多特征聚类与粘连分离模型的细胞抹片图像分割与分类

胰腺癌的诊断非常重要,而细胞抹片显微图像的病理分析是其诊断的主要手段。图像的准确自动分割和分类是病理分析的重要环节,因此本文提出了一种新的胰腺细胞抹片显微图像自动分割与分类算法。在分割方面,首先采用多特征Mean-shift聚类算法(MFMS)定位细胞核区域;接着采用弹性数学形态学结合角点检测的去粘连模型(CSM)对粘连重叠细胞核进行去粘连处理,实现了分割的准确性和鲁棒性。在分类方面,首先针对分割的细胞核提取了4个形状特征和138个不同颜色空间的纹理特征;然后结合支持向量机(SVM)和链式遗传算法(CAGA)实现封装式特征选择;最后将优选特征送入SVM进行分类,完成了胰腺细胞抹片显微图像的分类识别。本文采用了15幅图像一共461个细胞核进行测试。实验结果显示,本文算法可以实现不同类型的胰腺细胞抹片显微图像的自动分割与准确分类。就分割来说,本文算法可获得较高的正确率(93.46%±7.24%);就正常和癌变细胞的分类来说,本文算法可获得较高的分类正确率(96.55%±0.99%)、灵敏度(96.10%±3.08%)和特异度(96.80%±1.48%)。     

一种骨髓细胞识别分类算法的研究

为了准确有效地解决骨髓细胞的计算机识别分类问题,提出一种基于灰度阈值和彩色空间的骨髓细胞识别分类算法.该算法基于计算机图像处理技术,采用平滑、去噪等一系列的预处理得到平滑的骨髓细胞图像,通过对细胞图像的HSI颜色空间的分析,应用H通道和S通道的阈值分割方法分别将红细胞,白细胞的细胞核和细胞浆分割出来,并对有核细胞的胞核和胞浆提取形态特征和彩色光密度特征作为特征矢量,利用BP神经网络实现对骨髓细胞的分类识别.将该算法应用于临床采集到的150例骨髓细胞图像中,实验结果表明,该算法能较好地分类识别出各类骨髓细胞,具有较高的分类识别准确率.     

神经元干细胞序列图像中分裂子细胞的精确分割算法

针对神经元干细胞序列图像中存在的细胞分裂现象,为了实现对于分裂子细胞的特征提取、识别和跟踪,对于分裂出的微小目标--子细胞,提出了一种能够精确保留其形状特征的分割算法.采用了基于最大熵的模糊阅值分割方法,应用遗传算法确定最大模糊熵准则下的最优参数.利用加权距离变换、区域标注和形态学运算,实现了对分割结果中的干扰区域的吞没,以及对于分割图中欠分割目标的分离.仿真结果通过与硬分割比较,表明该算法在序列图像的追踪处理中对于需要特征提取的特定目标,能够实现最大限度保留该目标形状特征的精确分割.     

应用颜色迁移算法对褪色乳腺癌HE切片的保护性修复

目的探讨基于数字病理图像的颜色迁移算法对褪色乳腺癌HE切片的保护性修复。方法筛选2014—2016年青岛大学附属青岛市中心医院病理科褪色乳腺癌HE染色切片20张作为形状图像, 选用2022年乳腺癌HE染色石蜡切片5张作为颜色图像。数字化扫描后, 应用颜色迁移算法修复褪色图像的颜色。依据HE制片质量基本标准对恢复后图像进行颜色评价, 应用图像质量评估指标自然图像质量评价器(natural image quality evaluator, NIQE)、信息熵(entropy)、平均梯度(average gradient, AG)对恢复后图像进行质量评价。结果修复后图像符合HE制片质量基本标准, 评分较修复前升高(P<0.01), 差异有统计学意义;应用颜色迁移算法修复后, 图像的NIQE值下降(P<0.01), Entropy值上升(P<0.01), AG值上升(P<0.01), 差异有统计学意义。结论应用分区域颜色迁移算法对褪色乳腺癌HE染色切片进行颜色修复, 明显恢复了细胞核与细胞质染色对比, 有效保留了细胞核等重要结构的颜色, 提高图像的质量, 恢复其诊断及教...     

