Готові списки джерел за темами / Breast region of interest

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Добірка наукової літератури з теми "Breast region of interest"

Автор: Grafiati
Опубліковано: 10 січня 2023
Оновлено: 28 січня 2023

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Статті в журналах з теми "Breast region of interest"

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Yap, Moi Hoon, Manu Goyal, Fatima Osman, Robert Martí, Erika Denton, Arne Juette, and Reyer Zwiggelaar. "Breast ultrasound region of interest detection and lesion localisation." Artificial Intelligence in Medicine 107 (July 2020): 101880. http://dx.doi.org/10.1016/j.artmed.2020.101880.

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Kok Swee, Sim, Chia Fu Keong, Chong Sze Siang, Tso Chih Peng, Siti Fathimah Abbas, and Sarimah Omar. "Projection Based Region of Interest Segmentation in Breast MRI Images." International Journal on Advanced Science, Engineering and Information Technology 1, no. 1 (2011): 113. http://dx.doi.org/10.18517/ijaseit.1.1.26.

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3

Forbes, Florence, Nathalie Peyrard, Chris Fraley, Dianne Georgian-Smith, David M. Goldhaber, and Adrian E. Raftery. "Model-based Region-of-interest Selection in Dynamic Breast MRI." Journal of Computer Assisted Tomography 30, no. 4 (July 2006): 675–87. http://dx.doi.org/10.1097/00004728-200607000-00020.

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4

Sánchez-Ruiz, Daniel, Ivan Olmos-Pineda, and J. Arturo Olvera-López. "Automatic region of interest segmentation for breast thermogram image classification." Pattern Recognition Letters 135 (July 2020): 72–81. http://dx.doi.org/10.1016/j.patrec.2020.03.025.

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5

Nagarkar, Dilip B., Ezgi Mercan, Donald L. Weaver, Tad T. Brunyé, Patricia A. Carney, Mara H. Rendi, Andrew H. Beck, Paul D. Frederick, Linda G. Shapiro, and Joann G. Elmore. "Region of interest identification and diagnostic agreement in breast pathology." Modern Pathology 29, no. 9 (May 20, 2016): 1004–11. http://dx.doi.org/10.1038/modpathol.2016.85.

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6

Saadoon Abdoon, Rabab. "Utilizing image processing techniques for detecting breast abnormalities in thermography images." International Journal of Engineering & Technology 7, no. 4 (October 6, 2018): 2810. http://dx.doi.org/10.14419/ijet.v7i4.18312.

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Thermal Infrared (TIR) imaging of breasts involves a non-invasive, non-ionized, passive, safe and painless scan of the breasts. It is a graphing of the changes in breasts skin temperature using thermography. Thermograms are temperature distribution patterns with different colors to indicate temperature of the different regions within the tested breast, each color refers to a certain temperature range. In this work, three breast thermography images: one for normal case and two for cancerous cases, were employed to test the performance of the proposed segmentation methods: Region growing; clustering (K-means and FCM) algorithms and Histogram based enhancement technique to segment, detect and isolate the suspicious abnormal regions. These techniques were performed with the aid of suitable morphological operations to get the refined regions of interest. The results proved the efficiency of the proposed techniques to extract the abnormal (of high temperature) regions.
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7

ÇETİNEL, Gökçen, Fuldem MUTLU, and Sevda GÜL. "Detection of Breast Region of Interest via Breast MR Scan on an Axial Slice." International Journal of Applied Mathematics Electronics and Computers 8, no. 2 (June 30, 2020): 39–44. http://dx.doi.org/10.18100/ijamec.679142.

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Muhammad, Muhammad, Diyar Zeebaree, Adnan Mohsin Abdulazeez Brifcani, Jwan Saeed, and Dilovan Asaad Zebari. "Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images: A Review." Journal of Applied Science and Technology Trends 1, no. 3 (June 24, 2020): 78–91. http://dx.doi.org/10.38094/jastt20201328.

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The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.
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Divyashree, BV, Amarnath R, and Naveen M. "Novel approach to locate region of interest in mammograms for Breast cancer." International Journal of Intelligent Systems and Applications in Engineering 3, no. 6 (September 29, 2018): 185–90. http://dx.doi.org/10.18201/ijisae.2018644775.

