基于希尔伯特-黄变换的心音包络提取

目的 提取心音信号的包络特征,根据包络对信号进行时域分析.方法 本文提出了利用希尔伯特一黄变换对心音信号进行包络提取的方法.首先利用黄变换对心音信号进行预处理:然后对处理后的信号进行希尔伯特变换得到心音信号的包络.结果 突出了心音信号的第一心音和第二心音,准确提取了心音的各种时域特征.结论 采用此方法能够准确地提取心音信号的包络,为进行下一步分析奠定了基础.     

基于局部峰谷点插值提取心音包络的一种新方法

目的精确提取心音包络线,为心音特征的分析创造条件。方法基于局部关键点提取心音包络。首先计算信号的局部峰谷点,然后对其插值得到心音包络。结果通过分别与希尔伯特变换法、数学形态法所提取信号包络的比较,证明本方法提取的心音轮廓更加准确,突出了心音包络的时域特征。结论采用此方法能够准确地获得心音包络,为后续分析奠定了基础。     

基于小波能量谱的先天或风湿性心脏病异常心音分类算法研究

目的为解决先天性心脏病或风湿性心脏病异常心音分辨问题,应用离散小波频带能量对异常心音分类算法进行研究。方法采集22人的正常心音和116人的异常心音,对心音信号做离散小波变换,根据病理性心杂音在频域内的分布范围划分5层频带,计算得到各层频带的能量占比,根据单因素方差分析方法,提出了基于小波能量谱的心音分类指标。结果对正常心音和异常心音的4个听诊区域进行了分类,最优分类准确率为92%。区分动脉导管未闭和其余异常心音的最优分类准确率为81.9%。结论基于小波能量谱的心音分类算法无需对心音信号进行分割,提取特征值少,可准确有效地对心音信号进行分类,相较于传统的心音听诊,算法的引入能够在异常心音临床诊断中提供参考数据。     

结合静态小波变换的自适应窗口可除颤节律检测

目的为克服固定时间窗口分段导致R波截断的缺点,提出一种新的分段方法——固定R波个数的自适应窗口分段法,用于可除颤节律检测。方法首先用波谷波峰法定位ECG信号R波,以5个R波长度为窗口大小自适应分段ECG信号,得到每段信号能量、复杂度、时间长度时域特征;其次再对每段信号进行静态小波变换,获取每层小波系数与原始分段信号的相关系数时频特征;最后混合时域、时频域两类特征,输入到支持向量机、k-近邻、随机森林分类器进行信号分类,实现可除颤节律检测。结果在CUDB和VFDB两个开源数据库上对新算法进行验证比较,其准确率最高分别为:98.12%、97.19%;敏感度最高分别为:97.20%、95.88%;特异性最高分别为:98.72%、97.96%。结论新算法能够很好地实现可除颤节律的检测。     

基于WER-PCA的肥心病心杂音特征提取算法研究

目的为便捷有效地识别肥心病心音与正常心音,针对心音信号研究基于WER-PCA的肥心病心杂音特征提取算法。方法采集168例正常心音信号和194例肥心病心音信号,应用小波变换获得10维重构信号,应用主成分分析筛选出肥心病心杂音频段对应的小波分解特定层,提取心杂音频段的能量百分比,将其与正常心音的差异度相组合作为肥心病心音特征。结果对比正常心音与肥心病心音主成分构成元素,筛选出肥心病心杂音频带范围为86.15～689.05Hz;对正常心音和肥心病心音进行识别,差异度结合能量的识别特征,其最优识别正确率为95.3%。结论该算法能有效提取肥心病心杂音特征以识别肥心病。     

环境因素对暖体假人模拟潮湿服装的蒸发散热性能分析

目的 针对心音信号为便捷有效地识别肥心病心音与正常心音,研究基于WER-PCA的肥心病心杂音特征提取算法。方法 应用小波变换获得10维重构信号,应用主成分分析（PCA）筛选出肥心病心杂音频段对应的小波分解特定层,提取心杂音频段的小波能量百分比,将其与正常心音的差异度相组合作为肥心病心音特征。结果 对168例正常心音和194例肥心病心音进行识别,正常心音与肥心病心音最优识别正确率为95.3%,验证了提取出的特征的有效性。结论 该算法能有效提取肥心病心杂音特征识别肥心病,研究提出的心杂音特定频带可以为后续研究肥心病病症提供有效的参考诊断指标与方法。     

