Literature DB >> 16487954

Automated detection of masses in mammograms by local adaptive thresholding.

Guillaume Kom1, Alain Tiedeu, Martin Kom.   

Abstract

In this paper, an algorithm for detection of suspicious masses from mammographic images is presented. The proposed algorithm was tested on a database of 61 mammograms on which masses had previously been marked by experienced radiologists. Results show that the proposed method exhibits for mass detection, a sensitivity of 95.91%. The area under receiver operating characteristic (ROC) Az was 0.946 when enhancement of the original image was performed before detection and 0.938 otherwise. Furthermore in some cases, we could detect some masses that the radiologists were not able to mark out.

Mesh:

Year:  2006        PMID: 16487954     DOI: 10.1016/j.compbiomed.2005.12.004

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  9 in total

1.  Breast masses detection using phase portrait analysis and fuzzy inference systems.

Authors:  Arianna Mencattini; Marcello Salmeri
Journal:  Int J Comput Assist Radiol Surg       Date:  2011-10-11       Impact factor: 2.924

2.  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

3.  Detection of microcalcification clusters using Hessian matrix and foveal segmentation method on multiscale analysis in digital mammograms.

Authors:  Balakumaran Thangaraju; Ila Vennila; Gowrishankar Chinnasamy
Journal:  J Digit Imaging       Date:  2012-10       Impact factor: 4.056

4.  Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme.

Authors:  Maxine Tan; Jiantao Pu; Bin Zheng
Journal:  Phys Med Biol       Date:  2014-07-17       Impact factor: 3.609

5.  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

6.  A Novel Solution Based on Scale Invariant Feature Transform Descriptors and Deep Learning for the Detection of Suspicious Regions in Mammogram Images.

Authors:  Alessandro Bruno; Edoardo Ardizzone; Salvatore Vitabile; Massimo Midiri
Journal:  J Med Signals Sens       Date:  2020-07-03

7.  Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology.

Authors:  Hongyu Wang; Jun Feng; Qirong Bu; Feihong Liu; Min Zhang; Yu Ren; Yi Lv
Journal:  J Healthc Eng       Date:  2018-05-02       Impact factor: 2.682

8.  Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI.

Authors:  Han Jiao; Xinhua Jiang; Zhiyong Pang; Xiaofeng Lin; Yihua Huang; Li Li
Journal:  Comput Math Methods Med       Date:  2020-05-05       Impact factor: 2.238

9.  Breast Mass Detection in Mammography Based on Image Template Matching and CNN.

Authors:  Lilei Sun; Huijie Sun; Junqian Wang; Shuai Wu; Yong Zhao; Yong Xu
Journal:  Sensors (Basel)       Date:  2021-04-18       Impact factor: 3.576

  9 in total

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