免疫组化彩色细胞图像自动分割的研究

目的：免疫组化彩色细胞图像中阳性细胞的自动分割提取有着重要的临床意义。本文结合三种分割算法的特点，研究实现免疫组化彩色细胞图像的自动分割，提取图像中的阳性细胞。方法：（1）采用OTSU方法在灰度的基础上对免疫组化彩色细胞进行预分割，去除无关背景。（2）使用K-聚类算法，对彩色细胞图像进行彩色分类筛选出阳性细胞和阴性细胞，并对所得阳性细胞图像进行腐蚀，以获取阳性细胞图像的种子。（3）使用区域生长算法对阳性细胞种子进行区域增长。获取完整的阳性细胞图。结果：准确分割出图像中的阳性细胞。结论：该自动分割方法可用于后续的阳性细胞自动计数，辅助医生诊断疾病。     

基于眼肌语义分割和特征提取的甲状腺相关眼病诊断算法研究

目的 甲状腺相关眼病(thyroid-associated ophthalmopathy, TAO)是常见的眼病之一，通过CT图像进行诊断和筛查对治疗有着重要意义，但传统方法依赖有经验的医生对CT进行分析和诊断，尚无有效的自动化方法。为此本文提出一种可以从CT图像中自动提取特征进行TAO诊断的方法，辅助医生进行诊断。方法 设计了Unet-Orbit分割网络对CT中的眼肌进行图像分割，随后采用影像组学工具(PyRadiomics)从分割结果中的眼肌区域提取数值化特征。为了更好地利用影像组学的特征，设计了一个特征提取网络，采用自动编码器框架。将不同的眼肌提取到的特征，通过特征合并和变换进一步得到一组新特征。最后采用来自上海交通大学医学院附属第九人民医院的1 912个CT图像数据集，对使用原始影像组学特征的分类器与使用特征提取网络后的特征的分类器进行了比较。结果 在医院数据集上，该模型的诊断准确率、灵敏度和特异性分别为87.34%、84.73%和89.96%。结论 语义分割网络可以高效分割眼肌区域，特征提取网络得到的新特征可以提升多种不同分类器在TAO诊断的准确率，可能为TAO的诊断提供一个...     

多信息融合的彩色细胞图像分割方法

本文就从病理切片的彩色图像中提取细胞核的有关方法作了研究,提出了有效的分割方法的策略。该方法以数学形态学为主要工具,可以利用细胞的多个特征,较好地解决了病理显微图像背景杂,光照不均及粘连团聚现象等因素给分割带来的困难。     

基于多参数磁共振影像组学特征的脑垂体瘤质地术前评估方法

为了探究多参数磁共振图像(MP-MRI)特征对脑垂体瘤质地评估的应用价值,提出一种基于影像组学的计算机辅助诊断方法,以期实现术前肿瘤质地的准确判定,从而为手术入路的选择提供影像学依据。对磁共振图像(T1加权、T1加权对比增强、T2加权)的肿瘤区域分别提取6种共296维纹理特征。采用特征选择方法识别重要的影像组学特征,并且使用支持向量机和随机森林两种常用的分类器对垂体瘤质软与质韧进行判别。在84例临床研究样本共计252张MRI图像上,用所述方法进行训练、十折交叉验证及测试。实验结果表明,与单一MRI图像特征相比较,所提出的MP-MRI特征组合能够获得更好的分类效果,分类准确率、敏感性、特异性、AUC分别达到89.80%、90.51%、89.88%、94.08%,表明MP-MRI影像组学特征能够有效准确地识别垂体瘤的软韧质地,有助于垂体瘤疗效和预后的改善。     

