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Статті в журналах з теми "Aided detection"

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Автор: Grafiati

Опубліковано: 28 вересня 2022

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1

Azavedo, E. "Computer aided detection." European Journal of Cancer 38, no. 11 (March 2002): S39. http://dx.doi.org/10.1016/s0959-8049(02)80100-9.

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2

Zheng, Bin, Xingwei Wang, Dror Lederman, Jun Tan, and David Gur. "Computer-Aided Detection." Academic Radiology 17, no. 11 (November 2010): 1401–8. http://dx.doi.org/10.1016/j.acra.2010.06.009.

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3

Scotti, James V. "Computer Aided Near Earth Object Detection." Symposium - International Astronomical Union 160 (1994): 17–30. http://dx.doi.org/10.1017/s0074180900046428.

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The Spacewatch program at the University of Arizona has pioneered automatic methods of detecting Near Earth Objects. Our software presently includes three modes of object detection: automatic motion identification; automatic streak identification; and visual streak identification. For automatic motion detection at sidereal drift rates, the 4σ detection threshold is near magnitude V = 20.9 for nearly stellar asteroid images. The automatic streak detection is able to locate streaks whose peak signal is above ~4σ and whose length is longer than about 10 pixels. Some visually detected streaks have had peak signals near ~1σ.Between 1990 September 25 and 1993 June 30, 45 new Near Earth asteroids, two comets and two Centaur's have been discovered with the system. An additional six comets, five Near Earth asteroids, and one Centaur were also “re-discovered”. The system has directly detected for the first time Near Earth Objects in the complete size range from about 5 kilometers to about 5 meters. Each month ~2,000 main belt asteroids are also detected.Future upgrades in both hardware, software, and telescope aperture may allow an order of magnitude increase in the rate of discovery of Near Earth Objects in the next several years. Several of the techniques proposed for the Spaceguard Survey have already been tested by Spacewatch, and others will need to be tested in the near future before such a survey can be implemented.

4

Raj, Abhishek, Alankrita, Akansha Srivastava, and Vikrant Bhateja. "Computer Aided Detection of Brain Tumor in Magnetic Resonance Images." International Journal of Engineering and Technology 3, no. 5 (2011): 523–32. http://dx.doi.org/10.7763/ijet.2011.v3.280.

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5

Zheng, Bin, Ratan Shah, Luisa Wallace, Christiane Hakim, Marie A. Ganott, and David Gur. "Computer-Aided Detection in Mammography." Academic Radiology 9, no. 11 (November 2002): 1245–50. http://dx.doi.org/10.1016/s1076-6332(03)80557-3.

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6

Basha, C. M. A. K. Zeelan, Maruthi Padmaja, and G. N. Balaji. "Computer Aided Fracture Detection System." Journal of Medical Imaging and Health Informatics 8, no. 3 (March 1, 2018): 526–31. http://dx.doi.org/10.1166/jmihi.2018.2324.

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7

Jun Yoon, Hong, Bin Zheng, Berkman Sahiner, and Dev P. Chakraborty. "Evaluating computer-aided detection algorithms." Medical Physics 34, no. 6Part1 (May 11, 2007): 2024–38. http://dx.doi.org/10.1118/1.2736289.

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8

Fenton, Joshua J., Christoph I. Lee, Guibo Xing, Laura-Mae Baldwin, and Joann G. Elmore. "Computer-Aided Detection in Mammography." JAMA Internal Medicine 174, no. 12 (December 1, 2014): 2032. http://dx.doi.org/10.1001/jamainternmed.2014.5410.

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9

Karssemeijer, N. "Computer-Aided Detection in Mammography." Imaging Decisions MRI 12, no. 3 (September 2008): 23–28. http://dx.doi.org/10.1111/j.1617-0830.2009.00130.x.

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10

Zrnec, Aljaž, and Dejan Lavbič. "Social network aided plagiarism detection." British Journal of Educational Technology 48, no. 1 (August 17, 2015): 113–28. http://dx.doi.org/10.1111/bjet.12345.

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11

Astley, S. M., and F. J. Gilbert. "Computer-aided detection in mammography." Clinical Radiology 59, no. 5 (May 2004): 390–99. http://dx.doi.org/10.1016/j.crad.2003.11.017.

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12

Li, Feng, Roger Engelmann, Samuel G. Armato, and Heber MacMahon. "Computer-Aided Nodule Detection System." Academic Radiology 22, no. 4 (April 2015): 475–80. http://dx.doi.org/10.1016/j.acra.2014.11.008.

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13

Zhang, Qun, and Frank Kschischang. "Correlation-Aided Nonlinear Spectrum Detection." Journal of Lightwave Technology 39, no. 15 (August 2021): 4923–31. http://dx.doi.org/10.1109/jlt.2021.3078700.

