| Title |
Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection
|
|---|---|
| Published in |
Journal of Digital Imaging, March 2018
|
| DOI | 10.1007/s10278-018-0068-9 |
| Pubmed ID | |
| Authors |
Rongbo Shen, Kezhou Yan, Fen Xiao, Jia Chang, Cheng Jiang, Ke Zhou |
| Abstract |
In computer-aided diagnosis systems for breast mammography, the pectoral muscle region can easily cause a high false positive rate and misdiagnosis due to its similar texture and low contrast with breast parenchyma. Pectoral muscle region segmentation is a crucial pre-processing step to identify lesions, and accurate segmentation in poor-contrast mammograms is still a challenging task. In order to tackle this problem, a novel method is proposed to automatically segment pectoral muscle region in this paper. The proposed method combines genetic algorithm and morphological selection algorithm, incorporating four steps: pre-processing, genetic algorithm, morphological selection, and polynomial curve fitting. For the evaluation results on different databases, the proposed method achieves average FP rate and FN rate of 2.03 and 6.90% (mini MIAS), 1.60 and 4.03% (DDSM), and 2.42 and 13.61% (INBreast), respectively. The results can be comparable performance in various metrics over the state-of-the-art methods. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 39 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 23% |
| Other | 4 | 10% |
| Student > Doctoral Student | 2 | 5% |
| Researcher | 2 | 5% |
| Lecturer | 1 | 3% |
| Other | 5 | 13% |
| Unknown | 16 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 33% |
| Engineering | 4 | 10% |
| Neuroscience | 1 | 3% |
| Unspecified | 1 | 3% |
| Unknown | 20 | 51% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 April 2018.
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#20,483,282
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Outputs from Journal of Digital Imaging
#945
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Outputs of similar age
#291,747
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Outputs of similar age from Journal of Digital Imaging
#23
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