基于虚拟时频字典的心音信号压缩方法

目的提供用于长期监测心音信号或心音信号进行远程传输时,压缩心音信号以减少数据量的方法。方法虚拟出一个时频参数的树形字典。该字典规模庞大,但它仅在逻辑上存在,无需保存于存储器中。通过短时傅里叶变换将心音信号分解成若干时频分量,并从虚拟字典中检索出各分量的索引。只需记录时频分量的发生时刻和索引值,就可恢复出心音信号,从而降低存储量,获得较高的压缩比。结果以4 k Hz的采样率为例,相关系数至少高于0.97的条件下,正常心音信号的压缩比介于36~45之间;带心杂音的心音信号,压缩比介于11~30之间。结论本文算法在保留诊断信息(强相关)的前提下,大幅度降低了心音信号的存储量,取得了较好的压缩效果。     

常规胸腹部MDCT对骨质疏松性椎体骨折的自动检测

正摘要目的创建一种MDCT影像脊柱自动分段的原始数据算法,并利用其自动检出骨质疏松性椎体骨折。方法回顾性选择71例常规胸腹部横断MDCT检查病人。包括男     

详述半自动匹配算法用于脑动静脉畸形分段:初步研究

<正>摘要目的本研究摘要目的是用半自动分段算法在3D旋转血管造影(3D-RA)上区别脑动静脉畸形(b AVM)的不同组成。材料与方法 15例病人(男8例,女7例;14例幕上     

用数字滤波及相关函数对EOAE信号进行二次处理

用数字滤波及相关函数处理技术对10名(20耳)学龄前儿童短声诱发的EOAE信号进行二次处理.分析结果表明,通过滤波及相关处理后的EOAE信号比原始记录的EOAE信号波形更加清晰,易于辨认,包络波形明显成形,有效地提高了EOAE信号的信噪比及识别率.     

Evaluation of manual and automatic segmentation of the mouse heart from CINE MR images

PURPOSE: To compare global functional parameters determined from a stack of cinematographic MR images of mouse heart by a manual segmentation and an automatic segmentation algorithm. MATERIALS AND METHODS: The manual and automatic segmentation results of 22 mouse hearts were compared. The automatic segmentation was based on propagation of a minimum cost algorithm in polar space starting from manually drawn contours in one heart phase. Intra- and interobserver variability as well as validity of the automatic segmentation was determined. To test the reproducibility of the algorithm the variability was calculated from the intra- and interobserver input. RESULTS: The mean time of segmentation for one dataset was around 10 minutes and approximately 2.5 hours for automatic and manual segmentation, respectively. There were no significant differences between the automatic and the manual segmentation except for the end systolic epicardial volume. The automatically derived volumes correlated well with the manually derived volumes (R(2) = 0.90); left ventricular mass with and without papillary muscle showed a correlation R(2) of 0.74 and 0.76, respectively. The manual intraobserver variability was superior to the interobserver variability and the variability of the automatic segmentation, while the manual interobserver variability was comparable to the variability of the automatic segmentation. The automatic segmentation algorithm reduced the bias of the intra- and interobserver variability. CONCLUSION: We conclude that automatic segmentation of the mouse heart provides a fast and valid alternative to manual segmentation of the mouse heart.     

Fully automatic segmentation of the brain from T1-weighted MRI using Bridge Burner algorithm

PURPOSE: To validate Bridge Burner, a new brain segmentation algorithm based on thresholding, connectivity, surface detection, and a new operator of constrained growing. MATERIALS AND METHODS: T1-weighted MR images were selected at random from three previous neuroimaging studies to represent a spectrum of system manufacturers, pulse sequences, subject ages, genders, and neurological conditions. The ground truth consisted of brain masks generated manually by a consensus of expert observers. All cases were segmented using a common set of parameters. RESULTS: Bridge Burner segmentation errors were 3.4% +/- 1.3% (volume mismatch) and 0.34 +/- 0.17 mm (surface mismatch). The disagreement among experts was 3.8% +/- 2.0% (volume mismatch) and 0.48 +/- 0.49 mm (surface mismatch). The error obtained using the brain extraction tool (BET), a widely used brain segmentation program, was 8.3% +/- 9.1%. Bridge Burner brain masks are visually similar to the masks generated by human experts. Areas affected by signal intensity nonuniformity artifacts were occasionally undersegmented, and meninges and large sinuses were often falsely classified as the brain tissue. Segmentation of one MRI dataset takes seven seconds. CONCLUSION: The new fully automatic algorithm appears to provide accurate brain segmentation from high-resolution T1-weighted MR images.     

