Endoscopic Image Classification and Retrieval using Clustered Convolutional Features

With the growing use of minimally invasive surgical procedures, endoscopic video archives are growing at a rapid pace. Efficient access to relevant content in such huge multimedia archives require compact and discriminative visual features for indexing and matching. In this paper, we present an effective method to represent images using salient convolutional features. Convolutional kernels from the first layer of a pre-trained convolutional neural network (CNN) are analyzed and clustered into multiple distinct groups, based on their sensitivity to colors and textures. Dominant features detected by each cluster are collected into a single, layout-preserving feature map using a spatial maximal activator pooling (SMAP) approach. A moving window based structured pooling method then captures spatial layout features and global shape information from the aggregated feature map to populate feature histograms. Finally, individual histograms for each cluster are combined into a single comprehensive feature histogram. Clustering convolutional feature space allow extraction of color and texture features of varying strengths. Further, the SMAP approach enable us to select dominant discriminative features. The proposed features are compact and capable of conveniently outperforming several existing features extraction approaches in retrieval and classification tasks on endoscopy images dataset.     

一种自动提取彩色舌图像的算法

彩色舌体的自动提取技术为中医舌诊客观化提供了更加便捷的操作手段.传统舌图像的提取方法不能较精确地提取所需的舌体部分,且对于舌体细节(如舌体伪轮廓和点刺)的处理也不够理想.采用首先提取彩色舌图像在RGB空间的亮度特征信息,然后使用平滑、图像增强的方法对舌图像进行处理,再使用最大类间方差法进行自动分割,以提取出彩色舌体的初始轮廓.通过提取舌图像的最大连通区域以去除其他非舌体区域,进行负向处理后再次提取最大连通区域以去掉舌体内的孔洞,最终通过数学形态学及逻辑与运算提取出所需的舌体部分.实验证明,本研究具有一定的分割效果,满足后续舌体内部感兴趣区域再提取和分析的需要.     

Development of a Robust Algorithm for Detection of Nuclei and Classification of White Blood Cells in Peripheral Blood Smear Images

Peripheral Blood Smear analysis plays a vital role in diagnosis of many diseases such as leukemia, anemia, malaria, lymphoma and infections. Unusual variations in color, shape and size of blood cells indicate abnormal condition. We used a total of 117 images from Leishman stained peripheral blood smears acquired at a magnification of 100X. In this paper we present a robust image processing algorithm for detection of nuclei and classification of white blood cells based on features of the nuclei. We used novel image enhancement method to manage illumination variations and TissueQuant method to manage color variations for the detection of nuclei. Dice similarity coefficient of 0.95 was obtained for nucleus detection. We also compared the proposed method with a state-of-the-art method and the proposed method was found to be better. Shape and texture features of the detected nuclei were used for classifying white blood cells. We considered classification of WBCs using two approaches such as 5-class and cell-by-cell approaches using neural network and hybrid-classifier respectively. We compared the results of both the approaches for classification of white blood cells. Cell-by-cell approach offered 1.4% higher sensitivity in comparison with the 5-class approach. We obtained an accuracy of 100% for lymphocyte and basophil detection. Hence, we conclude that lymphocytes and basophils can be accurately detected even when the analysis is limited to the features of nuclei whereas, accurate detection of other types of WBCs will require analysis of the cytoplasm too.     

Comparison of Several Texture Features for Tumor Detection in CE Images

Capsule endoscopy (CE) has been widely used as a new technology to diagnose gastrointestinal tract diseases, especially for small intestine. However, the large number of images in each test is a great burden for physicians. As such, computer aided detection (CAD) scheme is needed to relieve the workload of clinicians. In this paper, automatic differentiation of tumor CE image and normal CE image is investigated through comparative textural feature analysis. Four different color textures are studied in this work, i.e., texture spectrum histogram, color wavelet covariance, rotation invariant uniform local binary pattern and curvelet based local binary pattern. With support vector machine being the classifier, the discrimination ability of these four different color textures for tumor detection in CE images is extensively compared in RGB, Lab and HSI color space through ten-fold cross-validation experiments on our CE image data. It is found that HSI color space is the most suitable color space for all these texture based CAD systems. Moreover, the best performance achieved is 83.50% in terms of average accuracy, which is obtained by the scheme based on rotation invariant uniform local binary pattern.     