基于活动轮廓模型的彩色白细胞图像自动分割方法研究

为解决血液白细胞显微图像自动识别中的图像分割问题,提出了一种基于活动轮廓的彩色白细胞图像自动分割方法,首先在Hue,Saturation,Intensitv（HSI）彩色空间中运用聚类分割得到细胞核,从而得到细胞所在的位置,然后用流域算法得到细胞大致的轮廓,最后将此轮廓作为初始轮廓,用梯度矢量流（GVF）外力及来自全局信息的区域力驱动,结合彩色信息,使得轮廓收敛于真实的细胞边界。实验结果表明,此方法能精确、有效地分割出单个以及部分重叠白细胞区域。     

基于HSI修正空间信息融合的彩色白细胞图像分割方法

本文提出一种基于HSI修正空间信息融合的白细胞自动分割方法。首先将细胞原图转换至HSI彩色空间,由于H分量分段函数变换公式的不连续,导致原图中视觉均匀的细胞浆区域在此通道中均匀性变差。对色调计算公式进行了修改,然后依据白细胞核、浆在H、S、I通道分布特点提取核、浆、红细胞和背景区域信息,利用信息融合理论和方法构造融合图像Ⅰ和只存在细胞浆和少量干扰的融合图像Ⅱ,分别提取细胞核和细胞浆。最后标记细胞核、浆,得到分割结果。实验结果表明:该算法对白细胞图像分割准确性高、鲁棒性强且具有普适性。     

病变细胞显微图像分析与识别技术的研究

依据病变细胞的形态和颜色特征,我们提出了一种基于RGB和HIS彩色空间的自适应自动阈值分割算法,该算法能有效地将病变细胞的胞核从复杂的背景中提取出来.在分割图像的基础上,应用canny边缘检测算法提取出细胞边缘,采用八链码跟踪技术提取出细胞的特征值.为了同正常细胞比较,同时提取了正常细胞的特征值,并提出了二步识别算法以对正常和病变细胞进行识别.实验结果表明,该系统能有效地分割血细胞图像并且诊断率较高.     

An Automatic and Robust Decision Support System for Accurate Acute Leukemia Diagnosis from Blood Microscopic Images

This paper proposes an automatic and robust decision support system for accurate acute leukemia diagnosis from blood microscopic images. It is a challenging issue to segment leukocytes under uneven imaging conditions since features of microscopic leukocyte images change in different laboratories. Therefore, this paper introduces an automatic robust method to segment leukocyte from blood microscopic images. The proposed robust segmentation technique was designed based on the fact that if background and erythrocytes could be removed from the blood microscopic image, the remainder area will indicate leukocyte candidate regions. A new set of features based on hematologist visual criteria for the recognition of malignant leukocytes in blood samples comprising shape, color, and LBP-based texture features are extracted. Two new ensemble classifiers are proposed for healthy and malignant leukocytes classification which each of them is highly effective in different levels of analysis. Experimental results demonstrate that the proposed approach effectively segments leukocytes from various types of blood microscopic images. The proposed method performs better than other available methods in terms of robustness and accuracy. The final accuracy rate achieved by the proposed method is 98.10% in cell level. To the best of our knowledge, the image level test for acute lymphoblastic leukemia (ALL) recognition was performed on the proposed system for the first time that achieves the best accuracy rate of 89.81%.     

CSF cytology--the ongoing dilemma to distinguish neoplastic and inflammatory lymphocytes

To evaluate different morphological criteria for the distinction between inflammatory and neoplastic lymphocytes in the cerebrospinal fluid (CSF). Forty-two cytospin preparations of CSF from patients with confirmed CSF involvement by aggressive B-cell lymphoma or acute leukemia were compared with 26 samples of inflammatory diseases. CSF cytology was analyzed morphologically for preselected parameters of cell, cytoplasm and nucleic appearance, and the presence of mitoses or apoptoses. None of the evaluated parameters sharply discerns neoplastic and inflammatory changes. However, neoplastic cells were significantly larger. Moreover, irregular shape and pointed borders of the cytoplasm, and deep notches in the nucleus were significantly more frequent in neoplastic than in inflammatory lymphocytes. No single parameter is sufficient to detect neoplastic lymphocytes. Considering a combination of cell size and irregular shape of cell and nucleus, however, may improve the diagnostic accuracy of CSF dissemination by aggressive hematological malignancies.     

