Iris recognition based on key image feature extraction

In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.     

基于深度特征融合的肺炎影像识别研究

在新型冠状病毒肺炎(COVID-19)疫情背景下,肺炎影像快速准确诊断显得尤为重要.针对肺炎影像纹理及细粒度特征受噪声影响大、常规手段识别率低等问题,本研究构建了一种新的基于跨层连接机制的多主干网络特征融合卷积模型.依托并行特征挖掘思路,利用多尺度感受野挖掘融合来捕获医学图像的局部细节,实现对COVID-19医学影像的...     

基于卷积神经网络特征提取与融合的心律失常分类

本研究提出一种新的心律失常自动分类方法,辅助医生诊治心律失常。通过构建卷积神经网络对心电信号以及QRS波群的小波分量进行特征提取,将网络提取到的心电信号特征和小波特征与人工提取的RR间期特征,输入到全连接层进行融合,在输出层使用softmax函数对心拍进行分类。使用MIT-BIH心律失常数据库中的MILL导联数据对网络进行训练和测试。经测试,该方法的总体分类准确度达98.12%,平均灵敏度为87.32%,平均阳性预测值为90.37%。该方法能够快速识别不同类型的心律失常,对于计算机辅助诊断心律失常的应用具有一定的参考价值。     

基于特征融合的肝包虫病CT图像识别

目的探讨特征融合方法在肝包虫病CT图像分类识别中的应用,旨在提高肝包虫病的诊断准确率。方法选取正常肝脏和单囊型肝包虫病CT图像各150张,对每幅图像采取空域与频域滤波算法、数学形态学算法和点处理,分别得到10幅特征子图像并对它们进行特征融合。对融合后的图像提取灰度和纹理特征,通过统计学分析筛选关键特征。结果对提取的10维特征进行统计学分析,得到正常肝脏和单囊型肝包虫CT融合图像之间完全没有交集的4个灰度和1个纹理特征取值范围,以此来区分肝包虫病与正常肝脏CT图像。结论从原始图像中提取特征子图像并进行融合,再对融合后图像提取特征的方法能够很好地区分识别正常肝脏和单囊型肝包虫病CT图像,为肝包虫病的早期诊断提供依据。     

SD-MSAEs: Promoter recognition in human genome based on deep feature extraction

The prediction and recognition of promoter in human genome play an important role in DNA sequence analysis. Entropy, in Shannon sense, of information theory is a multiple utility in bioinformatic details analysis. The relative entropy estimator methods based on statistical divergence (SD) are used to extract meaningful features to distinguish different regions of DNA sequences. In this paper, we choose context feature and use a set of methods of SD to select the most effective n-mers distinguishing promoter regions from other DNA regions in human genome. Extracted from the total possible combinations of n-mers, we can get four sparse distributions based on promoter and non-promoters training samples. The informative n-mers are selected by optimizing the differentiating extents of these distributions. Specially, we combine the advantage of statistical divergence and multiple sparse auto-encoders (MSAEs) in deep learning to extract deep feature for promoter recognition. And then we apply multiple SVMs and a decision model to construct a human promoter recognition method called SD-MSAEs. Framework is flexible that it can integrate new feature extraction or new classification models freely. Experimental results show that our method has high sensitivity and specificity.     

Feature dimensionality reduction for myoelectric pattern recognition: A comparison study of feature selection and feature projection methods

This study investigates the effect of the feature dimensionality reduction strategies on the classification of surface electromyography (EMG) signals toward developing a practical myoelectric control system. Two dimensionality reduction strategies, feature selection and feature projection, were tested on both EMG feature sets, respectively. A feature selection based myoelectric pattern recognition system was introduced to select the features by eliminating the redundant features of EMG recordings instead of directly choosing a subset of EMG channels. The Markov random field (MRF) method and a forward orthogonal search algorithm were employed to evaluate the contribution of each individual feature to the classification, respectively. Our results from 15 healthy subjects indicate that, with a feature selection analysis, independent of the type of feature set, across all subjects high overall accuracies can be achieved in classification of seven different forearm motions with a small number of top ranked original EMG features obtained from the forearm muscles (average overall classification accuracy >95% with 12 selected EMG features). Compared to various feature dimensionality reduction techniques in myoelectric pattern recognition, the proposed filter-based feature selection approach is independent of the type of classification algorithms and features, which can effectively reduce the redundant information not only across different channels, but also cross different features in the same channel. This may enable robust EMG feature dimensionality reduction without needing to change ongoing, practical use of classification algorithms, an important step toward clinical utility.     

