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

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1、引言 1 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

2、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 efficie

3、ncy 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 to

4、gether 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 l

5、east 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 severa

6、l 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 . Yuan et al. pro

7、pose 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 . Tang proposes a snake-based approach using

8、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 snake . The proposed method is robust to noise and can correctly trace the

9、 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

10、 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

11、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

12、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 cli

13、nical 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

14、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 . Do

15、minguez 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 l

16、esion 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 re

17、gions , 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 . Yao et al. propose a novel technique to detect colonic pol

18、yps 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 techni

19、que 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 fro

20、m 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 st

21、atistics , 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

22、 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 t

23、hat 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-

24、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 . Validation against manual tools shows high co

25、rrelation . 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