Literature DB >> 23247864

Computer-aided breast cancer detection using mammograms: a review.

Karthikeyan Ganesan1, U Rajendra Acharya, Chua Kuang Chua, Lim Choo Min, K Thomas Abraham, Kwan-Hoong Ng.   

Abstract

The American Cancer Society (ACS) recommends women aged 40 and above to have a mammogram every year and calls it a gold standard for breast cancer detection. Early detection of breast cancer can improve survival rates to a great extent. Inter-observer and intra-observer errors occur frequently in analysis of medical images, given the high variability between interpretations of different radiologists. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. A few papers have documented the general trend of computer-aided diagnosis of breast cancer, making a broad study of the several techniques involved. But, there is no definitive documentation focusing on the mathematical techniques used in breast cancer detection. This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts in the field.

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Year:  2012        PMID: 23247864     DOI: 10.1109/RBME.2012.2232289

Source DB:  PubMed          Journal:  IEEE Rev Biomed Eng        ISSN: 1937-3333


  22 in total

1.  Mass Detection in Mammographic Images Using Wavelet Processing and Adaptive Threshold Technique.

Authors:  P S Vikhe; V R Thool
Journal:  J Med Syst       Date:  2016-01-26       Impact factor: 4.460

2.  Mammogram segmentation using maximal cell strength updation in cellular automata.

Authors:  J Anitha; J Dinesh Peter
Journal:  Med Biol Eng Comput       Date:  2015-04-05       Impact factor: 2.602

3.  Global detection approach for clustered microcalcifications in mammograms using a deep learning network.

Authors:  Juan Wang; Robert M Nishikawa; Yongyi Yang
Journal:  J Med Imaging (Bellingham)       Date:  2017-04-22

4.  Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection.

Authors:  Kadayanallur Mahadevan Prabusankarlal; Palanisamy Thirumoorthy; Radhakrishnan Manavalan
Journal:  J Med Imaging (Bellingham)       Date:  2017-06-16

5.  [Future of mammography-based imaging].

Authors:  R Schulz-Wendtland; T Wittenberg; T Michel; A Hartmann; M W Beckmann; C Rauh; S M Jud; B Brehm; M Meier-Meitinger; G Anton; M Uder; P A Fasching
Journal:  Radiologe       Date:  2014-03       Impact factor: 0.635

6.  Quantitative texture analysis: robustness of radiomics across two digital mammography manufacturers' systems.

Authors:  Kayla R Mendel; Hui Li; Li Lan; Cathleen M Cahill; Victoria Rael; Hiroyuki Abe; Maryellen L Giger
Journal:  J Med Imaging (Bellingham)       Date:  2017-09-19

7.  Detection and Segmentation of Pectoral Muscle on MLO-View Mammogram Using Enhancement Filter.

Authors:  P S Vikhe; V R Thool
Journal:  J Med Syst       Date:  2017-10-25       Impact factor: 4.460

8.  A combined approach for the enhancement and segmentation of mammograms using modified fuzzy C-means method in wavelet domain.

Authors:  Subodh Srivastava; Neeraj Sharma; S K Singh; R Srivastava
Journal:  J Med Phys       Date:  2014-07

9.  Aberrant BLID expression is associated with breast cancer progression.

Authors:  Xiaoyan Li; Peng Su; Xianqiang Liu; Xiangnan Kong; Xin Zhang; Hongyu Zhang; Qifeng Yang
Journal:  Tumour Biol       Date:  2014-02-15

10.  CIMIDx: Prototype for a Cloud-Based System to Support Intelligent Medical Image Diagnosis With Efficiency.

Authors:  Selvaraj Rani Bhavani; Jagatheesan Senthilkumar; Arul Gnanaprakasam Chilambuchelvan; Dhanabalachandran Manjula; Ramasamy Krishnamoorthy; Arputharaj Kannan
Journal:  JMIR Med Inform       Date:  2015-03-27
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