基于法线特征匹配的主动脉弓动态图像测量方法

目的研究主动脉弓动态图像测量的方法。方法提出基于法线特征匹配的主动脉弓边界动态跟踪方法,首先通过手动选点和曲线拟合的方法确定第一帧图像的边界,从而克服了阈值分割法的边界不连续的缺点,然后使用法线特征匹配的方法对主动脉弓序列图像进行边缘检测,得到平滑的主动脉弓边界曲线,实现了主动脉弓序列图像的动态跟踪。结果准确测量了主动脉弓的运动变形参数。结论成功得出了主动脉弓的运动变形规律,为主动脉弓的有限元计算打下了良好的基础。     

A threshold fuzzy entropy based feature selection for medical database classification

Feature selection is one of the most common and critical tasks in database classification. It reduces the computational cost by removing insignificant features. Consequently, this makes the diagnosis process accurate and comprehensible. This paper presents the measurement of feature relevance based on fuzzy entropy, tested with a Radial Basis Function Network classifier for a medical database classification. Three feature selection strategies are devised to obtain the valuable subset of relevant features. Five benchmarked datasets, which are available in the UCI Machine Learning Repository, have been used in this work. The classification accuracy shows that the proposed method is capable of producing good results with fewer features than the original datasets.     

Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI

Dementia is a growing concern due to the aging process of the western societies. Non-invasive detection is therefore a high priority research endeavor. In this paper we report results of classification systems applied to the feature vectors obtained by a feature extraction method computed on structural magnetic resonance imaging (sMRI) volumes for the detection of two neurological disorders with cognitive impairment: myotonic dystrophy of type 1 (MD1) and Alzheimer disease (AD). The feature extraction process is based on the voxel clusters detected by voxel-based morphometry (VBM) analysis of sMRI upon a set of patient and control subjects. This feature extraction process is specific for each kind of disease and is grounded on the findings obtained by medical experts. The 10-fold cross-validation results of several statistical and neural network based classification algorithms trained and tested on these features show high specificity and moderate sensitivity of the classifiers, suggesting that the approach is better suited for rejecting than for detecting early stages of the diseases.     

A multi-stage automatic arrhythmia recognition and classification system

This paper describes an automatic classification system based on combination of diverse features for the purpose of automatic heartbeat recognition. The method consists of three stages. At the first stage, heartbeats are classified into 5 main groups defined by AAMI using optimal feature sets for each main group. At the second stage, main groups are classified into subgroups using optimal features for each subgroup. Then the third stage is added to the system for classifying beats that are labeled as unclassified beats in the first two classification stages. A diverse set of features including higher order statistics, morphological features, Fourier transform coefficients, and higher order statistics of the wavelet package coefficients are extracted for each different type of ECG beat. At the first stage, optimal features for main groups are determined by using a wrapper type feature selection algorithm. At the second stage, optimal features are similarly selected for discriminating each subgroup of the main groups. Then at the third stage, only raw data is used for classifying beats. In all stages, the classifiers are based on the k-nearest neighbor algorithm. ECG records used in this study are obtained from the MIT-BIH arrhythmia database. The classification accuracy of the proposed system is measured by sensitivity, selectivity, and specificity measures. The system is classified 16 heartbeat types. The measures of proposed system are 85.59%, 95.46%, and 99.56%, for average sensitivity, average selectivity, and average specificity, respectively.     