Computer-assisted lip diagnosis on traditional Chinese medicine using multi-class support vector machines

ABSTRACT: BACKGROUND: In Traditional Chinese Medicine (TCM), the lip diagnosis is an important diagnostic method which has a long history and is applied widely. The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body. However, the traditional diagnostic approach is mainly based on observation by doctor's nude eyes, which is non-quantitative and subjective. The non-quantitative approach largely depends on the doctor's experience and influences accurate the diagnosis and treatment in TCM. Developing new quantification methods to identify the exact syndrome based on the lip diagnosis of TCM becomes urgent and important. In this paper, we design a computer-assisted classification model to provide an automatic and quantitative approach for the diagnosis of TCM based on the lip images. METHODS: A computer-assisted classification method is designed and applied for syndrome diagnosis based on the lip images. Our purpose is to classify the lip images into four groups: deep-red, red, purple and pale. The proposed scheme consists of four steps including the lip image preprocessing, image feature extraction, feature selection and classification. The extracted 84 features contain the lip color space component, texture and moment features. Feature subset selection is performed by using SVM-RFE (Support Vector Machine with recursive feature elimination), mRMR (minimum Redundancy Maximum Relevance) and IG (information gain). Classification model is constructed based on the collected lip image features using multi-class SVM and Weighted multi-class SVM (WSVM). In addition, we compare SVM with k-nearest neighbor (kNN) algorithm, Multiple Asymmetric Partial Least Squares Classifier (MAPLSC) and Naive Bayes for the diagnosis performance comparison. All displayed faces image have obtained consent from the participants. RESULTS: A total of 257 lip images are collected for the modeling of lip diagnosis in TCM. The feature selection method SVM-RFE selects 9 important features which are composed of 5 color component features, 3 texture features and 1 moment feature. SVM, MAPLSC, Naive Bayes, kNN showed better classification results based on the 9 selected features than the results obtained from all the 84 features. The total classification accuracy of the five methods is 84%, 81%, 79% and 81%, 77%, respectively. So SVM achieves the best classification accuracy. The classification accuracy of SVM is 81%, 71%, 89% and 86% on Deep-red, Pale Purple, Red and lip image models, respectively. While with the feature selection algorithm mRMR and IG, the total classification accuracy of WSVM achieves the best classification accuracy. Therefore, the results show that the system can achieve best classification accuracy combined with SVM classifiers and SVM-REF feature selection algorithm. CONCLUSIONS: A diagnostic system is proposed, which firstly segments the lip from the original facial image based on the Chan-Vese level set model and Otsu method, then extracts three kinds of features (color space features, Haralick co-occurrence features and Zernike moment features) on the lip image. Meanwhile, SVM-REF is adopted to select the optimal features. Finally, SVM is applied to classify the four classes. Besides, we also compare different feature selection algorithms and classifiers to verify our system. So the developed automatic and quantitative diagnosis system of TCM is effective to distinguish four lip image classes: Deep-red, Purple, Red and Pale. This study puts forward a new method and idea for the quantitative examination on lip diagnosis of TCM, as well as provides a template for objective diagnosis in TCM.     

Boundary modelling and shape analysis methods for classification of mammographic masses

The problem of computer-aided classification of benign and malignant breast masses using shape features is addressed. The aim of the study is to look at the exceptions in shapes of masses such as circumscribed malignant tumours and spiculated benign masses which are difficult to classify correctly using common shape analysis methods. The proposed methods of shape analysis treat the object's boundary in terms of local details. The boundaries of masses analysed using the proposed methods were manually drawn on mammographic images by an expert radiologist (JELD). A boundary segmentation method is used to separate major portions of the boundary and to label them as concave or convex segments. To analyse the shape information localised in each segment, features are computed through an iterative procedure for polygonal modelling of the mass boundaries. Features are based on the concavity fraction of a mass boundary and the degree of narrowness of spicules as characterised by a spiculation index. Two features comprising spiculation index (SI) and fractional concavity (f_cc) developed in the present study when used in combination with the global shape feature of compactness resulted in a benign/malignant classification accuracy of 82%, with an area (A_z) of 0.79 under the receiver operating characteristics (ROC) curve with a database of the boundaries of 28 benign masses and 26 malignant tumours. SI alone resulted in a classification accuracy of 80% with A_z of 0.82. The combination of all the three features achieved 91% accuracy of circumscribed versus spiculated classification of masses based on shape.     