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14

Ouyang, Wanli, Xingyu Zeng, and Xiaogang Wang. "Single-Pedestrian Detection Aided by Two-Pedestrian Detection." IEEE Transactions on Pattern Analysis and Machine Intelligence 37, no. 9 (September 1, 2015): 1875–89. http://dx.doi.org/10.1109/tpami.2014.2377734.

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15

K.M., Abubakkar Sithik. "Computer Aided Diagnosis of Breast Nodule Detection Using Deep Learning Technique." International Journal of Psychosocial Rehabilitation 24, no. 5 (March 31, 2020): 1297–324. http://dx.doi.org/10.37200/ijpr/v24i5/pr201801.

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16

Kumar, M. N. Arun, M. N. Anil Kumar, and H. S. Sheshadri. "Computer Aided Detection of Clustered Microcalcification: A Survey." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 2 (January 10, 2019): 132–49. http://dx.doi.org/10.2174/1573405614666181012103750.

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Background: This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques. </P><P> Discussion: The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Breast cancer is one of the significant causes of death among women aged above 40. Mammography is considered the most successful means for prompt and timely detection of breast cancers. One notable visual indication of the malignant growth is the appearance of Masses, Architectural Distortions, and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind viewers, and it is hard for radiologists to supply both precise and steady assessment for a large number of mammograms created in extensive screening. Computer Aided Detection has been employed to help radiologists in detecting MC and MCCs. The automatic recognition of malignant MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation techniques are also summarized. Conclusion: The techniques used for segmentation of PM, MC detection and classification in a digitized mammogram are reviewed.

17

Soo, Mary Scott, Eric L. Rosen, Jessie Q. Xia, Sujata Ghate, and Jay A. Baker. "Computer-Aided Detection of Amorphous Calcifications." American Journal of Roentgenology 184, no. 3 (March 2005): 887–92. http://dx.doi.org/10.2214/ajr.184.3.01840887.

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18

Astley, Susan M. "Computer-aided detection for screening mammography." International Congress Series 1256 (June 2003): 927–32. http://dx.doi.org/10.1016/s0531-5131(03)00297-8.

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19

Yoon, Hong Jun, Bin Zheng, Berkman Sahiner, and Dev P. Chakraborty. "Erratum: “Evaluating computer-aided detection algorithms”." Medical Physics 35, no. 11 (October 28, 2008): 5197. http://dx.doi.org/10.1118/1.2995750.

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20

Perumpillichira, James J. "Computer-aided detection for virtual colonoscopy." Cancer Imaging 5, no. 1 (2005): 11–16. http://dx.doi.org/10.1102/1470-7330.2005.0016.

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21

Castellino, Ronald A. "Computer aided detection (CAD): an overview." Cancer Imaging 5, no. 1 (2005): 17–19. http://dx.doi.org/10.1102/1470-7330.2005.0018.

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22

Castellino, Ronald A. "Computer-Aided Detection in Oncologic Imaging." Cancer Journal 8, no. 2 (March 2002): 93–99. http://dx.doi.org/10.1097/00130404-200203000-00003.

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23

Hwang, Jiyoung, Myung Jin Chung, Younga Bae, Kyung Min Shin, Sun Young Jeong, and Kyung Soo Lee. "Computer-Aided Detection of Lung Nodules." Journal of Computer Assisted Tomography 34, no. 1 (January 2010): 31–34. http://dx.doi.org/10.1097/rct.0b013e3181b5c630.

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24

Niemeijer, Meindert, Marco Loog, Michael David Abràmoff, Max A. Viergever, Mathias Prokop, and Bram van Ginneken. "On Combining Computer-Aided Detection Systems." IEEE Transactions on Medical Imaging 30, no. 2 (February 2011): 215–23. http://dx.doi.org/10.1109/tmi.2010.2072789.

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25

Milne, Eric N. C. "Computer-aided Detection of Breast Cancer." Radiology 233, no. 2 (November 2004): 615–17. http://dx.doi.org/10.1148/radiol.2332040627.

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26

Hall, Ferris M. "Breast Imaging and Computer-Aided Detection." New England Journal of Medicine 356, no. 14 (April 5, 2007): 1464–66. http://dx.doi.org/10.1056/nejme078028.

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27

Bandiera, F., and G. Ricci. "Decision-aided GLR-based group detection." Electronics Letters 39, no. 5 (2003): 467. http://dx.doi.org/10.1049/el:20030295.

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28

Chi, Yanling, Jiayin Zhou, Sudhakar K. Venkatesh, Su Huang, Qi Tian, Tiffany Hennedige, and Jimin Liu. "Computer-aided focal liver lesion detection." International Journal of Computer Assisted Radiology and Surgery 8, no. 4 (March 31, 2013): 511–25. http://dx.doi.org/10.1007/s11548-013-0832-8.