Global left ventricular function in cardiac CT. Evaluation of an automated 3D region-growing segmentation algorithm

The purpose was to evaluate a new semi-automated 3D region-growing segmentation algorithm for functional analysis of the left ventricle in multislice CT (MSCT) of the heart. Twenty patients underwent contrast-enhanced MSCT of the heart (collimation 16×0.75 mm; 120 kV; 550 mAseff). Multiphase image reconstructions with 1-mm axial slices and 8-mm short-axis slices were performed. Left ventricular volume measurements (end-diastolic volume, end-systolic volume, ejection fraction and stroke volume) from manually drawn endocardial contours in the short axis slices were compared to semi-automated region-growing segmentation of the left ventricle from the 1-mm axial slices. The post-processing-time for both methods was recorded. Applying the new region-growing algorithm in 13/20 patients (65%), proper segmentation of the left ventricle was feasible. In these patients, the signal-to-noise ratio was higher than in the remaining patients (3.2±1.0 vs. 2.6±0.6). Volume measurements of both segmentation algorithms showed an excellent correlation (all P≤0.0001); the limits of agreement for the ejection fraction were 2.3±8.3 ml. In the patients with proper segmentation the mean post-processing time using the region-growing algorithm was diminished by 44.2%. On the basis of a good contrast-enhanced data set, a left ventricular volume analysis using the new semi-automated region-growing segmentation algorithm is technically feasible, accurate and more time-effective.     

Directional correlation in white matter tracks of the human brain

PURPOSE: To describe a technique for the detection of distinct brain fibers in sets of magnetic resonance (MR) diffusion tensor imaging (DTI) data. MATERIALS AND METHODS: MR-DTI can be used for a tractography of brain fibers presuming a data set of high spatial resolution and high signal to noise. A less demanding technique for the visualization of discrete brain fiber bundles involves segmentation. By using a region-growing algorithm, those voxels that have a direction similar to that of the major eigenvector in neighboring voxels of a data set can be marked. It has been shown recently by Mori et al (1) that this technique can be successfully applied to data from a single slice of a mouse brain. In this study, the segmentation technique was applied with modifications to multislice DTI data from the human brain. RESULTS: A distinct segmentation of various brain fiber bundles could be achieved by the use of a two-step algorithm. In the first step, voxels within large fiber tracts-such as corticofugal tracts (e.g., corticospinal tract) and the optic radiation-were segmented by starting the region-growing algorithm in the corpus callosum (CC) and erasing this major structure from the data set. In the second step, remaining voxels were segmented by the same algorithm; this revealed a good assignment of the similarly oriented fibers derived by segmentation to the anatomically given brain lobes. This two-step procedure was successfully applied to DTI data of six healthy volunteers. CONCLUSION: The segmentation technique for DTI data proposed by Mori et al (1) for data from mouse brains can be applied to multislice data from the human brain by using a two-step algorithm including a masking of the major fiber tracts.     

Automated knowledge-guided segmentation of colonic walls for computerized detection of polyps in CT colonography

