基于PET医学图像边缘检测算法的研究

目的:边缘检测在图像处理中至关重要,可被广泛应用于目标区域识别、区域形状检测、图像分割等图像分析领域。边缘是图像中不平稳现象和不规则结构的重要表现,往往携带着图像中的大量信息,并给出图像轮廓。在医学图像三维显示技术中,为了更精确的临床判别需要得到单像素的清晰轮廓,因此我们提出一种新的边缘检测算法。方法:在传统的小波边缘检测的基础上,提出了一种新的边缘算法,即基于小波极大值边缘检测算法,应用模糊算法构造相应的隶属函数,再对得到的极大值进一步筛选。结果:将该算法应用到医学图像中,最终可以得到较清楚的单像素边缘轮廓,实验结果证明了该算法的可行性。结论:运用这种算法处理过的医学图像边缘锐化更好,更清晰,能够为肿瘤的早期识别提供依据,满足医学影像识别的需要。     

基于多尺度植物种子图像边缘检测算法的设计与实现

小波变换,由于其具有时频局部化的特性及多尺度特性,能敏感地反映突变信号,是一种理想的边缘提取方法．本文系统地介绍了作者在图像边缘检测方面所做的理论探讨、算法及应用研究工作．目前的边缘提取方法有多种,本文将重点集中于基于小波变换的图像边缘检测方法的理论推导和算法实现．     

应用小波变换检测细胞显微荧光图像中的囊泡

文章提出了一种用小波变换来检测生物荧光图像中囊泡的方法。作者用à trous小波对图像进行小波变换,然后求出每层系数的中值绝对偏差σ,并用t=kσ/0.67作为阈值对每层系数进行门限滤波,然后通过提取小波变换系数来重构图像。通过设计实验与常用的“rolling ball”算法对比,发现小波变换算法在低信噪比的情况下,具有更好的灵敏度;对于形状大小不同的信号,具有更好的稳定性;而且对于信号的细节信息具有更好的保真性。     

医学图像中的边缘分析

根据医学图像处理的要求,需要将图像划分若干区域,其划分过程要求迅速、精确。本文结合实际经验,介绍了图像分割的重要方法——边缘提取,并着重分析了其中边缘检测和边缘跟踪的过程和方法,同时还给出了用计算机模拟得到的边缘提取的结果。     

用菌紫质薄膜传感器进行图像边缘检测的研究

应用菌紫质人工沉淀膜所具有的与动物视网膜类似的微分光电响应特性,以该人工膜为传感器构建了一个可检测图像边缘的原理系统,并成功地检测了到简单图的边缘。     

基于小波分析的医学图像的处理

医学图像的好坏直接影响着医生对病情的诊断和治疗,因此利用数字图像处理等技术对医学图像进行有效的处理,已成为医学图像处理研究和开发的一大热点.小波分析是对傅立叶变换的继承和发展,在医学影像领域有着广阔的应用前景.介绍了二维离散小波变换的一般形式,在图像分解的基础上,利用小波分析对医学图像进行去噪和增强处理,能够有效的改善图像质量,有利于医生对病情的诊断和治疗.     

用菌紫质薄膜传感器进行图像边缘检测的研究

应用菌紫质人工沉淀膜所具有的与动物视网膜类似的微分光电响应特性,以该人工膜为传感器构建了一个可检测图像边缘的原理系统,并成功地检测到了简单图像的边缘。本文的结果除进一步说明了菌紫质分子在图像技术中的应用前途外,还说明了这种分子在视觉功能模拟和人工视觉等方面的良好应用前景 。     

基于独立元分析和联合小波熵检测多导联ECG信号的QRS

QRS波群的准确定位是ECG信号自动分析的基础。为提高QRS检测率,提出一种基于独立元分析（ICA）和联合小波熵（CWS）检测多导联ECG信号QRS的算法。ICA算法从滤波后的多导联ECG信号中分离出对应心室活动的独立元;然后对各独立元进行连续小波变换（CWT）,重构小波系数的相空间,结合相空间中的QRS信息对独立元排序;最后检测排序后独立元的CWS得到QRS信息。实验对St.Petersburg12导联心率失常数据库及64导联犬心外膜数据库测试,比较本文算法与单导联QRS检测算法和双导联QRS检测算法的性能。结果表明,该文算法的性能最好,检测准确率分别为99.98%和100%。     