An automated feature extraction and emboli detection system based on the PCA and fuzzy sets

The Doppler ultrasound technique is commonly used to detect emboli in the cerebral circulation. Here an automated feature extraction and emboli detection system is proposed based on the principal components analysis (PCA) and fuzzy sets. In the system, two features, R(ry) and k, are extracted by the PCA method. Meanwhile, MMR and sigma(f min) are obtained with the traditional temporal processing and spectrogram analysis, respectively. Normal blood flow signals are firstly distinguished from abnormal signals by MMR. Then signals containing emboli and disturbance noises are further differentiated by other features based on fuzzy sets. From experiments with computer-simulated and clinical Doppler ultrasound signals, it is shown that features extracted from the PCA method achieve better classification performance than those of traditional methods. The fuzzy-based detection system not only obtains high classification accuracy but is more applicable in clinical diagnosis.     

基于图像分割的Tamura纹理特征算法的研究与实现***☆

背景：ITK主要提供医学图像处理、分割与配准算法,但其缺少可视化的功能,缺乏灵活实用的用户界面,VTK提供了丰富的医学影像处理与分析工具,具有强大的图形处理和可视化功能。 目的：利用以前的确诊病例和医生的诊断经验以及患者的相关病史,对确诊的医学影像资源进行管理,归档,并检索,以减少人工干预,提高图像的查全率和查准率。 方法：以视觉感知机制为基础,在ITK平台上进行图像平滑去噪和分割的预处理过程,利用Tamura算法完成纹理特征提取,最后通过实验采集、计算数据,完成对比分析。 结果与结论：基于图像分割的Tamura纹理特征算法在基于图像纹理检索应用上便于相似性度量,进而可提高检索的准确率。     

Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework

Recent technological advances in machine learning offer the possibility of decoding complex datasets and discern latent patterns. In this study, we adopt Liquid State Machines (LSM) to recognize the emotional state of an individual based on EEG data. LSM were applied to a previously validated EEG dataset where subjects view a battery of emotional film clips and then rate their degree of emotion during each film based on valence, arousal, and liking levels. We introduce LSM as a model for an automatic feature extraction and prediction from raw EEG with potential extension to a wider range of applications. We also elaborate on how to exploit the separation property in LSM to build a multipurpose and anytime recognition framework, where we used one trained model to predict valence, arousal and liking levels at different durations of the input. Our simulations showed that the LSM-based framework achieve outstanding results in comparison with other works using different emotion prediction scenarios with cross validation.     

Survival analysis for high-dimensional,heterogeneous medical data: Exploring feature extraction as an alternative to feature selection

BackgroundIn clinical research, the primary interest is often the time until occurrence of an adverse event, i.e., survival analysis. Its application to electronic health records is challenging for two main reasons: (1) patient records are comprised of high-dimensional feature vectors, and (2) feature vectors are a mix of categorical and real-valued features, which implies varying statistical properties among features. To learn from high-dimensional data, researchers can choose from a wide range of methods in the fields of feature selection and feature extraction. Whereas feature selection is well studied, little work focused on utilizing feature extraction techniques for survival analysis.ResultsWe investigate how well feature extraction methods can deal with features having varying statistical properties. In particular, we consider multiview spectral embedding algorithms, which specifically have been developed for these situations. We propose to use random survival forests to accurately determine local neighborhood relations from right censored survival data. We evaluated 10 combinations of feature extraction methods and 6 survival models with and without intrinsic feature selection in the context of survival analysis on 3 clinical datasets. Our results demonstrate that for small sample sizes – less than 500 patients – models with built-in feature selection (Cox model with ℓ₁ penalty, random survival forest, and gradient boosted models) outperform feature extraction methods by a median margin of 6.3% in concordance index (inter-quartile range: [−1.2 % ;14.6 %]).ConclusionsIf the number of samples is insufficient, feature extraction methods are unable to reliably identify the underlying manifold, which makes them of limited use in these situations. For large sample sizes – in our experiments, 2500 samples or more – feature extraction methods perform as well as feature selection methods.     