Diabetic Retinopathy: A Quadtree Based Blood Vessel Detection Algorithm Using RGB Components in Fundus Images

Blood vessel detection in retinal images is a fundamental step for feature extraction and interpretation of image content. This paper proposes a novel computational paradigm for detection of blood vessels in fundus images based on RGB components and quadtree decomposition. The proposed algorithm employs median filtering, quadtree decomposition, post filtration of detected edges, and morphological reconstruction on retinal images. The application of preprocessing algorithm helps in enhancing the image to make it better fit for the subsequent analysis and it is a vital phase before decomposing the image. Quadtree decomposition provides information on the different types of blocks and intensities of the pixels within the blocks. The post filtration and morphological reconstruction assist in filling the edges of the blood vessels and removing the false alarms and unwanted objects from the background, while restoring the original shape of the connected vessels. The proposed method which makes use of the three color components (RGB) is tested on various images of publicly available database. The results are compared with those obtained by other known methods as well as with the results obtained by using the proposed method with the green color component only. It is shown that the proposed method can yield true positive fraction values as high as 0.77, which are comparable to or somewhat higher than the results obtained by other known methods. It is also shown that the effect of noise can be reduced if the proposed method is implemented using only the green color component.     

基于机器学习的椎弓根螺钉钉道超声图像评估方法

目的 探讨基于支持向量机（SVM）构建的人工智能辅助诊断模型对椎弓根螺钉钉道完整性进行超声鉴别与验证的方法研究。 方法 本文提出了一种基于超声图像智能分析的椎弓根钉道完整性评估方法。数据采自4个新鲜人体胸腰椎标本。预建立钉道50个，共800张超声图像（其中钉道完整与破损的样本各400个），采用五折交叉验证的方法对样本进行训练集与测试集的划分，对人工智能辅助诊断模型进行训练及测试。首先对超声图像进行裁剪，并采用亮度映射方法进行图像增强得到易于计算机判断识别且排除无效信息干扰的超声图像；然后通过灰度共生矩阵算法进行纹理特征提取，并采用支持向量机模型对正常和严重破损样本的初始分类模型进行搭建；其次，使用灰度分布得到用于区分前景和背景的阈值，并通过设计的损失函数得到得到钉道同心圆的半径；最后将同心圆外部图像的熵、方差、对比度、能量、平均绝对偏差作为第二类特征，最后进行轻微破损样本和未破损样本的二次分类模型搭建。 结果 初始分类的准确率为74.75%，特异性为71.81%，灵敏度为81.5%，F1值为76.35%，假正率为32%，假负率为18.5%。二次分类的准确率为93.75%，特异性为91.55%，灵敏度为97.5%，F1值为94.43%，假正率为9%，假负率为2.5%。二次类准确率与初始分类相比较，准确率提升19%。 结论 本文提出的基于SVM机器学习模型的方法可较为准确地检测椎弓根钉道的破损情况，且准确率较高，可用于术中实时判断椎弓根钉道的状态。     

Performance Evaluation of Color Models in the Fusion of Functional and Anatomical Images

Fusion of the functional image with an anatomical image provides additional diagnostic information. It is widely used in diagnosis, treatment planning, and follow-up of oncology. Functional image is a low-resolution pseudo color image representing the uptake of radioactive tracer that gives the important metabolic information. Whereas, anatomical image is a high-resolution gray scale image that gives structural details. Fused image should consist of all the anatomical details without any changes in the functional content. This is achieved through fusion in de-correlated color model and the choice of color model has greater impact on the fusion outcome. In the present work, suitability of different color models for functional and anatomical image fusion is studied. After converting the functional image into de-correlated color model, the achromatic component of functional image is fused with an anatomical image by using proposed nonsubsampled shearlet transform (NSST) based image fusion algorithm to get new achromatic component with all the anatomical details. This new achromatic and original chromatic channels of functional image are converted to RGB format to get fused functional and anatomical image. Fusion is performed in different color models. Different cases of SPECT-MRI images are used for this color model study. Based on visual and quantitative analysis of fused images, the best color model for the stated purpose is determined.     

不同标准照明体光源对正常舌尖舌色色度学参数的影响

目的 比较标准照明体A、B、C、E和D65光源对正常舌尖舌色色度学参数的影响,为中医舌诊中舌色测量的标准化研究提供实验基础.方法 选取在校本科生正常人群35例作为研究对象,将光源修正为等能白光(标准照明体E光源),应用可见反射光谱法采集舌尖舌色数据,获得380～780 nm的光谱曲线,根据标准照明体A、B、C和D65光谱分布特征计算在各照明体条件下的可见反射光谱曲线;进行CIE XYZ颜色匹配三刺激值的计算,获得各标准照明体条件下正常舌尖舌色的CIE 1964色品坐标、主波长值与RGB分值,并进行比较分析.结果 在标准照明体A、B、C、E和D65光源照明下,舌尖舌色的可见反射光谱曲线、CIE 1964色品坐标、RGB分值均存在显著性的差异,但以各标准照明体特性为参照的主波长值存在一致性.结论 应用可见反射光谱法对色诊资料采集可提供一种标准的测色方法.不同的标准照明体光源条件可使色诊资料采集出现偏差,采用统一的标准照明体光源进行色诊研究可以减少这方面的误差,同时对色诊的数码成像资料进行色彩校正时,要以照明体的特性为基本参照进行数据分析.     