基于空间小波变换的生态地理界线识别与定位

为了提高生态地理分界线识别和定位的客观性,探讨了通过空间小波变换获取多尺度模极大值定位过渡带的方法.以NDVI和降水作为小波多尺度分解的对象,应用db3小波核函数分别对49条样带的模极大值进行了多尺度检测,并在GIS中确定其地理坐标.研究结果表明:识别半干旱半湿润生态地理分界线的最佳空间尺度为20～40 km,小于这一尺度定位过程容易受到局部地表覆被因素如城市区域或地形的影响,大于这一尺度由于要素被过度平滑,造成定位不准;从定位点的聚集度分析,NDVI的定位效果好于降水,特别是在较大空间尺度上.而与综合自然地理区划方案中的半干旱半湿润分界线比较,从定位点的方向性、平均最短距离以及均衡度三项指标综合判断,小波变换对于降水过渡带的定位优于对NDVI的定位.研究证实,空间小变换与GIS结合是提高生态地理分界线识别与定位科学性的重要途径,是对专家系统划分界线方法的有力补充和完善.     

基于小波变换的QRS综合波的检测

本文描述了基于二进制小波变换（DyWT),ECG信号中QRS综合波的检测。设计－小波它适合于QRS检测,将基于心电信号的特殊的特征的特征为小波的尺度。DyWT较之其它方法最基本的优点为强有力的抑制噪声检测以及在分析随时间变化ECG波形时的灵活性。     

High-Throughput Method for Automated Colony and Cell Counting by Digital Image Analysis Based on Edge Detection

Counting cells and colonies is an integral part of high-throughput screens and quantitative cellular assays. Due to its subjective and time-intensive nature, manual counting has hindered the adoption of cellular assays such as tumor spheroid formation in high-throughput screens. The objective of this study was to develop an automated method for quick and reliable counting of cells and colonies from digital images. For this purpose, I developed an ImageJ macro Cell Colony Edge and a CellProfiler Pipeline Cell Colony Counting, and compared them to other open-source digital methods and manual counts. The ImageJ macro Cell Colony Edge is valuable in counting cells and colonies, and measuring their area, volume, morphology, and intensity. In this study, I demonstrate that Cell Colony Edge is superior to other open-source methods, in speed, accuracy and applicability to diverse cellular assays. It can fulfill the need to automate colony/cell counting in high-throughput screens, colony forming assays, and cellular assays.     

Neutrosophic Based Nakagami Total Variation Method for Speckle Suppression in Thyroid Ultrasound Images

Background

Neutrosophic based methods are becoming very popular in denoising of images due to the capability of handling indeterminacy. The main goal of denoising is to maintain balance between edge preservation and speckle reduction.

SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement

Accurate identification of cell types from single-cell RNA sequencing(scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies. This task corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells affects the result significantly. Although many approaches for cell type identification have been proposed,the accuracy still needs to be improved. In this study, we proposed a novel single-cell clustering framework based on similarity learning, called SSRE. SSRE models the relationships between cells based on subspace assumption, and generates a sparse representation of the cell-to-cell similarity.The sparse representation retains the most similar neighbors for each cell. Besides, three classical pairwise similarities are incorporated with a gene selection and enhancement strategy to further improve the effectiveness of SSRE. Tested on ten real scRNA-seq datasets and five simulated datasets, SSRE achieved the superior performance in most cases compared to several state-of-the-art single-cell clustering methods. In addition, SSRE can be extended to visualization of scRNA-seq data and identification of differentially expressed genes. The matlab and python implementations of SSRE are available at https://github.com/CSUBioGroup/SSRE.     

A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images

ObjectiveIn this research paper, the brain MRI images are going to classify by considering the excellence of CNN on a public dataset to classify Benign and Malignant tumors.Materials and MethodsDeep learning (DL) methods due to good performance in the last few years have become more popular for Image classification. Convolution Neural Network (CNN), with several methods, can extract features without using handcrafted models, and eventually, show better accuracy of classification. The proposed hybrid model combined CNN and support vector machine (SVM) in terms of classification and with threshold-based segmentation in terms of detection.ResultThe findings of previous studies are based on different models with their accuracy as Rough Extreme Learning Machine (RELM)-94.233%, Deep CNN (DCNN)-95%, Deep Neural Network (DNN) and Discrete Wavelet Autoencoder (DWA)-96%, k-nearest neighbors (kNN)-96.6%, CNN-97.5%. The overall accuracy of the hybrid CNN-SVM is obtained as 98.4959%.ConclusionIn today's world, brain cancer is one of the most dangerous diseases with the highest death rate, detection and classification of brain tumors due to abnormal growth of cells, shapes, orientation, and the location is a challengeable task in medical imaging. Magnetic resonance imaging (MRI) is a typical method of medical imaging for brain tumor analysis. Conventional machine learning (ML) techniques categorize brain cancer based on some handicraft property with the radiologist specialist choice. That can lead to failure in the execution and also decrease the effectiveness of an Algorithm. With a brief look came to know that the proposed hybrid model provides more effective and improvement techniques for classification.     