Melanomas non-invasive diagnosis application based on the ABCD rule and pattern recognition image processing algorithms

In this paper an automated dermatological tool for the parameterization of melanomas is presented. The system is based on the standard ABCD Rule and dermatological Pattern Recognition protocols. On the one hand, a complete stack of algorithms for the asymmetry, border, color, and diameter parameterization were developed. On the other hand, three automatic algorithms for digital image processing have been developed in order to detect the appropriate patterns. These allow one to calculate certain quantitative features based on the aspect and inner patterns of the melanoma using simple-operation algorithms, in order to minimize response time. The database used consists of 160 500×500-pixel RGB images (20 images per pattern) cataloged by dermatologists, and the results have turned out to be successful according to assessment by medical experts. While the ABCD algorithms are mathematically reliable, the proposed algorithms for pattern recognition produced a remarkable rate of globular, reticular, and blue veil Pattern recognition, with an average above 85% of accuracy. It thus proves to be a reliable system when performing a diagnosis.     

基于高斯模型的心电特征提取研究

为精确定位R峰并提取心电特征,提出一种基于高斯模型的心电特征提取方法。首先,采用STMHT算法定位R峰;其次,基于已定位R峰确定了以6个高斯函数为每个心电节拍建立模型;最后,基于高斯模型提取心电特征。利用MIT-BIH心律失常数据库中的心电记录验证了算法性能,平均检测准确率达到99.80%。     

A novel feature extraction for robust EMG pattern recognition

This paper presents the detailed evaluation and classification of Surface Electromyogram (SEMG) signals at different upper arm muscles for different operations. After acquiring the data from selected locations, interpretation of signals was done for the estimation of parameters using simulated algorithm. First, different types of arm operations were analysed; then statistical techniques were implemented for investigating muscle force relationships in terms of amplitude estimation. The classification (Artificial Neural Network) based results have been presented for detecting different pre-defined arm motions in order to discriminate SEMG signals. The outcome of research indicates that a neural network classifier performs best with an average classification rate of 92.50%. Finally, the result also inferred the operations which were observed to be easy for arm recognition and the study is a step forward to develop powerful, flexible and efficient prosthetic designs.     

多类核共空间模式特征提取方法研究

为了缓解共空间模式（CSP）下,对脑内的源信号和记录的脑电（EEG）信号之间严格的线性模式的假设关系,需要研究一种核共空间模式（KCSP）的特征提取方法。考虑到脑-机接口（BCI）研究已经逐渐从两类的模式识别发展为多类的模式识别,因而提出了多类核共空间模式（MKCSP）的方法,该方法将KCSP方法和多类CSP方法结合起来。我们用Logistic线性分类器对提取的特征进行了分类。实验使用的数据是2005年BCI竞赛Ⅲ的数据集Ⅲ₃a。通过实验表明,本文中的方法能够从多类别的单次试验的EEG数据中提取相应的特征,并得到了较好分类结果。     

Adaptive feature extraction for EEG signal classification

One challenge in the current research of brain–computer interfaces (BCIs) is how to classify time-varying electroencephalographic (EEG) signals as accurately as possible. In this paper, we address this problem from the aspect of updating feature extractors and propose an adaptive feature extractor, namely adaptive common spatial patterns (ACSP). Through the weighed update of signal covariances, the most discriminative features related to the current brain states are extracted by the method of multi-class common spatial patterns (CSP). Pseudo-online simulations of EEG signal classification with a support vector machine (SVM) classifier for multi-class mental imagery tasks show the effectiveness of the proposed adaptive feature extractor.     

基于中层特征的医学图像分割研究进展

图像的中层特征将图像中的全局信息和局部信息结合,同时具备代表性和特异性,能够更好地表达图像的信息。已有的研究工作成功地将中层特征用于医学图像的分割,主要的方法包括稀疏编码和空间金字塔匹配(spatial pyramid matching,SPM)算法,词典学习,以及神经网络等算法。中层特征的应用提高了算法性能。本文介绍了现有的基于中层特征的医学图像分割算法,并对今后的研究工作进行了展望。