旅游景点英语引言
英文文献原文
Digital image processing and pattern recognition techniques for the detection of cancer
Cancer is the cond leading cau of death for both men and women in the world , and is expected to become the leading cau of death in the next few decades . In recent years , cancer detection has become a significant area of rearch activities in the image processing and pattern recognition community .Medical imaging technologies have already made a great impact on our capabilities of detecting cancer early and diagnosing the dia more accurately . In order to further improve the efficiency and veracity of diagnos and treatment , image processing and pattern recognition techniques have been widely applied to analysis and recognition of cancer , evaluation of the effectiveness of treatment , and prediction of the development of cancer . The aim of this special issue is to bring together rearchers working on image processing and pattern recognition techniques for the detection and asssment of cancer , and to promote rearch in image processing and pattern recognition for oncology . A number of papers were submitted to this special issue and each was peer-reviewed by at least three experts in the field . From the submitted papers , 17were finally lected f
or inclusion in this special issue . The lected papers cover a broad range of topics that are reprentative of the state-of-the-art in computer-aided detection or diagnosis(CAD)of cancer . They cover veral imaging modalities(such as CT , MRI , and mammography) and different types of cancer (including breast cancer , skin cancer , etc.) , which we summarize below .
四级真题听力
Skin cancer is the most prevalent among all types of cancers . Three papers in this special issue deal with skin cancer . Y uan et al. propo a skin lesion gmentation method. The method is bad on region fusion and narrow-band energy graph partitioning . The method can deal with challenging situations with skin lesions , such as topological changes , weak or fal edges , and asymmetry . T ang propos a snake-bad approach using multi-direction gradient vector flow (GVF) for the gmentation of skin cancer images . A new anisotropic diffusion filter is developed as a preprocessing step . After the noi is removed , the image is gmented using a GVF
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lancysnake . The propod method is robust to noi and can correctly trace the boundary of the skin cancer even if there are other objects near the skin cancer region . Serrano et al. prent a method bad on Markov random fields (MRF) to detect different patterns in dermoscopic images . Different
from previous approaches on automatic dermatological image classification with the ABCD rule (Asymmetry , Border irregularity , Color variegation , and Diameter greater than 6mm or growing) , this paper follows a new trend to look for specific patterns in lesions which could lead physicians to a clinical asssment.
法语学习网站Breast cancer is the most frequently diagnod cancer other than skin cancer and a leading cau of cancer deaths in women in developed countries . In recent years , CAD schemes have been developed as a potentially efficacious solution to improving radiologists’diagnostic accuracy in breast cancer screening and diagnosis . The predominant approach of CAD in breast cancer and medical imaging in general is to u automated image analysis to rve as a “cond reader”, with the aim of improving radiologists’diagnostic performance . Thanks to inten rearch and development efforts , CAD schemes have now been introduces in screening mammography , and clinical studies have shown that such schemes can result in higher nsitivity at the cost of a small increa in recall rate . In this issue , we have three papers in the area of CAD for breast cancer . Wei et al. propo an image-retrieval bad approach to CAD , in which retrieved images similar to that being evaluated (called the query image) are ud to support a CAD classifier , yielding an improved measure of malignancy . This involves arching a large databa for the images that are most similar to the qu
ery image , bad on features that are automatically extracted from the images . Dominguez et al. investigate the u of image features characterizing the boundary contours of mass lesions in mammograms for classification of benign vs. Malignant mass . They study and evaluate the impact of the features on diagnostic accuracy with veral different classifier designs when the lesion contours are extracted using two different automatic gmentation techniques . Schaefer et al. study the u of thermal imaging for breast cancer detection . In their scheme , statistical features are extracted from thermograms to quantify bilateral differences between left and right breast regions , which are ud subquently as input to a fuzzy-rule-bad classification system for diagnosis.
Colon cancer is the third most common cancer in men and women , and also the third most
common cau of cancer-related death in the USA . Y ao et al. propo a novel technique to detect colonic polyps using CT Colonography . They u ideas from geographic information systems to employ topographical height maps , which mimic the procedure ud by radiologists for the detection of polyps . The technique can also be ud to measure consistently the size of polyps . Hafner et al. prent a technique to classify and asss colonic polyps , which are precursors of colorectal cancer . The classification is performed bad on the pit-pattern in zoom-endoscopy images . They propo
uppitya novel color waveler cross co-occurence matrix which employs the wavelet transform to extract texture features from color channels.
Lung cancer occurs most commonly between the ages of 45 and 70 years , and has one of the wor survival rates of all the types of cancer . Two papers are included in this special issue on lung cancer rearch . Pattichis et al. evaluate new mathematical models that are bad on statistics , logic functions , and veral statistical classifiers to analyze reader performance in grading chest radiographs for pneumoconiosis . The technique can be potentially applied to the detection of nodules related to early stages of lung cancer . El-Baz et al. focus on the early diagnosis of pulmonary nodules that may lead to lung cancer . Their methods monitor the development of lung nodules in successive low-do chest CT scans . They propo a new two-step registration method to align globally and locally two detected nodules . Experments on a relatively large data t demonstrate that the propod registration method contributes to preci identification and diagnosis of nodule development .
