Ultrasound image matching using genetic algorithms

Image compounding reduces the artifacts inherent in ultrasound imaging, but accurate matching of images for compounding depends on their accurate placement in the compound image plane. A method is presented to reduce displacement errors during compounding of ultrasound B-scans of a normal human shank. A genetic algorithm was used to place matching B-scans in the compound image. The method was tested on a phantom and was shown to reduce, but not eliminate, mismatches due to the displacement of B-scans from their original position in the compound image plane. The results can be extended to applications in lower-limb prosthetics, where ultrasound imaging can be used to visualise the internal geometry of amputees' residual limbs.     

Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm

Detection of seizures in EEG can be challenging because of myogenic artifacts and might be time-consuming. In this study, we propose a method using subband nonlinear parameters and genetic algorithm for automatic seizure detection in EEG. In the experiment, the discrete wavelet transform was used to decompose EEG into five subband components. Nonlinear parameters were extracted and employed as the features to train the support vector machine with linear kernel function (SVML) and radial basis function kernel function (SVMRBF) classifiers. A genetic algorithm (GA) was used for selecting the effective feature subset. The seizure detection sensitivities of the SVML and the SVMRBF with GA are 90.8% and 94.0%, respectively. The sensitivity of SVMRBF increases to 95.8% by using GA for weight adjustment. Moreover, the proposed method is capable of discriminating the interictal EEG of epileptic subjects from the normal EEG, which is considered difficult, yet crucial, in clinical services.     

Atrial cell action potential parameter fitting using genetic algorithms

Understanding of the considerable variation in action potential (AP) shape throughout the heart is necessary to explain normal and pathological cardiac function. Existing mathematical models reproduce typical APs, but not all measured APs, as fitting the sets of non-linear equations is a tedious process. The study describes the integration of a pre-existing mathematical model of an atrial cell AP with a genetic algorithm to provide an automated tool to generate APs for arbitrary cells by fitting ionic channel conductances. Using the Nygren model as the base, the technique was first verified by starting with random values and fitting the Nygren model to itself with an error of only 0.03%. The Courtemanche model, which has a different morphology from that of the Nygren model, was successfully fitted. The AP duration restitution curve generated by the fit matched that of the target model very well. Finally, experimentally recorded APs were reproduced. To match AP duration restitution behaviour properly, it was necessary simultaneously to fit over several stimulation frequencies. Also, fitting of the upstroke was better if the stimulating current pulse replicated that foundin situ as opposed to a rectangular pulse. In conclusion, the modelled parameters were successfully able to reproduce any given atrial AP. This tool can be useful for determining parameters in new AP models, reproducing specific APs, as well as determining the locus of drug action by examining changes in conductance values.     

医学图象匹配的遗传算法方法

本文用变形轮廓线的方法实现医学图象匹配过程中的对应标记点问题,并用遗传算法进行最佳匹配结果的搜索。遗传算法用变形轮廓的能量函数作为适应度函数,并通过动态映射方法实现搜索空间尺度的自适应变化。实验结果表明,本文的方法能有效地用于建立医学图谱和图象之间的一一对应关系,是对以往人工交互标注方法的重要改进。     

Cancer gene search with data-mining and genetic algorithms

Cancer leads to approximately 25% of all mortalities, making it the second leading cause of death in the United States. Early and accurate detection of cancer is critical to the well being of patients. Analysis of gene expression data leads to cancer identification and classification, which will facilitate proper treatment selection and drug development. Gene expression data sets for ovarian, prostate, and lung cancer were analyzed in this research. An integrated gene-search algorithm for genetic expression data analysis was proposed. This integrated algorithm involves a genetic algorithm and correlation-based heuristics for data preprocessing (on partitioned data sets) and data mining (decision tree and support vector machines algorithms) for making predictions. Knowledge derived by the proposed algorithm has high classification accuracy with the ability to identify the most significant genes. Bagging and stacking algorithms were applied to further enhance the classification accuracy. The results were compared with that reported in the literature. Mapping of genotype information to the phenotype parameters will ultimately reduce the cost and complexity of cancer detection and classification.     