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29

LLOBET, R., J. PEREZCORTES, A. TOSELLI, and A. JUAN. "Computer-aided detection of prostate cancer." International Journal of Medical Informatics 76, no. 7 (July 2007): 547–56. http://dx.doi.org/10.1016/j.ijmedinf.2006.03.001.

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30

Xu, Yan-ran, and Jun Zhao. "Computer-aided detection for CT colonography." Journal of Shanghai Jiaotong University (Science) 19, no. 5 (October 2014): 531–37. http://dx.doi.org/10.1007/s12204-014-1536-0.

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31

Astley, Susan M. "Computer-aided detection for screening mammography1." Academic Radiology 11, no. 10 (October 2004): 1139–43. http://dx.doi.org/10.1016/j.acra.2004.07.009.

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32

Summers, Ronald M., Jiamin Liu, Bhavya Rehani, Phillip Stafford, Linda Brown, Adeline Louie, Duncan S. Barlow, et al. "CT Colonography Computer-Aided Polyp Detection." Academic Radiology 17, no. 8 (August 2010): 948–59. http://dx.doi.org/10.1016/j.acra.2010.03.024.

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33

Summers, Ronald M. "Evaluation of Computer-aided Detection Devices." Academic Radiology 19, no. 4 (April 2012): 377–79. http://dx.doi.org/10.1016/j.acra.2012.01.010.

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34

Choi, Jinho. "Data-Aided Sensing for Distributed Detection." IEEE Wireless Communications Letters 10, no. 5 (May 2021): 1138–41. http://dx.doi.org/10.1109/lwc.2021.3064690.

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35

Jia, Tong, Cheng Dong Wu, and Ying Wei. "Computer Aided Lung Nodule Detection on CT Data." Applied Mechanics and Materials 44-47 (December 2010): 3492–96. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3492.

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A new computer-aided detection (CAD) scheme for detecting lung nodules is proposed in this paper. Firstly, the lung region is segmented from the CT data using adaptive threshold algorithm etc; Secondly, building active contour model to segment and remove lung vessel accurately in the lung region; Next, suspicious nodules are detected and omitted renal vessel is filtered using a selective shape filter; Finally, nodule features are extracted and rule-based classifier is used to distinguish true or false positive nodules. Experiment results indicate that this scheme can help radiologist improve the diagnosis efficiency.

36

Ziyad, Shabana Rasheed, Venkatachalam Radha, and Thavavel Vayyapuri. "Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 1 (January 6, 2020): 16–26. http://dx.doi.org/10.2174/1573405615666190206153321.

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Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

37

Zaman, Asim, Baozhang Ren, and Xiang Liu. "Artificial Intelligence-Aided Automated Detection of Railroad Trespassing." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 7 (May 9, 2019): 25–37. http://dx.doi.org/10.1177/0361198119846468.

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Trespassing is the leading cause of rail-related deaths and has been on the rise for the past 10 years. Detection of unsafe trespassing of railroad tracks is critical for understanding and preventing fatalities. Witnessing these events has become possible with the widespread deployment of large volumes of surveillance video data in the railroad industry. This potential source of information requires immense labor to monitor in real time. To address this challenge this paper describes an artificial intelligence (AI) framework for the automatic detection of trespassing events in real time. This framework was implemented on three railroad video live streams, a grade crossing and two right-of-ways, in the United States. The AI algorithm automatically detects trespassing events, differentiates between the type of violator (car, motorcycle, truck, pedestrian, etc.) and sends an alert text message to a designated destination with important information including a video clip of the trespassing event. In this study, the AI has analyzed hours of live footage with no false positives or missed detections yet. This paper and its subsequent studies aim to provide the railroad industry with state-of-the-art AI tools to harness the untapped potential of an existing closed-circuit television infrastructure through the real-time analysis of their data feeds. The data generated from these studies will potentially help researchers understand human factors in railroad safety research and give them a real-time edge on tackling the critical challenges of trespassing in the railroad industry.

38

Carrillo-de-Gea, Juan Manuel, Ginés García-Mateos, José Luis Fernández-Alemán, and José Luis Hernández-Hernández. "A Computer-Aided Detection System for Digital Chest Radiographs." Journal of Healthcare Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/8208923.

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Computer-aided detection systems aim at the automatic detection of diseases using different medical imaging modalities. In this paper, a novel approach to detecting normality/pathology in digital chest radiographs is proposed. The problem tackled is complicated since it is not focused on particular diseases but anything that differs from what is considered as normality. First, the areas of interest of the chest are found using template matching on the images. Then, a texture descriptor called local binary patterns (LBP) is computed for those areas. After that, LBP histograms are applied in a classifier algorithm, which produces the final normality/pathology decision. Our experimental results show the feasibility of the proposal, with success rates above 87% in the best cases. Moreover, our technique is able to locate the possible areas of pathology in nonnormal radiographs. Strengths and limitations of the proposed approach are described in the Conclusions.