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Muhammad, Muhammad, Diyar Zeebaree, Adnan Mohsin Abdulazeez Brifcani, Jwan Saeed, and Dilovan Asaad Zebari. "A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images." Journal of Applied Science and Technology Trends 1, no. 3 (June 24, 2020): 78–91. http://dx.doi.org/10.38094/2020jastt1328.

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Анотація:
The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.
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Дисертації з теми "Breast region of interest"

1

Budihal, Prasad Adhokshaja Achar. "Region of Interest Based Compression of Grayscale Images." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5842.

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Image compression based on Region of Interest (ROI) has been one of the hot topics of interest in image processing. There is not a single widely accepted method for detecting the ROI automatically form an image. To reduce the transmission bandwidth and storage space requirements of gray scale images, an algorithm is suggested for detecting the ROI automatically based on Tsallis entropy method. Tsallis entropy method is used to segment the image into two segments, the ROI and the background. These two segments can then be compressed at different rates, to avoid losing information in the ROI while achieving a good compression. Different approaches of compression based on wavelets and use of various compression methods are also discussed.
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2

Gopalan, Ramya. "Exploiting Region Of Interest For Improved Video Coding." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1250622014.

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3

Sinharoy, Indranil. "Region-of-interest estimation for multi-aperture imaging systems." Ann Arbor, Mich. : ProQuest, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1440435.

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Анотація:
Thesis (M.S. in Electrical Engineering)--S.M.U., 2007.
Title from PDF title page (viewed Mar. 18, 2008). Source: Masters Abstracts International, Volume: 45-03, page: 1615. Adviser: Scott C. Douglas. Includes bibliographical references.
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4

Sassi, Salem Ahmed. "Region of interest imaging technique : a novel approach to increase image contrast within the region of interest and reduce patient dose in fluoroscopy." Thesis, St George's, University of London, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264975.

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Chambers, Julie Anne. "Analysis of the BRCA1 region in human and mouse." Thesis, University College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298465.

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Karlsson, Linda S. "Spatio-Temporal Pre-Processing Methods for Region-of-Interest Video Coding." Licentiate thesis, Mid Sweden University, Department of Information Technology and Media, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-51.

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In video transmission at low bit rates the challenge is to compress the video with a minimal reduction of the percieved quality. The compression can be adapted to knowledge of which regions in the video sequence are of most interest to the viewer. Region of interest (ROI) video coding uses this information to control the allocation of bits to the background and the ROI. The aim is to increase the quality in the ROI at the expense of the quality in the background. In order for this to occur the typical content of an ROI for a particular application is firstly determined and the actual detection is performed based on this information. The allocation of bits can then be controlled based on the result of the detection.

In this licenciate thesis existing methods to control bit allocation in ROI video coding are investigated. In particular pre-processing methods that are applied independently of the codec or standard. This makes it possible to apply the method directly to the video sequence without modifications to the codec. Three filters are proposed in this thesis based on previous approaches. The spatial filter that only modifies the background within a single frame and the temporal filter that uses information from the previous frame. These two filters are also combined into a spatio-temporal filter. The abilities of these filters to reduce the number of bits necessary to encode the background and to successfully re-allocate these to the ROI are investigated. In addition the computational compexities of the algorithms are analysed.

The theoretical analysis is verified by quantitative tests. These include measuring the quality using both the PSNR of the ROI and the border of the background, as well as subjective tests with human test subjects and an analysis of motion vector statistics.

The qualitative analysis shows that the spatio-temporal filter has a better coding efficiency than the other filters and it successfully re-allocates the bits from the foreground to the background. The spatio-temporal filter gives an improvement in average PSNR in the ROI of more than 1.32 dB or a reduction in bitrate of 31 % compared to the encoding of the original sequence. This result is similar to or slightly better than the spatial filter. However, the spatio-temporal filter has a better performance, since its computational complexity is lower than that of the spatial filter.

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Sundstedt, Karin Veronica. "Rendering and validation of high-fidelity graphics using region-of-interest." Thesis, University of Bristol, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440273.