A new approach to detect QRS complexes based on a histogram and genetic algorithm

A new approach, based on a histogram and genetic algorithm (GA), has been developed to rapidly and reliably detect QRS complexes of the ECG curve. Here, the new approach is described and the principle of the histogram and GA is explained. The results of detection of QRS complexes using the new method are given. Additionally, as an example of its novel application, the P-wave and f-wave (when atrial fibrillation is absent and present, respectively) are extracted easily and efficiently from the ECG curve using the method.     

Image reconstruction for positron emission tomography using fuzzy nonlinear anisotropic diffusion penalty

Iterative algorithms such as maximum likelihood-expectation maximization (ML-EM) become the standard for the reconstruction in emission computed tomography. However, such algorithms are sensitive to noise artifacts so that the reconstruction begins to degrade when the number of iterations reaches a certain value. In this paper, we have investigated a new iterative algorithm for penalized-likelihood image reconstruction that uses the fuzzy nonlinear anisotropic diffusion (AD) as a penalty function. The proposed algorithm does not suffer from the same problem as that of ML-EM algorithm, and it converges to a low noisy solution even if the iteration number is high. The fuzzy reasoning instead of a nonnegative monotonically decreasing function was used to calculate the diffusion coefficients which control the whole diffusion. Thus, the diffusion strength is controlled by fuzzy rules expressed in a linguistic form. The proposed method makes use of the advantages of fuzzy set theory in dealing with uncertain problems and nonlinear AD techniques in removing the noise as well as preserving the edges. Quantitative analysis shows that the proposed reconstruction algorithm is suitable to produce better reconstructed images when compared with ML-EM, ordered subsets EM (OS-EM), Gaussian-MAP, MRP, TV-EM reconstructed images.     

Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function

A new restoration methodology is proposed to enhance mammographic images through the improvement of contrast features and the simultaneous suppression of noise. Denoising is performed in the first step using the Anscombe transformation to convert the signal-dependent quantum noise into an approximately signal-independent Gaussian additive noise. In the Anscombe domain, noise is filtered through an adaptive Wiener filter, whose parameters are obtained by considering local image statistics. In the second step, a filter based on the modulation transfer function of the imaging system in the whole radiation field is applied for image enhancement. This methodology can be used as a preprocessing module for computer-aided detection (CAD) systems to improve the performance of breast cancer screening. A preliminary assessment of the restoration algorithm was performed using synthetic images with different levels of quantum noise. Afterward, we evaluated the effect of the preprocessing on the performance of a previously developed CAD system for clustered microcalcification detection in mammographic images. The results from the synthetic images showed an increase of up to 11.5 dB (p = 0.002) in the peak signal-to-noise ratio. Moreover, the mean structural similarity index increased up to 8.3 % (p < 0.001). Regarding CAD performance, the results suggested that the preprocessing increased the detectability of microcalcifications in mammographic images without increasing the false-positive rates. Receiver operating characteristic analysis revealed an average increase of 14.1 % (p = 0.01) in overall CAD performance when restored image sets were used.     

基于非线性变换法提高平扫头颅CT的灰白质对比度

目的 采用矩阵实验室(MATLAB)图像处理技术构建一种非线性变换法,用以提高平扫头颅CT图像的脑灰、白质对比度.方法 使用西门子definition型双源CT采集38例(女16例,男22例)在本院就诊怀疑脑部病变的患者脑正常平扫图像数据,测量脑灰、白质的平均CT值,并计算对应的像素值,对各例DICOM图像行MATLAB后处理.通过频域内的圆形滤波器,实现图像的高、低通滤波分离.采用基于灰度值调整的非线性变换法,以脑灰质和脑白质的平均灰度值为两个转折点,对图像的灰度进行拉伸,以增强图像的灰白质对比.结果 38例患者的正常脑平扫图像,经灰度值非线性变换法处理后,两转折点处灰度值差值较原始图像增加了1.5个像素值(0＜△p≤3);脑灰质和脑白质对比度也明显提高;灰白质分界更加清楚;且处理后图像与原始图像保持接近.结论 基于MATLAB图像处理软件的灰度值非线性变换法,能够在不增加噪声的前提下,有效增强平扫头颅CT图像的灰白质对比度,适用于任何CT机型采集的DICOM图像数据.     