It is estimated that almost a quarter of a million people in the USA are living with kidney cancer and that the number increas by 51000 every year . Linguraru et al. propo a computer-assisted radiology tool to asss renal tumors in contrast-enhanced CT for the management of tumor diagnos
is and respon to treatment . The tool accurately gments , measures , and characterizes renal tumors, and has been adopted in clinical practice . V alidation against manual tools shows high correlation .
Neuroblastoma is a cancer of the sympathetic nervous system and one of the most malignant dias affecting children . Two papers in this field are included in this special issue . Sertel et al. prent techniques for classification of the degree of Schwannian stromal development as either stroma-rich or stroma-poor , which is a critical decision factor affecting the
prognosis . The classification is bad on texture features extracted using co-occurrence statistics and local binary patterns . Their work is uful in helping pathologists in the decision-making process . Kong et al. propo image processing and pattern recognition techniques to classify the grade of neuroblastic differentiation on whole-slide histology images . The prented technique is promising to facilitate grading of whole-slide images of neuroblastoma biopsies with high throughput .
This special issue also includes papers which are not derectly focud on the detection or diagnosis of a specific type of cancer but deal with the development of techniques applicable to cancer detection . T a et al. propo a framework of graph-bad tools for the gmentation of microscopic phonetic
cellular images . Bad on the framework , automatic or interactive gmentation schemes are developed for color cytological and histological images . T osun et al. propo an object-oriented gmentation algorithm for biopsy images for the detection of cancer . The propod algorithm us a homogeneity measure bad on the distribution of the objects to characterize tissue components . Colon biopsy images were ud to verify the effectiveness of the method ; the gmentation accuracy was improved as compared to its pixel-bad counterpart . Narasimha et al. prent a machine-learning tool for automatic texton-bad joint classification and gmentation of mitochondria in MNT-1 cells imaged using an ion-abrasion scanning electron microscope . The propod approach has minimal ur intervention and can achieve high classification accuracy . El Naqa et al. investigate intensity-volume histogram metrics as well as shape and texture features extracted from PET images to predict a patient’s respon to treatment . Preliminary results suggest that the propod approach could potentially provide better tools and discriminant power for functional imaging in clinical prognosis.
中译日翻译器
We hope that the collection of the lected papers in this special issue will rve as a basis for inspiring further rigorous rearch in CAD of various types of cancer . We invite you to explore this special issue and benefit from the papers .money什么意思
april缩写On behalf of the Editorial Committee , we take this opportunity to gratefully acknowledge the autors and the reviewers for their diligence in abilding by the editorial timeline . Our thanks also go to the Editors-in-Chief of Pattern Recognition , Dr. Robert S. Ledley and Dr.C.Y. Suen , for their encouragement and support for this special issue .
英文文献译文
数字图像处理和模式识别技术关于检测癌症的应用
世界上癌症是对于人类（不论男人还是女人）生命的第二杀手。而且，在今后几十年里，预计癌症会变为威胁生命的第一因素。近几年里，癌症检测已经成为在图像处理和模式识别领域中，重要的研究活动。医学成像技术已经在癌症早期检测和诊断该疾病的准确性的能力方面起了很大的作用。为了进一步提高诊断和治疗的效率和准确性，图像处理和模式识别技术已广泛应用于分析和识别癌症，鉴定治疗效果，并预测癌症的发展。这个特殊的问题的目的是汇集研究人员图像处理和模式识别技术的检测和评估的癌症，并促进对图像处理和模式识别肿瘤的研究。关于这个特殊论题的一些论文已提交，每篇论文都经过这个领域的专家审查至少三遍。从这些提交了的论文中，最终有17篇当选。这些被选定的论文涉及的议题范围广泛，是计算机辅助检测或诊断（CAD）癌症的技术发展水平的代表。它们涉及若干成像方式（例如CT、核磁共振成像技术MRI和乳房X射线照相术）和不同类型的癌症（包括乳腺癌，皮肤癌等等），我们总结如下：
皮肤癌是所有类型的癌症中最普遍的。在这个特殊论题上的三份论文论述了皮肤癌。Yuan 等人提出了一种皮肤病灶分割方法。该方法是基于区域的融合和窄带能量图分割。该方法能处理复杂情况下的皮肤病变，如拓扑的变化，弱的或伪造的边缘，和不对称性。唐氏提出了一种使用多方向梯度矢量流（GVF）跟踪运动目标的方法来实现皮肤癌图像分割。一种新的各向异性扩散滤波发展成为一个预处理步骤。噪声消除后，使用梯度矢量流（GVF）分割图像。该方法具有较强的性能排除噪音影响，即使有其他物体分布在皮肤癌病变区域，也可以正确进行皮肤癌边缘检测。Serrano等人提出了一种基于马尔可夫随机场（MRF）来检测dermoscopic图像的不同模式。不同于以往皮肤图像按照各种规则（不对称性，边界不规则性，颜色异质性，以及直径大于6毫米或者增长）自动分类的方法，这篇文章遵循新趋势检测病变的具体模式，指导医生的临床评估。
乳腺癌是除了皮肤癌以外最普遍的癌症，是在发达国家里导致妇女死亡的头号杀手。近年来，计算机辅助探测（CAD）方案已经发展成为一个很有发展潜力的解决办法，这种方法提高了放射科在乳腺癌筛查和诊断的确诊率。总体上，在乳腺癌和医疗成像方面CAD的主要方式是使用自动图像分析，做“二次处理”，其目的是改善放射科的诊断性能。由于相信的英文