寄生虫虫卵图像计算机自动识别技术研究

目的探讨建立寄生虫虫卵显微图像计算机自动识别系统的相关问题。方法从研究国内外虫卵图像识别技术的相关进展和10种人体常见寄生虫虫卵图像的特性入手,以Visual C++6.0为系统开发工具,运用数字图像处理,将虫卵数字图像经过预处理,提取形态、颜色和纹理等特征,通过相应的虫卵分类识别算法的实现,初步实现人体常见寄生虫虫卵图像的计算机自动识别。结果实验系统可在获取图像后一步实现对10种常见人体寄生虫虫卵的自动识别,平均识别率为93.0%,每幅图像处理时间为1~3s。结论对虫卵图像纹理特征的选用,可显著提高系统对样本的分类精度;系统开发所采用的分析方法、特征和计算参数的选取原则,有益于日后的类似研究。     

一种基于离散Tchebichef正交多项式和傅里叶梅林矩的局部多特征图像检索算法

提出了一种基于离散Tchebichef正交多项式和傅里叶梅林矩的局部多特征图像检索算法。通过对图像进行正交变换和多分辨率重排序,在变换域中提取出纹理、颜色和形状特征,生成具有较强区分能力的图像特征。由于傅里叶梅林矩具有旋转不变性,因此在处理发生旋转变换和平移变换的图像时,检索效果较好。最后,对提出的算法用多个数据集进行了检索实验,并对实验结果进行了比较和分析。     

中药饮片切面纹理特征提取研究

目的:研究中药饮片切面纹理特征提取方法。方法:利用基于Tamura方法研究中药饮片切面纹理粗糙度、对比度、方向度、线性度、规则度、粗略度等特征参数提取。结果:由于Tamura方法更加符合人眼视觉习惯与心理感知,对比试验表明多种参数结合的Tamura方法能够较好的描述中药饮片切面纹理特点。结论:该方法可以局部反应纹理局部细节,从而实现了将视觉感知用科学、定量的形式表示,为基于机器视觉的中药饮片自动辨识及分级等应用提供理论依据,由于受到饮片颜色、饮片背景、饮片形状等因素的影响,基于Tamura纹理方法提取中药饮片切面纹理特征参数,需要多种特征参数融合才能够较为精确的表示纹理特征。     

计算机辅助RGB颜色测量分析系统客观评价鲜红斑痣的疗效研究

为探讨计算机辅助RGB测量分析系统对鲜红斑痣疗效的评价效果，选择临床确诊为鲜红斑痣的20例病人，采用计算机辅助测量分析系统和具有丰富临床经验的整形外科医生目测两种方法，分别对疗效进行评价，并对评价结果进行对比研究。结果显示，RGB测量分析系统评价效果为51．6％，整形外科医生定性评价治疗鲜红斑痣的效果为47．13％，但临床医生最大差异达到60％且主观易变。RGB测量分析系统能够定量得出治疗效果，并准确测量变化面积和颜色变化的百分比。整形外科医生与计算机辅助测量系统的疗效评价具有线性相关（r=0．879）。由此。认为RGB颜色测量分析系统可替代临床医生评价，且更具客观性，评价内容更精确全面。     

基于CLSTM的步态分类方法

步态分类在人体运动能量消耗评估等应用中具有重要意义,提高分类精度和降低对统计特征的依赖是步态分类的研究热点。采用传统的步态分类方法提取的步态特征用于细分化步态时不能得到较好的效果。考虑到步态的连续性和不同轴之间信号的相关性,本文提出了基于CLSTM的步态分类方法：采用卷积神经网络（CNN）操作,通过计算多轴步态数据提取步态特征;基于长短期记忆（LSTM）构建步态时间序列模型,学习步态特征图时间维度上的长期依赖性。基于USC-HAD数据集的实验结果表明,用此方法提取了步态序列特征,很好地利用了步态时间序列特点,提升了11种步态的分类精度。     

Automated Bleeding Detection in Capsule Endoscopy Videos Using Statistical Features and Region Growing

Wireless Capsule Endoscopy (WCE) is a technology in the field of endoscopic imaging which facilitates direct visualization of the entire small intestine. Many algorithms are being developed to automatically identify clinically important frames in WCE videos. This paper presents a supervised method for automated detection of bleeding regions present in WCE frames or images. The proposed method characterizes the image regions by using statistical features derived from the first order histogram probability of the three planes of RGB color space. Despite being inconsistent and tiresome, manual selection of regions has been a popular technique for creating training data in the studies of capsule endoscopic images. We propose a semi-automatic region-annotation algorithm for creating training data efficiently. All possible combinations of different features are exhaustively analyzed to find the optimum feature set with the best performance. During operation, regions from images are obtained by applying a segmentation method. Finally, a trained neural network recognizes the patterns of the data arising from bleeding and non-bleeding regions.     