基于粗配准和混合互信息的医学图像配准算法

利用边缘检测算子和数学形态学方法提取医学图像轮廓,采用主轴矩法配准两幅图像的轮廓,使其达到粗略配准的目的。通常的互信息测度是基于香农熵的,因为香农熵对于局部极值具有很强吸引域,而某些参数下的雷尼熵可以消除局部极值,将两种测度分别用于精配过程中不同的搜索阶段。首先利用全局搜索算法,寻找基于雷尼熵的归一化互信息测度的全局最优初值,再通过局部优化算法对当前的最优解寻找基于香农熵的更精确的全局最优解。实验表明,这种配准算法是有效的。     

A quantitative approach of using genetic algorithm in designing a probability scoring system of an adverse drug reaction assessment system

BACKGROUND: The detrimental effects of adverse drug reactions (ADRs) are well established. Hence, precise and accurate assessment of ADRs' causality which can differentiate signal from noise is crucial in screening, management and minimisation of ADRs. OBJECTIVE: The current study reported our attempt to improve the scoring system of a previously published algorithm of ADR assessment by our group using a genetic algorithm approach so that the final score can measure the probability of ADR causality. DESIGN: Using ADR cases obtained from the Centre for Drug Administration, the national centre for pharmacovigilance in Singapore, with known causality probability values as reference points, rules were developed to define possible combinations of criteria for 'Definite' ADR cases and 'Probable' ADR cases. A new scoring system was developed using these parameters with the help of genetic algorithm, and tested on 37 'Definite' and 431 'Not Definite' ADR cases. In addition, sensitivity and specificity analysis were performed to allow a comparison of performance between our algorithm and that used by the Adverse Drug Reaction Advisory Committee in Australia (ADRAC). RESULTS: The new scoring system is able to provide a probability of the causality of an ADR by a suspected drug. When applied to the 'Definite' and 'Not Definite' ADR reports, the new algorithm gave a sensitivity of 83.8% and specificity of 71.0%. CONCLUSIONS: Using a quantitative method of assessing causality in the new algorithm allows rare and new ADRs to be more readily identified since a quantitative score can give a more precise degree of ADR causality. This scoring system that provides a probability score would help to make this algorithm more informative and assistive for clinicians, regulatory agencies or pharmaceutical companies to generate ADR alerts. The higher sensitivity value displayed by our algorithm also shows that it would be a good ADR screening tool.     

几种图像去噪算法的仿真分析及其在fMRI数据处理中的应用

目的去除图像噪声是医学图像处理过程中的基本预处理步骤,对图像的后继分析处理的质量有重大影响。本文基于图像去噪和医学图像的诊断准确率息息相关这一现实问题,对几种图像去噪算法进行仿真分析,并实现功能磁共振(functional magnetic resonance imaging,f MRI)数据应用。方法首先阐述了几种常用图像去噪算法的基本原理,其次使用不同算法对加入高斯噪声的Lena图像进行去噪仿真,并对不同结果的峰值信噪比(peak signal-to-noise ratio,PSNR)和均方差(mean square error,MSE)进行比较,最后总结并选择最优降噪算法应用于f MRI数据分析中,以期获得较好的后期处理基础。结果小波分层阈值算法在f MRI处理中的峰值信噪比和均方差更优。结论在f MRI图像去噪过程中,利用小波分层阈值算法更能提高图像的信息利用率,有助于提高医师诊断的准确率。     

Screening of knee-joint vibroarthrographic signals using the strict 2-surface proximal classifier and genetic algorithm

We propose the strict 2-surface proximal (S2SP) classifier, by seeking two cross proximal planes to fit the distribution of the given samples in a corresponding feature space. The method is applied to screen knee-joint vibration or vibroarthrographic (VAG) signals based on statistical parameters derived from signals and selected by the genetic algorithm. A database of 89 VAG signals was studied. With the leave-one-out procedure, the linear S2SP classifier provided an efficiency of 0.82 in terms of the area under the receiver operating characteristics curve (A(z)); the nonlinear S2SP classifier provided 0.95 in A(z) value using the Gaussian kernel, and possessed good robustness around the selected kernel parameter.     