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Rafiee, Gholamreza. "Automatic region-of-interest extraction in low depth-of-field images." Thesis, University of Newcastle upon Tyne, 2013. http://hdl.handle.net/10443/2194.

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Automatic extraction of focused regions from images with low depth-of-field (DOF) is a problem without an efficient solution yet. The capability of extracting focused regions can help to bridge the semantic gap by integrating image regions which are meaningfully relevant and generally do not exhibit uniform visual characteristics. There exist two main difficulties for extracting focused regions from low DOF images using high-frequency based techniques: computational complexity and performance. A novel unsupervised segmentation approach based on ensemble clustering is proposed to extract the focused regions from low DOF images in two stages. The first stage is to cluster image blocks in a joint contrast-energy feature space into three constituent groups. To achieve this, we make use of a normal mixture-based model along with standard expectation-maximization (EM) algorithm at two consecutive levels of block size. To avoid the common problem of local optima experienced in many models, an ensemble EM clustering algorithm is proposed. As a result, relevant blocks, i.e., block-based region-of-interest (ROI), closely conforming to image objects are extracted. In stage two, two different approaches have been developed to extract pixel-based ROI. In the first approach, a binary saliency map is constructed from the relevant blocks at the pixel level, which is based on difference of Gaussian (DOG) and binarization methods. Then, a set of morphological operations is employed to create the pixel-based ROI from the map. Experimental results demonstrate that the proposed approach achieves an average segmentation performance of 91.3% and is computationally 3 times faster than the best existing approach. In the second approach, a minimal graph cut is constructed by using the max-flow method and also by using object/background seeds provided by the ensemble clustering algorithm. Experimental results demonstrate an average segmentation performance of 91.7% and approximately 50% reduction of the average computational time by the proposed colour based approach compared with existing unsupervised approaches.
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Smith, Sarah Jane. "Cancer in Trent region : incidence, mortality and survival." Thesis, University of Nottingham, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312199.

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Chihaoui, Takwa. "Système d'identification de personnes basé sur la rétine." Thesis, Paris Est, 2018. http://www.theses.fr/2018PESC1145/document.

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Notre travail s’inscrit dans le cadre de la biométrie par la rétine. La rétine est la couche sensorielle de l’œil, elle présente une texture riche et unique même chez les jumeaux. Ses propriétés ont fait de la biométrie par la rétine un axe de recherche actif. En effet, de nombreuses méthodes ont été proposées pour les différentes étapes de la méthode biométrique allant du prétraitement de l’image rétinienne à son analyse, en passant par sa caractérisation, afin d’identifier et authentifier un individu. Nous nous intéressons dans ces travaux de thèse, à l’étude, la conception, le développement et l’évaluation d’une nouvelle méthode biométrique basée sur la rétine. Notre première contribution réside dans la conception d’une méthode d’analyse d’image rétinienne saine et pathologique, suivie d’une sélection d’une région d’intérêt autour du disque optique. Cette méthode améliore la qualité de l’image rétinienne et extrait une région d’intérêt la plus stable de la rétine afin de maintenir une densité d’information satisfaisante, pour assurer une meilleure qualité de reconnaissance. Notre deuxième contribution porte sur la proposition d’une nouvelle méthode d’extraction de caractéristiques locales basée sur le descripteur standard SIFT. Elle applique une nouvelle approche reposant sur la suppression des points d’intérêt non informatifs extraits par le descripteur standard SIFT. Cette nouvelle méthode d’extraction des caractéristiques locales réduit le nombre des points d’intérêt redondants tout en maintenant la qualité de la description. Nous avons validé, la méthode biométrique proposée sur différentes bases comprenant des images saines et pathologiques. Les résultats obtenus montrent des performances encourageantes. Ces résultats indiquent, que la méthode que nous avons proposée, localise correctement la région d’intérêt rétinienne. En mode identification, un taux d’identification correcte d’environ 99.8% est atteint. En mode vérification, nous avons obtenu un taux FRR de 0.12% quant aux taux FAR et EER (erreur), ils sont de 0%. L’étude comparative a montré que notre méthode est plus discriminative que d’autres méthodes de l’état de l’art, notamment celles basées sur la segmentation et l’extraction de l’arbre vasculaire
Our work is part of the retina biometrics. The retina is the sensory layer of the eye; it has a rich and unique texture even in twins. Its properties have made the retina biometrics an active research area. Indeed, numerous methods have been proposed for the various stages of the biometric method, from pretreatment of the retinal image to its analysis, through its characterization, in order to identify and authenticate an individual. We are interested in this work in these thesis works, the study, design, development and evaluation of a new biometric method based on the retina. This thesis presents our contributions for each stage of the proposed biometric method. Our first contribution lies in the proposition of a healthy and pathological retinal image analysis method, followed by a selection of a region of interest around the optical disc. This method improves the quality of the retinal image and extracts a more stable region of interest from the retina to maintain a satisfactory information density, to ensure a better quality of recognition. Our second contribution consists in proposing a new method for extracting local characteristics based on the standard SIFT descriptor. It applies a new method based on the removal of non-informative points of interest extracted by the standard SIFT descriptor. This new method of extracting local features reduces the number of redundant points of interest while maintaining the quality of the description. We validated, the proposed biometric method on different bases including healthy and pathological images. This biometric method has yielded encouraging results on healthy and pathological retinal images. The results obtained show encouraging performances. These results indicate that the method we have proposed, correctly locates the retinal region of interest. In identification mode, a correct identification rate of approximately 99.8% is reached. In verification mode, we obtained 0.12% as FRR error rate and 0% for the FAR and EER error rates. The comparative study showed that our method is more discriminative than other state-of-the-art methods, especially those based on segmentation and extraction of the vascular tree
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Книги з теми "Breast region of interest"