Microtubule bundles in fish cerebellar Purkinje cells

Summary The initial axon segments and the cell bodies of Purkinje cells were examined in electron microscopic serial sections and toluidine blue semithin sections of goldfish cerebellum. We observed two characteristic cytoplasmic features different from those of other vertebrate neurons. 1. The areas of Nissl substance and Golgi apparatus are sharply divided in the periphery and center of the cytoplasm. 2. Microtubules fasciculated by cross-bridges in the axon hillock and initial axon segment remain bundled in the perikaryon, pass near the eccentric nucleus, and enter into the Golgi area of the central cytoplasm, where they are surrounded by mitochondria. We suggest that the intracellular fasciculated microtubules may establish a prepared pathway for fast anterograde and retrograde transport to and from the Golgi area of the cell body.     

Evolutionary weighting of image features for diagnosing of CNS tumors

This paper concerns an application of evolutionary feature weighting for diagnosis support in neuropathology. The original data in the classification task are the microscopic images of ten classes of central nervous system (CNS) neuroepithelial tumors. These images are segmented and described by the features characterizing regions resulting from the segmentation process. The final features are in part irrelevant. Thus, we employ an evolutionary algorithm to reduce the number of irrelevant attributes, using the predictive accuracy of a classifier ('wrapper' approach) as an individual's fitness measure. The novelty of our approach consists in the application of evolutionary algorithm for feature weighting, not only for feature selection. The weights obtained give quantitative information about the relative importance of the features. The results of computational experiments show a significant improvement of predictive accuracy of the evolutionarily found feature sets with respect to the original feature set.     

小鼠胚着床前线粒体的分布和超微结构变化

为了解小鼠着档前细胞中线粒体的分布和超微结构的变化规律，观察了２细胞胚，４细胞胚，８细胞胚，桑椹胚，早期囊胚和晚期囊胚，２细胞期和桑椹胚期，线粒体绕胞核集，在挤紧的８细胞胚中，线粒体在细胞接触面处的胞质边缘密集。囊胚期，滋养层细胞的线粒体在胞核周围较宽的区域中分布。     

SVM-Based CAC System for B-Mode Kidney Ultrasound Images

The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee’s sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases.     

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

The purpose of this paper is to evaluate the effect of the combination of magnetic resonance spectroscopic imaging (MRSI) data and magnetic resonance imaging (MRI) data on the classification result of four brain tumor classes. Suppressed and unsuppressed short echo time MRSI and MRI were performed on 24 patients with a brain tumor and four volunteers. Four different feature reduction procedures were applied to the MRSI data: simple quantitation, principal component analysis, independent component analysis and LCModel. Water intensities were calculated from the unsuppressed MRSI data. Features were extracted from the MR images which were acquired with four different contrasts to comply with the spatial resolution of the MRSI. Evaluation was performed by investigating different combinations of the MRSI features, the MRI features and the water intensities. For each data set, the isolation in feature space of the tumor classes, healthy brain tissue and cerebrospinal fluid was calculated and visualized. A test set was used to calculate classification results for each data set. Finally, the effect of the selected feature reduction procedures on the MRSI data was investigated to ascertain whether it was more important than the addition of MRI information. Conclusions are that the combination of features from MRSI data and MRI data improves the classification result considerably when compared with features obtained from MRSI data alone. This effect is larger than the effect of specific feature reduction procedures on the MRSI data. The addition of water intensities to the data set also increases the classification result, although not significantly. We show that the combination of data from different MR investigations can be very important for brain tumor classification, particularly if a large number of tumors are to be classified simultaneously.