A hybrid genetic algorithm-queuing multi-compartment model for optimizing inpatient bed occupancy and associated costs

PurposeExplore how efficient intelligent decision support systems, both easily understandable and straightforwardly implemented, can help modern hospital managers to optimize both bed occupancy and utilization costs.Methods and materialsThis paper proposes a hybrid genetic algorithm-queuing multi-compartment model for the patient flow in hospitals. A finite capacity queuing model with phase-type service distribution is combined with a compartmental model, and an associated cost model is set up. An evolutionary-based approach is used for enhancing the ability to optimize both bed management and associated costs. In addition, a “What-if analysis” shows how changing the model parameters could improve performance while controlling costs. The study uses bed-occupancy data collected at the Department of Geriatric Medicine – St. George's Hospital, London, period 1969–1984, and January 2000.ResultsThe hybrid model revealed that a bed-occupancy exceeding 91%, implying a patient rejection rate around 1.1%, can be carried out with 159 beds plus 8 unstaffed beds. The same holding and penalty costs, but significantly different bed allocations (156 vs. 184 staffed beds, and 8 vs. 9 unstaffed beds, respectively) will result in significantly different costs (£755 vs. £1172). Moreover, once the arrival rate exceeds 7 patient/day, the costs associated to the finite capacity system become significantly smaller than those associated to an Erlang B queuing model (£134 vs. £947).ConclusionEncoding the whole information provided by both the queuing system and the cost model through chromosomes, the genetic algorithm represents an efficient tool in optimizing the bed allocation and associated costs. The methodology can be extended to different medical departments with minor modifications in structure and parameterization.     

河北省手足口病病例的病原构成及EV71病毒基因特征分析

目的了解河北省不同地区、不同严重程度手足口病病例的病原构成情况及EV71病毒的基因特征。方法采集河北省不同地区的HFMD患者粪便、疱疹液、咽拭子标本进行核酸检测和病毒分离,同时结合所收集的HFMD患病例的居住地、疾病严重程度信息加以分析。选取18株EV71阳性分离株进行VP1编码区基因扩增和核苷酸序列测定和分析,与其它38株各基因型和基因亚型的EV71代表株构建系统发生树。结果2009年河北省HFMD临床诊断病例的EV阳性率为65．13％,其中以EV71为主,占阳性病例的58．0％（752／1296）。秦皇岛、邯郸、保定、邢台地区手足口病例以EV71感染为主,而衡水、沧州等地区则以CA16为主。轻型病例中EV71阳性率为37．74％,重症病例中EV71阳性率为80．64％,死亡病例检测13例,均为EV71阳性。18株EV71分离株的VP114核苷酸同源性为94．9％～99．8％,与C4亚型代表株的VP1区核苷酸同源性最高,为91．9％～99．6％。进化树结果显示,河北省EV71分离株与c4亚型代表株处于同一分支,并在C4a进化分支的不同簇中。结论2009年引起河北省手足口病流行的病原体主要为EV71和CA16。秦皇岛、邯郸、保定、邢台地区手足口病例以EV7I感染为主,而衡水、廊坊、沧州等地区则以CA16为主。EV71是重症病例的主要致病病原体。河北省EV71分离株为c4亚型C4a进化分支。     

Automatic detection of solitary lung nodules using quality threshold clustering,genetic algorithm and diversity index

ObjectiveThe present work has the objective of developing an automatic methodology for the detection of lung nodules.MethodologyThe proposed methodology is based on image processing and pattern recognition techniques and can be summarized in three stages. In the first stage, the extraction and reconstruction of the pulmonary parenchyma is carried out and then enhanced to highlight its structures. In the second stage, nodule candidates are segmented. Finally, in the third stage, shape and texture features are extracted, selected and then classified using a support vector machine.ResultsIn the testing stage, with 140 new exams from the Lung Image Database Consortium image collection, 80% of which are for training and 20% are for testing, good results were achieved, as indicated by a sensitivity of 85.91%, a specificity of 97.70% and an accuracy of 97.55%, with a false positive rate of 1.82 per exam and 0.008 per slice and an area under the free response operating characteristic of 0.8062.ConclusionLung cancer presents the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis considerably increases the survival chance of patients. The methodology proposed herein contributes to this diagnosis by being a useful tool for specialists who are attempting to detect nodules.     

Mirandese language and genetic differentiation in Iberia: a study using X chromosome markers

Background: In the Iberian Peninsula, the Mirandese dialect, spoken in Miranda do Douro (Portugal) close to the north-eastern border with Spain, has attracted much attention.

