外文翻译----数字图像处理和模式识别技术关于检测癌症的应用

引言

英文文献原文

Digital image processing and pattern recognition techniques for the detection of cancer

Cancer is the second leading cause of death for both men and women in the world , and is expected to become the leading cause of death in the next few decades . In recent years , cancer detection has become a significant area of research 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 disease more accurately . In order to further improve the efficiency and veracity of diagnoses 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 researchers working on image processing and pattern recognition techniques for the detection and assessment of cancer , and to promote research 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 these submitted papers , 17were finally selected for inclusion in this special issue . These selected papers cover a broad range of topics that are representative of the state-of-the-art in computer-aided detection or diagnosis(CAD)of cancer . They cover several imaging modalities(such as CT , MRI , and mammography) and different types of cancer (including breast cancer , skin cancer , etc.) , which we summarize below .

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. propose a skin lesion segmentation method. The method is based 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 false edges , and asymmetry . T ang proposes a snake-based approach using multi-direction gradient vector flow (GVF) for the segmentation of skin cancer images . A new anisotropic diffusion filter is developed as a preprocessing step . After the noise is removed , the image is segmented using a GVF

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snake . The proposed method is robust to noise and can correctly trace the boundary of the skin cancer even if there are other objects near the skin cancer region . Serrano et al. present a method based 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 assessment.

Breast cancer is the most frequently diagnosed cancer other than skin cancer and a leading cause 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 use automated image analysis to serve as a “second reader”, with the aim of improving radiologists’diagnostic performance . Thanks to intense research and development efforts , CAD schemes have now been introduces in screening mammography , and clinical studies have shown that such schemes can result in higher sensitivity at the cost of a small increase in recall rate . In this issue , we have three papers in the area of CAD for breast cancer . Wei et al. propose an image-retrieval based approach to CAD , in which retrieved images similar to that being evaluated (called the query image) are used to support a CAD classifier , yielding an improved measure of malignancy . This involves searching a large database for the images that are most similar to the query image , based on features that are automatically extracted from the images . Dominguez et al. investigate the use of image features characterizing the boundary contours of mass lesions in mammograms for classification of benign vs. Malignant masses . They study and evaluate the impact of these features on diagnostic accuracy with several different classifier designs when the lesion contours are extracted using two different automatic segmentation techniques . Schaefer et al. study the use 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 used subsequently as input to a fuzzy-rule-based classification system for diagnosis.

Colon cancer is the third most common cancer in men and women , and also the third most

common cause of cancer-related death in the USA . Y ao et al. propose a novel technique to detect colonic polyps using CT Colonography . They use ideas from geographic information systems to employ topographical height maps , which mimic the procedure used by radiologists for the detection of polyps . The technique can also be used to measure consistently the size of polyps . Hafner et al. present a technique to classify and assess colonic polyps , which are precursors of colorectal cancer . The classification is performed based on the pit-pattern in zoom-endoscopy images . They propose a 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 worse survival rates of all the types of cancer . Two papers are included in this special issue on lung cancer research . Pattichis et al. evaluate new mathematical models that are based on statistics , logic functions , and several 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-dose chest CT scans . They propose a new two-step registration method to align globally and locally two detected nodules . Experments on a relatively large data set demonstrate that the proposed registration method contributes to precise 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 increases by 51000 every year . Linguraru et al. propose a computer-assisted radiology tool to assess renal tumors in contrast-enhanced CT for the management of tumor diagnosis and response to treatment . The tool accurately segments , 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 diseases affecting children . Two papers in this field are included in this special issue . Sertel et al. present 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 based on texture features extracted using co-occurrence statistics and local binary patterns . Their work is useful in helping pathologists in the decision-making process . Kong et al. propose image processing and pattern recognition techniques to classify the grade of neuroblastic differentiation on whole-slide histology images . The presented 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 focused 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. propose a framework of graph-based tools for the segmentation of microscopic cellular images . Based on the framework , automatic or interactive segmentation schemes are developed for color cytological and histological images . T osun et al. propose an object-oriented segmentation algorithm for biopsy images for the detection of cancer . The proposed algorithm uses a homogeneity measure based on the distribution of the objects to characterize tissue components . Colon biopsy images were used to verify the effectiveness of the method ; the segmentation accuracy was improved as compared to its pixel-based counterpart . Narasimha et al. present a machine-learning tool for automatic texton-based joint classification and segmentation of mitochondria in MNT-1 cells imaged using an ion-abrasion scanning electron microscope . The proposed approach has minimal user 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 response to treatment . Preliminary results suggest that the proposed approach could potentially provide better tools and discriminant power for functional imaging in clinical prognosis.

We hope that the collection of the selected papers in this special issue will serve as a basis for inspiring further rigorous research in CAD of various types of cancer . We invite you to explore this special issue and benefit from these papers .