PURPOSE: We have developed a novel automated technique for segmenting colonic walls for the application of computer-aided polyp detection in CT colonography. In particular, the technique was designed to minimize the presence of extracolonic components, such as small bowel, in the segmented colon. METHODS: The segmentation technique combines an improved version of our previously reported anatomy-oriented colon segmentation technique with a colon-based analysis step that performs self-adjusting volume-growing within the colonic lumen. Extracolonic components are eliminated by intersecting of the resulting two segmentations, so that the colonic walls remain in the intersection. The technique was evaluated on 88 CT colonography datasets. The colon segmentations were evaluated subjectively by four radiologists, as well as objectively by performance of an automated polyp detection on the segmentation. For comparison, the tests were also performed for the anatomy-oriented colon segmentation technique. RESULTS: On average, the technique covered 98% of the visible colonic walls. Approximately 50% of the extracolonic components remaining in the anatomy-oriented segmentation were removed, but 10-15% of the segmentation still contained extracolonic components. The dataset-based false-positive rate of the automated polyp detection was improved by 10% without compromising the 100% case-based sensitivity, and the case-based false-positive rate was improved by 15% over the previous false-positive rate. CONCLUSIONS: The technique segments practically all of the colonic walls in the region of diagnostic quality with a large reduction in the amount of extracolonic components over our previously used technique. The new segmentation improves the specificity of our computer-aided polyp detection scheme significantly without any degradation in detection sensitivity.     

Application of region-based segmentation and neural network edge detection to skin lesions.

This paper proposes two approaches to the skin lesion image segmentation problem. The first is a mainly region-based segmentation method where an optimal threshold is determined iteratively by an isodata algorithm. The second method proposed is based on neural network edge detection and a rational Gaussian curve that fits an approximate closed elastic curve between the recognized neural network edge patterns. A quantitative comparison of the techniques is enabled by the use of synthetic lesions to which Gaussian noise is added. The proposed techniques are also compared with an established automatic skin segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties the iterative thresholding method provides the best performance over a range of signal to noise ratios. Iterative thresholding technique is also demonstrated to have similar performance when tested on real skin lesions.     

Automated lung segmentation for thoracic CT impact on computer-aided diagnosis

RATIONALE AND OBJECTIVES: Automated lung segmentation in thoracic computed tomography scans is essential for the development of computer-aided diagnostic (CAD) methods. A core segmentation method may be developed for general application; however, modifications may be required for specific clinical tasks. MATERIALS AND METHODS: An automated lung segmentation method has been applied (1) as preprocessing for automated lung nodule detection and (2) as the foundation for computer-assisted measurements of pleural mesothelioma tumor thickness. The core method uses gray-level thresholding to segment the lungs within each computed tomography section. The segmentation is revised through separation of right and left lungs along the anterior junction line, elimination of the trachea and main bronchi from the lung segmentation regions, and suppression of the diaphragm. Segmentation modifications required for nodule detection include a rolling ball algorithm to include juxtapleural nodules and morphologic erosion to eliminate partial volume pixels at the boundary of the segmentation regions. RESULTS: For automated lung nodule detection, 4 of 82 actual nodules (4.9%) were excluded from the lung segmentation regions when the core segmentation method was modified compared with 14 nodules (17.1%) excluded without modifications. The computer-assisted quantification of mesothelioma method achieved a correlation coefficient of 0.990 with 134 manual measurements when the core segmentation method was used alone; correlation was reduced to 0.977 when the segmentation modifications, as adapted for the lung nodule detection task, were applied to the mesothelioma measurement task. CONCLUSION: Different CAD applications impose different requirements on the automated lung segmentation process. The specific approach to lung segmentation must be adapted to the particular CAD task.     

Development of automated segmentation method for the posterior portion of the temporal lobe on coronal MR images

Brain MRI is an important method for examining the diseases caused by various cerebral pathologies, and the measurement of temporal lobe volume is useful for identifying dementia and temporal lobe abnormalities. However, no segmentation algorithm for the temporal lobe on coronal MR images has been established. Such an algorithm is needed because the shape of the temporal lobe on coronal images varies from area to area. The purpose of this research was to develop a segmentation method for the posterior portion of the temporal lobe on coronal MR images. The subjects were 11 normal patients, whose coronal T(1)-weighted images were selected for this study. The preprocessing algorithm for segmentation consists of smoothing, binarization, and thinning. The first step of the segmentation process consists of recognition techniques for the temporal lobe region on thinning images. The next step is distance transformation on identified thinning images. Finally, the temporal lobe was segmented by using the original images and distance transformation images and employing the newly developed algorithm. The rate of accuracy of automated recognition was over 74% for all cases, while the average rate of accuracy was 83.2+/-4.0%. These results suggest that this segmentation method can clearly segment the temporal lobe and has potential for clinical use. Based on this study, although it included only 11 normal patients, we have started applying this segmentation method to many patients, with or without temporal lobe disease.