population and Individual Information

Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.     

A method of tumor classification based on wavelet packet transforms and neighborhood rough set

Tumor classification is an important application domain of gene expression data. Because of its characteristics of high dimensionality and small sample size (SSS), and a great number of redundant genes not related to tumor phenotypes, various feature extraction or gene selection methods have been applied to gene expression data analysis. Wavelet packet transforms (WPT) and neighborhood rough sets (NRS) are effective tools to extract and select features. In this paper, a novel approach of tumor classification is proposed based on WPT and NRS. First the classification features are extracted by WPT and the decision tables are formed, then the attributes of the decision tables are reduced by NRS. Thirdly, a feature subset with few attributes and high classification ability is obtained. The experimental results on three gene expression datasets demonstrate that the proposed method is effective and feasible.     

Impact of pain and palliative care services on patients

Background:

Palliative care has become an emerging need of the day as the existing health-care facilities play only a limited role in the care of the chronically ill in the society. Patients with terminal illness in most cases spend their lives in the community among their family and neighbors, so there is the need for a multi disciplinary team for their constant care. Volunteers are primary care givers who originate normally from the same locality with local knowledge and good public contact through which they can make significant contributions in a team work by bridging the gap between the patient community and outside world.

Aim:

The present study has been undertaken to analyze the impact of palliative care services on patients by considering 51 variables.

Materials and Methods:

The respondents of the study include 50 pain and palliative care patients selected at random from 15 palliative care units functioning in Ernakulam district. The analysis was made by using statistical techniques viz. weighted average method, Chi-square test, Friedman repeated measures analysis of variance on ranks and percentages.

Results:

The study revealed that the major benefit of palliative care to the patients is the reduction of pain to a considerable extent, which was unbearable for them earlier. Second, the hope of patients could be maintained or strengthened through palliative care treatment.

Conclusion:

It is understood that the services of the doctors and nurses are to be improved further by making available their services to all the palliative care patients in a uniform manner.     

Tumor classification by combining PNN classifier ensemble with neighborhood rough set based gene reduction

Since Golub applied gene expression profiles (GEP) to the molecular classification of tumor subtypes for more accurately and reliably clinical diagnosis, a number of studies on GEP-based tumor classification have been done. However, the challenges from high dimension and small sample size of tumor dataset still exist. This paper presents a new tumor classification approach based on an ensemble of probabilistic neural network (PNN) and neighborhood rough set model based gene reduction. Informative genes were initially selected by gene ranking based on an iterative search margin algorithm and then were further refined by gene reduction to select many minimum gene subsets. Finally, the candidate base PNN classifiers trained by each of the selected gene subsets were integrated by majority voting strategy to construct an ensemble classifier. Experiments on tumor datasets showed that this approach can obtain both high and stable classification performance, which is not too sensitive to the number of initially selected genes and competitive to most existing methods. Additionally, the classification results can be cross-verified in a single biomedical experiment by the selected gene subsets, and biologically experimental results also proved that the genes included in the selected gene subsets are functionally related to carcinogenesis, indicating that the performance obtained by the proposed method is convincing.     

The universal muscular chain reaction,muscle spasm,Torsions, ruptures and extravasations. Chameleons of pathology and some manifestations of simple muscular disorders

Most bodily functions require the coordinated actions of complementary and supplementary paired muscle groups. Where this essential muscular cooperation is lacking, hollow organs may burst and others become literally screwed up, giving rise to many similar spastic diseases such as Torticollis, Twisted ovarian cyst, Torsion of the Testis, Volvulus of the intestines, Varicose Veins, Megacolon, Aortamegaly, Scoliosis, Erb's Palsy, Peyronie's Disease, Main-en-Griffe, Undescended Foot (Pes Cavus), Talipes, Strabismus. Spasm is “panenepidemic” and unclassified examples of Torsion Dystonia and Dyskinesia really are as common as debt and taxes.     

Hypo- und Hypermagnesämie aus kardiologischer Sicht

Zusammenfassung Eine Reihe pathologischer Zustände bedingen Magnesiummangel. Zustände mit Hypermagnesämie sind ebenfalls bekannt, doch wesentlich seltener. Für den Kardiologen beachtenswert ist, daß unter Therapie mit bestimmten Diuretica bei Herzinsuffizienz, bei Herzinfarkt, Kardiomyopathie, Digitalisintoxikation und bestimmten Herzrhythmusstörungen Hypomagnesämie beobachtet wurde. Leider kann in der klinischen Routine nur ein extracelluläres Magnesiumdefizit durch Serumbestimmungen gemessen werden; über Magnesiummangel einzelner Organe kann nichts ausgesagt werden. Hinweise für Magnesiummangel geben aber neben der Messung des Serumspiegels Anamnese, klinischer Befund, bestimmte EKG-Veränderungen wie auch evtl. Hypokalämie, ein Zustand, bei dem sich oft — besonders bei Aldosteronismus — parallele Veränderungen zeigten.Tierexperimente deuten darauf hin, daß infarktähnliche Läsionen unter Magnesiummangel entstehen, doch ob Herzinfarkt beim Menschen durch Magnesiummangel ausgelöst werden kann, ist noch ungeklärt. In Leichenherzen zeigte sich im Infarktgebiet neben Calciumakkumulation signifikanter Magnesiumverlust, wobei unklar blieb, ob sich Ursache oder Folge des Infarktes widerspiegelten. Falls ein ursächlicher Zusammenhang besteht, ist er im Myokardstoffwechsel selbst zu suchen, wie bei der Alkoholkardiomyopathie, wo myokardialer Magnesiummangel zumindest als pathogenetischer Teilfaktor anerkannt wird. Andererseits versucht man aber auch Beziehungen zwischen Atherosklerose, Blutgerinnung und Hypomagnesämie herzustellen, in der Meinung, daß Magnesiummangel auch über den coronaren Pathomechanismus des Herzinfarktes wirken könnte. Sicher scheint, daß gewisse EKG-Veränderungen und Herzrhythmusstörungen durch einen irritierten Magnesiumhaushalt bedingt sein können, da sie bei Gabe bzw. Entzug von Magnesium verschwinden. Daß Magnesiummangel die Glykosidtoleranz verringert, wird tierexperimentell bestätigt. Unter Hypomagnesämie bewirkt Acetylstrophanthidin eher und länger Rhythmusstörungen als ohne, außerdem lassen diese sich durch Magnesiumgaben eliminieren. Da in gewissen Fällen spontane und digitalisinduzierte Herzrythmusstörungen durch Magnesiuminjektionen beseitigt wurden, scheint Magnesium als Therapeuticum angebracht. Einsatz verschiedener Magnesiumsalze bei Angina pectoris, degenerativen Herzerkrankungen und Herzinsuffizienz ohne geprüften und offensichtlich gestörten Magnesiumhaushalt ist fragwürdig, weil keine eindeutigen klinischen Erfolgsbeweise vorliegen. Immerhin mag es aber larvierte, durch Serumbestimmungen nicht erfaßbare Mangelzustände geben. Allgemein erscheint es aus kardiologischer Sicht ratsam, den Magnesiumhaushalt zu überwachen und in entsprechenden Fällen auszugleichen, um möglichen Myokardläsionen oder fatalen Herzrhythmusstörungen entgegenzuwirken.