1

North Western Regional Health Authority. Department of Public Health Medicine. Breast cancer in the North Western region: A report. Manchester: Department of Public Health Medicine, Centre for Cancer Epidemiology, 1993.

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2

Kavery, Benjamin. American strategic interest in Persian Gulf. Hyderabad: Cauvery Publications, 1990.

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3

Ḥarfūsh, Jamāl Karam. Breast-feeding patterns: A review of studies in the Eastern Mediterranean region. 2nd ed. Alexandria, Egypt: World Health Organization, Regional Office for the Eastern Mediterranean, 1993.

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4

Meier, Judith A. H. Advertisements and notices of interest: From Norristown, Pennsylvania, newspapers. Apollo, PA: Closson Press, 1987.

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5

Moynihan, K. T. Wildlife and sites of special wildlife interest in the western Waikato region. Wellington: New Zealand Wildlife Service, Dept. of Internal Affairs, 1986.

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6

Sato, Kiyotaka. Real interest rate linkages in the Asian-Pacific region: A time-varying parameter approach. Seoul, Korea: Korea Institute for International Economic Policy, 2004.

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7

Lai, Joe Heng. A polymorphic locus in the promoter region of the IGFBP3 gene is associated with mammographic breast density. Ottawa: National Library of Canada, 2003.

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8

Saba, Naureen. Directory of demographers and social scientists with interest in demography and economic development in the Asian region. Islamabad, Pakistan: National Institute of Population Studies, 1987.

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9

Wilson, William Bender. From the Hudson to the Ohio: A region of historic, romantic, and scenic interest and other sketches. [Philadelphia]: Kensington Press, 1987.

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10

Sing it to her bones. Hampton Falls, N.H: Beeler Large Print, 1999.

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Частини книг з теми "Breast region of interest"

1

Kooi, Thijs, Albert Gubern-Merida, Jan-Jurre Mordang, Ritse Mann, Ruud Pijnappel, Klaas Schuur, Ard den Heeten, and Nico Karssemeijer. "A Comparison Between a Deep Convolutional Neural Network and Radiologists for Classifying Regions of Interest in Mammography." In Breast Imaging, 51–56. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41546-8_7.

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Nieczkowski, Tomasz, and Andrzej Obuchowicz. "’Sonar’ — Region of Interest Identification and Segmentation Method for Cytological Breast Cancer Images." In Advances in Soft Computing, 566–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75175-5_71.

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Villalobos-Montiel, Adrian J., Mario I. Chacon-Murguia, Jorge D. Calderon-Contreras, and Leticia Ortega-Maynez. "Automatic Segmentation of Regions of Interest in Breast Thermographic Images." In Lecture Notes in Computer Science, 135–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19264-2_14.

