基于拉曼光谱法的品牌仿冒药检测方法研究

目的针对目前市面上的品牌药仿冒的制假现象,以国内多个厂家生产的头孢氨苄胶囊为工具药,建立基于支持向量机和相似度法或主成分分析的两步判别方法。方法第一步,通过模拟建立头孢氨苄胶囊品牌药及4种仿冒药的分类模型,建模交叉验证的准确率为95.63%,并对可疑样品进行预测分类,准确率为93.75%,以自制样品作为额外的预测集,假阳性率为25%。第二步,对阳性样品进行相似度计算或主成分分析,可分别将假阳性率降低至5%和0%。结果与结论两步判别法可快速、准确地实现对品牌仿冒药的检测。     

基于支持向量机的乳腺病变检测

目的：利用支持向量机(SVM)对乳腺X光片图像中的病变区域进行检测和分类，识别出含钙化点区域和肿瘤区域。方法：在对目标区域加特定方形窗处理后，提取直接参数、灰度共生矩阵参数和频域参数，分别作为SVM分类器的输入进行训练和测试，并与3种参数同时输入的结果进行比较。结果：单独使用直接参数，频域参数和灰度共生矩阵参数的分类结果分别是92．28％、90.35％和91．12％，而3种参数结合的结果是99．23％。结论：所提取的3种参数可以较好地反映含钙化点区域、肿瘤区域和正常区域的特征，使用SVM分类器进行分类后取得了很好的效果，基本上可以准确识别出3种区域。     

基于CSSD和SVM的抑郁症脑电信号分类

从EEG脑电信号中提取与疾病相关的信息以实现对抑郁症的自动诊断。首先采用共空域子空间分解(CSSD)方法,对躁狂型抑郁症患者与健康人两组的16导联脑电信号进行特征提取,然后用支持向量机(SVM)分类器进行训练和分类测试。实验结果表明,相对于用小波变换提取的频率相关参数为分类特征的分类准确率为88%,采用CSSD方法提取特征参数进行分类可以取得更理想的效果为95%,后者的16导联脑电信号在空间模型上表现出较高的模式可分性。该研究成果对精神抑郁症的物理诊断和研究提供了新的视角。     

加权SVM在中医舌象分类与识别中的应用研究

加权SVM是解决不均衡舌象样本分类和识别的有效方法，但在边界支撑向量很少是错分样本的情况下，已有方法有时不能取得良好的效果。本研究首先采用已有的加权方法进行舌象样本的分类与识别，在训练结果不能使得大小样本的分类率得到很好均衡的情形下，分别进一步加大和缩小重要样本和非重要样本的惩罚项，直到满足要求为止。理论分析和实验均表明该方法的有效性。     

基于超声图像特征区分子宫肌瘤和腺肌病

超声图像子宫肌瘤和腺肌病的区分目前主要依赖于医生的经验,缺乏客观的指标.为提高区分的性能,提出一种新的对子宫肌瘤和腺肌病的超声图像进行多分辨率分析的自动分类方法.提取图像在多分辨率下的纹理参数,同时结合计算出的带方向分形维数,建立支撑矢量机进行子宫肌瘤和腺肌病的分类判决.通过对27例正常、45例腺肌病和74例肌瘤离体样本超声图像进行分析,结果表明：提取的多分辨率纹理参数和带方向的分形维数对区分子宫肌瘤和腺肌病是敏感的,结合这两类参数建立的支撑矢量机区分子宫肌瘤和腺肌病的正确率近100%.     

Classification of hyperspectral medical tongue images for tongue diagnosis

Human tongue is one of the important organs of the body, which carries abound of information of the health status. The images of the human tongue that are used in computerized tongue diagnosis of traditional Chinese medicine (TCM) are all RGB color images captured with color CCD cameras currently. However, this conversional method impedes the accurate analysis on the subjects of tongue surface because of the influence of illumination and tongue pose. To address this problem, this paper presents a novel approach to analyze the tongue surface information based on hyperspectral medical tongue images with support vector machines. The experimental results based on chronic Cholecystitis patients and healthy volunteers illustrate its effectiveness.     

基于小波包分析和SVM的在线手写签名鉴别

针对在线手写签名难以提取有效特征的实际情况,提出用小波包分解和单支重构来构造能量特征向量的方法,直接利用各频段成分能量的变化来反映签名的动态特征。给出了衡量各特征识别能力的Fisher准则,并且基于该准则剔除了识别能力差的特征,优化了特征空间。用该方法构造的特征向量能突出反映签名的动态特征。然后采用SVM对签名进行识别。实验证明:采用本文方法识别的正确率高达99.38%,错误拒绝率FRR=0.25%,错误接受率FAR=1.0%,其性能令人满意。     

Ultrasound breast tumor image computer-aided diagnosis with texture and morphological features

RATIONALE AND OBJECTIVES: Computer-aided diagnosis (CAD) systems based on shape analysis have been proved to be highly accurate in evaluating breast tumors. However, it takes considerable time to train the classifier and diagnose breast tumors, because extracting morphologic features require a lot of computation. Hence, to develop a highly accurate and quick CAD system, we combined the texture and morphologic features of ultrasound breast tumor imaging to evaluate breast tumors in this study. MATERIALS AND METHODS: This study evaluated 210 ultrasound breast tumor images, including 120 benign tumors and 90 malignant tumors. The breast tumors were segmented automatically by the level set method. The autocovariance texture features and solidity morphologic feature were extracted, and a support vector machine was used to identify the tumor as benign or malignant. RESULTS: The accuracy of the proposed diagnostic system for classifying breast tumors was 92.86%, the sensitivity was 94.44%, the specificity was 91.67%, the positive predictive value was 89.47%, and the negative predictive value was 95.65%. In addition, the proposed system reduced the training time compared to systems based only on the morphologic analysis. CONCLUSIONS: The CAD system based on texture and morphologic analysis can differentiate benign from malignant breast tumors with high accuracy and short training time. It is therefore clinically useful to reduce the number of biopsies of benign lesions and offer a second reading to assist inexperienced physicians in avoiding misdiagnosis.     

一种新特征评价方法在红斑鳞状皮肤病诊断中的应用

针对改进F-score特征评价准则没有考虑特征测量量纲对特征重要性的影响,提出一种新的特征重要性评价准则D-score,避免不同特征测量量纲的影响,衡量样本特征在两类或多类之间的辨别能力。将D-score分别与前向顺序搜索、前向顺序浮动搜索两种搜索策略结合,以支持向量机的分类准确率评估所选特征子集的有效性,结合Filter和Wrapper特征选择方法的优势进行特征选择,得到两种混合特征选择方法。将该方法应用于红斑鳞状皮肤病诊断研究,并与基于改进F-score的混合特征选择方法进行了实验对比。十折交叉验证实验结果显示:在红斑鳞状皮肤病诊断研究中,D-score特征评价准则优于改进的F-score准则,基于D-score和前向顺序搜索策略的诊断准确率提高1.11%;D-score结合前向顺序浮动搜索策略的最低诊断准确率提高约3个百分点,平均诊断准确率提高约0.3个百分点,最高诊断准确率达到100%。前向顺序浮动搜索中,D-score准则选择的共有特征是改进F-score准则所选择共有特征的子集。所提出的D-score特征重要性评价准则是一种有效的特征区分能力度量准则,在红斑鳞状皮肤病的诊断中选择出了更有分类意义的特征,提高了诊断准确性。