Three-dimensional Texture Analysis of Renal Cell Carcinoma Cell Nuclei for Computerized Automatic Grading

The extraction of important features in cancer cell image analysis is a key process in grading renal cell carcinoma. In this study, we analyzed the three-dimensional chromatin texture of cell nuclei based on digital image cytometry. Individual images of 2,423 cell nuclei were extracted from 80 renal cell carcinomas (RCCs) using confocal laser scanning microscopy (CLSM). First, we applied the 3D texture mapping method to render the volume of entire tissue sections. Then, we determined the chromatin texture quantitatively by calculating 3D gray level co-occurrence matrices and 3D run length matrices. Finally, to demonstrate the suitability of 3D texture features for classification, we performed a discriminant analysis. In addition, we conducted a principal component analysis to obtain optimized texture features. Automatic grading of cell nuclei using 3D texture features had an accuracy of 78.30%. Combining 3D textural and 3D morphological features improved the accuracy to 82.19%.     

Computer Aided Diagnosis System for Breast Cancer Based on Color Doppler Flow Imaging

Color Doppler flow imaging takes a great value in diagnosing and classifying benign and malignant breast lesions. However, scanning of color Doppler sonography is operator-dependent and ineffective. In this paper, a novel breast classification system based on B-Mode ultrasound and color Doppler flow imaging is proposed. First, different feature extraction methods were used to obtain the texture and geometric features from B-Mode ultrasound images. In color Doppler feature extraction stage, several spectrum features are extracted by applying blood flow velocity analysis to Doppler signals. Moreover, a velocity coherent vector method is proposed based on color coherence vector, which is helpful for designing to the optimize detection of flow indices from different blood flow velocity fields automatically. Finally, a support vector machine classifier with selected feature vectors is used to classify breast tumors into benign and malignant. The experimental results demonstrate that the proposed computer-aided diagnosis system is useful for reducing the unnecessary biopsy and death rate.     

Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound

Quantitative characterization of carotid atherosclerosis and classification into symptomatic or asymptomatic type is crucial in both diagnosis and treatment planning. This paper describes a computer-aided diagnosis (CAD) system which analyzes ultrasound images and classifies them into symptomatic and asymptomatic based on the textural features. The proposed CAD system consists of three modules. The first module is preprocessing, which conditions the images for the subsequent feature extraction. The feature extraction stage uses image texture analysis to calculate Standard deviation, Entropy, Symmetry, and Run Percentage. Finally, classification is performed using AdaBoost and Support Vector Machine for automated decision making. For Adaboost, we compared the performance of five distinct configurations (Least Squares, Maximum- Likelihood, Normal Density Discriminant Function, Pocket, and Stumps) of this algorithm. For Support Vector Machine, we compared the performance using five different configurations (linear kernel, polynomial kernel configurations of different orders and radial basis function kernels). SVM with radial basis function kernel for support vector machine presented the best classification result: classification accuracy of 82.4%, sensitivity of 82.9%, and specificity of 82.1%. We feel that texture features coupled with the Support Vector Machine classifier can be used to identify the plaque tissue type. An Integrated Index, called symptomatic asymptomatic carotid index (SACI), is proposed using texture features to discriminate symptomatic and asymptomatic carotid ultrasound images using just one index or number. We hope this SACI can be used as an adjunct tool by the vascular surgeons for daily screening.     

基于颜色空间变换的数字化人体切片图像背景移除算法

目的对数字化人体图片进行预处理,完整提取其中的人体数据。方法应用首例中国数字化可视人体数据集,提出一种基于颜色空间变换并引入标记矩阵进行移除背景的算法,并以头部切片为例进行详细阐述。结果将原始数据从RGB颜色空间变换到YIQ颜色空间后,对I灰度图取阈值为f=0进行标记,再结合标记矩阵对原始图像进行背景移除的操作,能够完好地提取出所需要的人体数据。结论本文所提出的算法简单易行而且运算效率高,对目标区域的数据保存较好,为图片的下一步处理奠定了基础。