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

紧张的研究和开发工作，计算机辅助探测计划（CAD）目前已被引进到筛查乳房造影术。临床研究显示，这些计划可能会在稍微增加回现率的代价下达到更高的灵敏度。在这个问题上，针对乳腺癌的CAD领域有三篇论文。Wei等人提出了一种基于计算机图像探测（CAD）的图像检索的方法，其中检索图像类似于图像查询，被作为计算机辅助探测（CAD）分类器，从而使恶性肿瘤的检测得到改进。这需要寻找一个大型数据库，与图像查询相似，具有自动从图像中自动提取数据的功能。Dominguezet等人研究使用乳房透视检查的损害组织图像的边界轮廓特征区分良性或者恶性。他们用几个不同的分类器，对病变轮廓的提取使用两种不同的自动分割技术，研究和评估这些轮廓特征对诊断的准确性的影响。Schaefer 等人研究热成像在乳腺癌检测中的应用。在他们的方案中，统计特征从热量分析图中提取，从而量化左右乳房区域的差异，随后输入到模糊规则库分类系统进行诊断。

结肠癌是在男性或者女性中普遍性第三大的癌症，也是美国导致因癌症死亡的第三大最常见的原因。Y ao等人提出了一种使用CT结肠成像的新技术来检测结肠息肉。他们使用的方法是受地理信息系统采用地形高度图的启发，放射线学者模仿该方法检测息肉。这种技术还可以用来检测息肉的大小。Hafner等人展示了一种用来分类和评估大肠癌的前体细胞的结肠息肉的技术。这种分类技术基于坑格局变焦内镜图像起作用。他们提出了一种新颖的彩色小波跨共生矩阵，其中采用小波变换从提取彩色信号通道提取纹理特性。

肺癌发生的最常见的年龄介于45岁到70岁，并且在所有类型的癌症中生存率最低。这个特殊论题中，有两篇论文包括了对肺癌的研究。Pattichis等人评估新的基于统计数据、逻辑功能和一些统计分类的数学模型，从而分析检测器在尘肺病胸片分级中的性能。该技术应用于关于早期肺癌结核的检测潜力巨大。El-Baz等人专注于可能导致肺癌的肺结核的早期诊断。他们的方法通过连续低剂量胸部CT扫描监测结核的发展。他们提出一个新的两步登记的方法，使从整体和局部检测结核密切联系在一起。实验获得的相当多的一组数据表明，该方法有助于准确的识别和诊断结核的发展。

据估计，近25万在美国生活的人患有肾癌，并且人数每年增加5.1万。Linguraru等人介绍了一种电脑辅助放射工具。在对诊断肿瘤和疗效的管理的造影强化CT中，用此工具评估肾肿瘤。该工具准确地分割、估计和描绘肾肿瘤，并且已经应用到临床实践中。以手动工具校验的结果表明它们高度相关。

神经母细胞瘤——关于交感神经系统的癌症，是一种对于儿童的最致命的疾病。有两篇论文与这个领域相关。Sertel等人介绍了鉴别Schwannian基质发展程度（例如基质丰富或基质缺乏）的技术，这是一个影响预后（医学术语，指医生对疾病结果的预测）的重要决定因素。分类是根据应用共生统计和局部二进制模式提取的纹理特征。他们的工作有益于病理学家进行决策。Kong等人介绍了应用图像处理和模式识别技术对整个滑动组织图像的神

经细胞分化程度进行分类。所提出的技术应用于促进整个滑动的神经母细胞瘤组织切片图像高处理能力的程度分级是有希望的。

这一个特殊论题，还包括没有直接的专注于检测或诊断特定类型的癌症，但论述了适用于发展癌症检测技术的论文。Ta等人论述了一种分割显微细胞图像的工具框架。基于该框架，自动或交互式分割方案为细胞和彩色组织图像而制定。Tosun等人提出了一种面向对象的用于癌症检测的切片图像分割算法。该算法采用了基于对象的分布的同性质措施描绘组织的组成部分。结肠切片图像被用来验证了该方法的有效性；分割精度的提高是基于像素的对应。Narasimha等人描述了用于研究的机械工具，应用离子磨损扫描电子显微镜实现纹理的自动联合分割和MNT-1细胞图像中线粒体的分割。该方法极少用户干预并且可以实现高精度的分割。El Naqa等人研究强度容积柱状图指标以及从PET图像提取的形状和纹理特征，用来预测患者对治疗的反应。初步结果表明，该方法有可能为临床预后中的功能成像提供更好的工具和判别力。

我们希望，在这个特殊论题上的论文选编将作为一个基础，进一步激发人们严谨地研究各种癌症的计算机辅助探测（CAD）。我们邀请大家来研究这一特殊论题，充分地利用这些论文。

代表编辑委员会，我们借此机会深深地感谢所有作者和评审员对遵守主编的时间安排做出的努力。我们还要感谢模式识别的主编辑Robert S . Ledley博士和C . Y . Suen博士对这一特殊论题的鼓励和支持。