Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection
| Autor: | Jiang Cheng, Rongbo Shen, Zhou Ke, Xiao Fen, Kezhou Yan, Chang Jia |
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| Rok vydání: | 2018 |
| Předmět: |
Databases
Factual Computer science Pectoral muscle Breast Neoplasms 02 engineering and technology Article Pectoralis Muscles 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Genetic algorithm 0202 electrical engineering electronic engineering information engineering medicine Humans Mammography Radiology Nuclear Medicine and imaging Segmentation Breast Diagnostic Errors Selection algorithm Selection (genetic algorithm) Radiological and Ultrasound Technology medicine.diagnostic_test business.industry Pattern recognition Computer Science Applications Curve fitting Radiographic Image Interpretation Computer-Assisted Female 020201 artificial intelligence & image processing Artificial intelligence False positive rate business Algorithms |
| Zdroj: | Journal of Digital Imaging. 31:680-691 |
| ISSN: | 1618-727X 0897-1889 |
| DOI: | 10.1007/s10278-018-0068-9 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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