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Amutha, S., D. R. Ramesh Babu, R. Mamatha, S. Vidhya Suman, and M. Ravi Shankar. "Speckle Noise Reduction in Breast Ultrasound Images for Segmentation of Region Of Interest (ROI) Using Discrete Wavelets." In Lecture Notes in Electrical Engineering, 747–54. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7618-0_72.

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Modica, Marcello. "Research Interest." In RaumFragen: Stadt – Region – Landschaft, 3–18. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-37681-9_1.

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AbstractThe occurrence of industrial brownfield sites in mountain regions is emerging as a key spatial development challenge. The European Alps offer a privileged case study area in this regards due to their high level of industrial maturity and the clear evidences of an ongoing structural change. The difference between urban and mountain brownfields seems to lie not much in the content of the sites, which is indeed functionally related to certain industries and processes, but more on the physical and non-physical relationships with the context. The misunderstanding of these specific conditions leads often to incomplete or even failed transformation attempts, as proved by few examples of the two recurring strategies implemented in the Alps: building recycling and land recycling. An alternative approach capable of highlighting and enhancing the infrastructural specificity of mountain brownfields, based on an holistic understanding of landscape, might help to overcome the existing planning and management shortcomings.
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Colborn, Gene L., and David B. Lause. "Pectoral Region and Breast." In Musculoskeletal Anatomy, 66–81. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9780429162473-7.

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Colborn, Gene L., and David B. Lause. "Pectoral Region and Breast." In Musculoskeletal Anatomy, 66–81. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9780429162473-7.

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Colborn, Gene L., and David B. Lause. "Pectoral Region and Breast." In Musculoskeletal Anatomy, 66–81. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9780429162473-7.

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Kim, Jaehwan, and Ilkwon Jeong. "Normalized Matting of Interest Region." In Advances in Visual Computing, 446–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41939-3_43.

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Verma, Manisha, and Shanmuganathan Raman. "Interest Region Based Motion Magnification." In Image Analysis and Processing - ICIAP 2017, 27–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68560-1_3.

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Тези доповідей конференцій з теми "Breast region of interest"

1

Aygüneş, Bulut, Selim Aksoy, Gökberk Cinbiş, Kemal Kösemehmetoglu, Sevgen Önder, and Ayşegül Üner. "Graph convolutional networks for region of interest classification in breast histopathology." In Digital Pathology, edited by John E. Tomaszewski and Aaron D. Ward. SPIE, 2020. http://dx.doi.org/10.1117/12.2550636.

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Manohar, Srirang, Sanne E. Vaartjes, Johan G. C. van Hespen, Joost M. Klaase, Frank M. van den Engh, Andy K. H. The, Wiendelt Steenbergen, and Ton G. van Leeuwen. "Region-of-interest breast images with the Twente Photoacoustic Mammoscope (PAM)." In Biomedical Optics (BiOS) 2007, edited by Alexander A. Oraevsky and Lihong V. Wang. SPIE, 2007. http://dx.doi.org/10.1117/12.699995.

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3

Yap, M. H., E. A. Edirisinghe, H. E. Bez, and H. T. Ewe. "Initial lesion detection and region of interest labeling in ultrasound breast images." In IET International Conference on Visual Information Engineering (VIE 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20060552.

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Pati, Pushpak, Sonali Andani, Matheus Palhares Viana, Maria Gabrani, Peter Wild, Jan Hendrik Ruschoff, and Matthew Pediaditis. "Deep positive-unlabeled learning for region of interest localization in breast tissue images." In Digital Pathology, edited by Metin N. Gurcan and John E. Tomaszewski. SPIE, 2018. http://dx.doi.org/10.1117/12.2293721.

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Michahial, Stafford, and Bindu A. Thomas. "A novel algorithm for locating region of interest in breast ultra sound images." In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE, 2017. http://dx.doi.org/10.1109/iceeccot.2017.8284598.

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Platania, Richard, Shayan Shams, Seungwon Yang, Jian Zhang, Kisung Lee, and Seung-Jong Park. "Automated Breast Cancer Diagnosis Using Deep Learning and Region of Interest Detection (BC-DROID)." In BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3107411.3107484.