Aim, subjects and methods: This study focuses on providing further insight into the connections forged between Miranda do Douro and regions in the nearby Province of Zamora. This is in order to better assess the extent to which such relations could have been detained by the current patterns of genetic diversity of the populations, whilst contributing to refining the knowledge on patterns of micro-differentiation within the Peninsula. The genetic characterization of both populations was performed through the analysis of X-chromosomal markers: X-STRs and X-indels.

Results and conclusion: The results showed that Miranda do Douro tended to present slightly lower levels of diversity in comparison to the other studied regions, which can be a discreet sign of isolation of that population over the years that might have led the way to the preservation of a language not spoken anywhere else in the country. The analysis of X-STRs particularly brought to light the presence of a subtle population sub-structure at the micro-geographical area encompassing the north-eastern border, which seems to portray the importance of the political border as a mechanism withholding gene flow between the two countries.     

On-line method for enhancement of electroencephalogram signals in presence of electro-oculogram artefacts using nonlinear recursive least squares technique

A record of the human scalp potentials (EEG) is one of the most commonly used techniques for understanding the brain functions. While recording EEG signals, other undesired signals called artefacts get superimposed on the EEG signals. Of these, electro-oculogram generated by eye movements is found to be the most significant and common. Conventional artefact minimisation schemes use linear estimation models. However, the result in sub-optimal schemes as signal processing in the human brain is highly nonlinear. In this paper therefore an on-line technique is suggested for artefact minimisation using nonlinear recursive least squares. Results confirm the efficacy of the method.     

Mathematical modeling and application of genetic algorithm to parameter estimation in signal transduction: trafficking and promiscuous coupling of G-protein coupled receptors

G-protein-coupled receptors (GPCRs) constitute a large and diverse family of proteins whose primary function is to transduce extracellular stimuli into intracellular signals. These receptors play a critical role in signal transduction, and are among the most important pharmacological drug targets. Upon binding of extracellular ligands, these receptor molecules couple to one or several subtypes of G-protein which reside at the intracellular side of the plasma membrane to trigger intracellular signaling events. The question of how GPCRs select and activate a single or multiple G-protein subtype(s) has been the topic of intense investigations. Evidence is also accumulating; however, that certain GPCRs can be internalized via lipid rafts and caveolae. In many cases, the mechanisms responsible for this still remain to be elucidated. In this work, we extend the mathematical model proposed by Chen et al. [Modelling of signalling via G-protein coupled receptors: pathway-dependent agonist potency and efficacy, Bull. Math. Biol. 65 (5) (2003) 933–958] to take into account internalization, recycling, degradation and synthesis of the receptors. In constructing the model, we assume that the receptors can exist in multiple conformational states allowing for a multiple effecter pathways. As data on kinetic reaction rates in the signalling processes measured in reliable in vivo and in vitro experiments is currently limited to a small number of known values. In this paper, we also apply a genetic algorithm (GA) to estimate the parameter values in our model.     

A genetic analysis of serotonergic biosynthetic and metabolic enzymes in migraine using a DNA pooling approach

Migraine is a common debilitating primary headache disorder with significant mental, physical and social health implications. The brain neurotransmitter 5-hydroxytryptamine (5-HT; serotonin) is involved in nociceptive pathways and has been implicated in the pathophysiology of migraine. With few genetic studies investigating biosynthetic and metabolic enzymes governing the rate of 5-HT activity and their relationship to migraine, it was the objective of this study to assess genetic variants within the human tryptophan hydroxylase (TPH), amino acid decarboxylase (AADC) and monoamine oxidase A (MAOA) genes in migraine susceptibility. This objective was undertaken using a high-throughput DNA pooling experimental design, which proved to be a very accurate, sensitive and specific method of estimating allele frequencies for single nucleotide polymorphism, insertion deletion and variable number tandem repeat loci. Application of DNA pooling to a wide array of genetic loci provides greater scope in the assessment of population-based genetic association study designs. Despite the application of this high-throughput genotyping method, negative results from the two-stage DNA pooling design used to screen loci within the TPH, AADC and MAOA genes did not support their role in migraine susceptibility.