A Unified Mechanism for Aminopeptidase N-based Tumor Cell Motility and Tumor-homing Therapy

Tumor cell surface aminopeptidase N (APN or CD13) has two puzzling functions unrelated to its enzymatic activity: mediating tumor cell motility and serving as a receptor for tumor-homing peptides (peptides that bring anti-cancer drugs to tumor cells). To investigate APN-based tumor-homing therapy, we determined the crystal structure of APN complexed with a tumor-homing peptide containing a representative Asn-Gly-Arg (NGR) motif. The tumor-homing peptide binds to the APN enzymatic active site, but it resists APN degradation due to a distorted scissile peptide bond. To explore APN-based tumor cell motility, we examined the interactions between APN and extracellular matrix (ECM) proteins. APN binds to, but does not degrade, NGR motifs in ECM proteins that share similar conformations with the NGR motif in the APN-bound tumor-homing peptide. Therefore, APN-based tumor cell motility and tumor-homing therapy rely on a unified mechanism in which both functions are driven by the specific and stable interactions between APN and the NGR motifs in ECM proteins and tumor-homing peptides. This study further implicates APN as an integrin-like molecule that functions broadly in cell motility and adhesion by interacting with its signature NGR motifs in the extracellular environment.     

肝细胞癌循环肿瘤细胞标志物及检测方法研究进展

肝细胞癌(hepatocellular carcinoma, HCC)在中国是一种高发病率和高死亡率的恶性肿瘤。肿瘤切除、肝移植是治疗该病最有效的手段,但术后的高复发率和高转移率是影响患者预后的重要因素。外周血循环肿瘤细胞(circulating tumor cells, CTCs)是导致肝细胞癌术后复发和转移的必要因子。综述了CTCs的标记物——磷脂酰肌醇蛋白聚糖-3、转铁蛋白受体、甲胎蛋白、α-L岩藻糖苷酶、上皮细胞粘附因子、高尔基蛋白73和异常凝血酶原等,以及利用这些标记物检测CTCs的特异性和灵敏度,以期为肝细胞癌转移的早期检测、术后的复发、预后评估和选择治疗方案等提供依据。     

稀土化合物氯化亚鈰对人肺癌细胞PG、人胃癌细胞BGC-823的作用

 以人肺癌细胞 Ｐ Ｇ 和人胃癌细胞 Ｂ Ｇ Ｃ ８２３ 作为研究对象,利用 Ｍ Ｔ Ｔ 测定、３ Ｈ Ｔｄ Ｒ 参入、流式细胞术、软琼脂培养、 Ｎｏｒｔｈｅｒｎ ｂｌｏｔ、 Ｗ ｓｔｅｒｎ ｂｌｏｔ 等实验方法,观察了稀土化合物氯化亚鈰（ Ｃｅ Ｃｌ３）抑癌作用．结果表明, Ｃｅ Ｃｌ３ 浓度为 ００５ ｍ ｍ ｏｌ／ Ｌ,０１ ｍ ｍ ｏｌ／ Ｌ,０５ ｍ ｍ ｏｌ／ Ｌ和 １ ｍ ｍ ｏｌ／ Ｌ可抑制 Ｐ Ｇ 细胞的增殖;浓度为 ０５ ｍ ｍ ｏｌ／ Ｌ和 １ ｍ ｍ ｏｌ／ Ｌ可抑制 Ｐ Ｇ 细胞 Ｄ Ｎ Ａ 的合成,其 Ｇ１ 期细胞比例增加而 Ｓ期细胞比例减少,在软琼脂中的生长能力降低,原癌基因 ｃ ｍ ｙｃ 和 ｃ ｒａｓ 表达降低,ｐ１６ 蛋白质表达降低．而同样浓度的 Ｃｅ Ｃｌ３ 对 Ｂ Ｇ Ｃ ８２３ 细胞和正常细胞 ２ Ｂ Ｓ未见影响．提示：稀土化合物抑制肺癌细胞 Ｐ Ｇ 的增殖以及降低其恶性度的作用机制可能与一些增殖相关的原癌基因的表达和细胞周期的调控有关,其确切的机理还需进一步的研究．