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Cesário, G. J., L. Paixão, R. Carvalho, M. Chevalier, M. R. P. Attie, M. S. Nogueira, and DIVANIZIA SOUZA. "Dependence of the region of interest (ROI) on the evaluation of geometric distortion and ghost artifact-distortion in digital breast tomosynthesis." In Fifteenth International Workshop on Breast Imaging, edited by Chantal Van Ongeval, Nicholas Marshall, and Hilde Bosmans. SPIE, 2020. http://dx.doi.org/10.1117/12.2564353.

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Correa, Thays, Fabíola De Oliveira, Matheus Baffa, and Lucas Lattari. "Unsupervised Segmentation of Breast Infrared Images in Lateral View Using Histogram of Oriented Gradients." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wvc.2020.13477.

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Анотація:
Breast cancer is the second most common type of cancer in the world. It is estimated that 29.7% of new cases diagnosed in Brazil occur in any structures of the breasts. However, the disease has a good prognosis if detected early. Thus, the development of new technologies to help doctors to provide an accurate diagnosis is indispensable. The goal of this work is to develop a new method to automate parts of computer-aided diagnosis systems, performing the unsupervised segmentation of the Region of Interest (ROI) of infrared breast images acquired in lateral view. The segmentation proposed in this paper consists of three stages. The first stage pre-processes the infrared images of the lateral region of breasts. Later, features are extracted from a descriptor based on Histogram of Oriented Gradients (HOG). Concluding, a Machine Learning algorithm is used to perform the segmentation of the sample. The current method obtained an average of 89.9% accuracy and 94.3% specificity in our experiments, which is promising compared to other works.
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Lu, Ximing, Sachin Mehta, Tad T. Brunye, Donald L. Weaver, Joann G. Elmore, and Linda G. Shapiro. "Analysis of Regions of Interest and Distractor Regions in Breast Biopsy Images." In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2021. http://dx.doi.org/10.1109/bhi50953.2021.9508513.

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Patil, Shreyas Malakarjun, Li Tong, and May D. Wang. "Generating Region of Interests for Invasive Breast Cancer in Histopathological Whole-Slide-Image." In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2020. http://dx.doi.org/10.1109/compsac48688.2020.0-174.

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Звіти організацій з теми "Breast region of interest"

1

Forbes, Florence, Nathalie Peyrard, Chris Fraley, Dianne Georgian-Smith, David M. Goldhaber, and Adrian E. Raftery. Model-Based Region-of-Interest Selection in Dynamic Breast MRI. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada478329.

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2

Azevedo, S., P. Rizo, and P. Grangeat. Region-of-interest cone-beam computed tomography. Office of Scientific and Technical Information (OSTI), June 1995. http://dx.doi.org/10.2172/125412.

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3

Agarwal, Rahul. Whole genome resequencing: dissect 5alpha-androst-16-en-3-one region of interest on pigChr13. Cold Spring Harbor Laboratory, October 2016. http://dx.doi.org/10.1101/079665.

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Yılmaz, Bahri. The Regional Comprehensive Economic Partnership (RCEP): The World's Interest and Competition Concentrated in the Indo-Pacific Region. Sabanci University, December 2022. http://dx.doi.org/10.5900/su_fass_wp.2022.45047.

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Pagan, Jose A. Barriers to Breast Cancer Screening Among Latinas in the US-Mexico Border Region. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada484322.

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Pagan, Jose A. Barriers to Breast Cancer Screening Among Latinas in the U.S.-Mexico Border Region. Fort Belvoir, VA: Defense Technical Information Center, May 2008. http://dx.doi.org/10.21236/ada486634.

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Brown, Cynthia J. Barriers to Breast Cancer Screening among Latinas in the U.S.-Mexico Border Region. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada549852.

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Cowell, John K. Isolation of Genes from Chromosome Region Ip31 Involved in the Development of Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada396993.

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Cowell, John K. Isolation of Genes from Chromosome Region Ip31 Involved in the Development of Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada393453.

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Cowell, John K. Isolation of Genes From Chromosome Region Ip31 Involved in the Development of Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada384614.

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