39

Kadhim, Omar Raad, Hassan Jassim Motlak, and Kasim Karam Abdalla. "Computer-aided diagnostic system kinds and pulmonary nodule detection efficacy." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (October 1, 2022): 4734. http://dx.doi.org/10.11591/ijece.v12i5.pp4734-4745.

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This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.

40

Bartlett, Megan L., and Jason S. McCarley. "Ironic efficiency in automation-aided signal detection." Ergonomics 64, no. 1 (August 24, 2020): 103–12. http://dx.doi.org/10.1080/00140139.2020.1809716.

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41

Schnorrenberg, F., C. S. Pattichis, K. C. Kyriacou, and C. N. Schizas. "Computer-aided detection of breast cancer nuclei." IEEE Transactions on Information Technology in Biomedicine 1, no. 2 (June 1997): 128–40. http://dx.doi.org/10.1109/4233.640655.

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42

Sciotino, C., R. Etienne-Cummings, H. Lehmann, J. Lewin, C. Cheng-wu, and B. Asiyanbola. "Computer Aided Detection Of Retained Foreign Bodies." Journal of Surgical Research 165, no. 2 (February 2011): 175. http://dx.doi.org/10.1016/j.jss.2010.11.820.

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43

Castellino, R. A. "Computer-aided detection (CAD) in screening mammography." Cancer Imaging 1, no. 1 (2000): 25–27. http://dx.doi.org/10.1102/1470-7330/00/010025+03.

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44

Hong, Wei, Feng Qiu, and Arie kaufman. "A Pipeline for Computer Aided Polyp Detection." IEEE Transactions on Visualization and Computer Graphics 12, no. 5 (September 2006): 861–68. http://dx.doi.org/10.1109/tvcg.2006.112.

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45

Pan, Tony C., Metin N. Gurcan, Stephen A. Langella, Scott W. Oster, Shannon L. Hastings, Ashish Sharma, Benjamin G. Rutt, et al. "GridCAD: Grid-based Computer-aided Detection System." RadioGraphics 27, no. 3 (May 2007): 889–97. http://dx.doi.org/10.1148/rg.273065153.

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46

Naja, Ghinwa, Pierre Bouvrette, Sabahudin Hrapovich, Yali Liu, and John H. T. Luong. "Detection of bacteria aided by immuno-nanoparticles." Journal of Raman Spectroscopy 38, no. 11 (2007): 1383–89. http://dx.doi.org/10.1002/jrs.1785.

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47

Ueda, Yuko. "CAD (Computer Aided Detection) for Digital Mammography." Japanese Journal of Radiological Technology 63, no. 12 (2007): 1412–17. http://dx.doi.org/10.6009/jjrt.63.1412.

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48

Hall, Ferris M. "Computer-Aided Detection (CAD) of Amorphous Calcifications." American Journal of Roentgenology 186, no. 3 (March 2006): 902. http://dx.doi.org/10.2214/ajr.06.5013.

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49

Fu, Yanan, Wei Zhang, Mrinal Mandal, and Max Q. H. Meng. "Computer-Aided Bleeding Detection in WCE Video." IEEE Journal of Biomedical and Health Informatics 18, no. 2 (March 2014): 636–42. http://dx.doi.org/10.1109/jbhi.2013.2257819.

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50

Wood, Chris. "Computer Aided Detection (CAD) for Breast MRI." Technology in Cancer Research & Treatment 4, no. 1 (February 2005): 49–53. http://dx.doi.org/10.1177/153303460500400107.

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Since 1999, there has been a 40 percent increase per year in the number of breast MR studies performed in the United States. In addition, over 1200 sites in the United States have purchased surface coils for use in breast MR. This number is expected to grow to over 2,000 coils by the end of 2007. It is well accepted that MR sensitivity for invasive breast cancers is near 100%, but as the use of breast MRI increases, radiologists interpreting breast MR are challenged to achieve high specificity while retaining high sensitivity. Reading the large number of acquired MR images in a reasonable amount of time also becomes more important as the number of studies increases. Breast MR acquisition and image interpretation techniques have been refined through clinical optimization. The number of images to interpret, however, has increased to several hundred per case. Computer Aided Detection (CAD) algorithms have allowed radiologists to regain efficiency while maintaining optimized acquisition techniques. The first CAD system for breast MR (CADstream by Confirma, Inc.) was launched in January 2003. The CAD installed base has since grown to over 150 systems in the US. The primary reason for this quick adoption of CAD for breast MR is that the CAD software enables readers to increase their efficiency while potentially improving their overall accuracy. The full benefits CAD for Breast MR are realized when the interpreting radiologist has a thorough understanding of the algorithms used, and the limitations of CAD.