Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods
The correct segmentation of tumours can simplify formulate the diagnostic hypothesis, particularly in cases of irregular shapes, with fuzzy margins or spicules growing into the surrounding tissue, which are more likely to be malignant. In this study, the following active contour methods were used to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-11-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/9/11/277 |
| _version_ | 1798005626815643648 |
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| author | Marcin Ciecholewski |
| author_facet | Marcin Ciecholewski |
| author_sort | Marcin Ciecholewski |
| collection | DOAJ |
| description | The correct segmentation of tumours can simplify formulate the diagnostic hypothesis, particularly in cases of irregular shapes, with fuzzy margins or spicules growing into the surrounding tissue, which are more likely to be malignant. In this study, the following active contour methods were used to segment the masses: an edge–based active contour model using an inflation/deflation force with a damping coefficient (EM), a geometric active contour model (GAC) and an active contour without edges (ACWE). The preprocessing techniques presented in this publication are to reduce noise and at the same time amplify uniform areas of images in order to improve segmentation results. In addition, the use of image sampling by bicubic interpolation was tested to shorten the evolution time of active contour methods. The experiments used a test set composed of 100 cases taken from two publicly available databases: Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) database. The qualitative assessment concerned the ability to formulate an adequate diagnostic hypothesis and, for the individual methods (malignant and benign cases together), it amounted to at least: 81% (EM), 76% (GAC), and 69% (ACWE). The quantitative test consisted of measuring the following indexes: overlap value (OV) and extra fraction (EF). The OV of the segmentation for malignant and benign cases had the following average values: 0.81 ∓ 0.10 (EM), 0.79 ∓ 0.09 (GAC), 0.76 ∓ 0.18 (ACWE). The average values of the EF index, in turn, amounted to: 0.07 ∓ 0.06 (EM), 0.07 ∓ 0.05 (GAC) 0.34 ∓ 0.32 (ACWE). The qualitative and quantitative results obtained are the best for EM and are comparable or better than for other methods presented in the literature. |
| first_indexed | 2024-04-11T12:42:26Z |
| format | Article |
| id | doaj.art-c3c72bb9cf5648dbb38befdc88f2d266 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T12:42:26Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-c3c72bb9cf5648dbb38befdc88f2d2662022-12-22T04:23:27ZengMDPI AGSymmetry2073-89942017-11-0191127710.3390/sym9110277sym9110277Malignant and Benign Mass Segmentation in Mammograms Using Active Contour MethodsMarcin Ciecholewski0Institute of Informatics, Faculty of Mathematics, Physics and Informatics, University of Gdańsk, 80-308 Gdańsk, PolandThe correct segmentation of tumours can simplify formulate the diagnostic hypothesis, particularly in cases of irregular shapes, with fuzzy margins or spicules growing into the surrounding tissue, which are more likely to be malignant. In this study, the following active contour methods were used to segment the masses: an edge–based active contour model using an inflation/deflation force with a damping coefficient (EM), a geometric active contour model (GAC) and an active contour without edges (ACWE). The preprocessing techniques presented in this publication are to reduce noise and at the same time amplify uniform areas of images in order to improve segmentation results. In addition, the use of image sampling by bicubic interpolation was tested to shorten the evolution time of active contour methods. The experiments used a test set composed of 100 cases taken from two publicly available databases: Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) database. The qualitative assessment concerned the ability to formulate an adequate diagnostic hypothesis and, for the individual methods (malignant and benign cases together), it amounted to at least: 81% (EM), 76% (GAC), and 69% (ACWE). The quantitative test consisted of measuring the following indexes: overlap value (OV) and extra fraction (EF). The OV of the segmentation for malignant and benign cases had the following average values: 0.81 ∓ 0.10 (EM), 0.79 ∓ 0.09 (GAC), 0.76 ∓ 0.18 (ACWE). The average values of the EF index, in turn, amounted to: 0.07 ∓ 0.06 (EM), 0.07 ∓ 0.05 (GAC) 0.34 ∓ 0.32 (ACWE). The qualitative and quantitative results obtained are the best for EM and are comparable or better than for other methods presented in the literature.https://www.mdpi.com/2073-8994/9/11/277active contouredge–based active contourregion–based active contourimage processingsegmentationmassesbreast cancermammography |
| spellingShingle | Marcin Ciecholewski Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods Symmetry active contour edge–based active contour region–based active contour image processing segmentation masses breast cancer mammography |
| title | Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods |
| title_full | Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods |
| title_fullStr | Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods |
| title_full_unstemmed | Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods |
| title_short | Malignant and Benign Mass Segmentation in Mammograms Using Active Contour Methods |
| title_sort | malignant and benign mass segmentation in mammograms using active contour methods |
| topic | active contour edge–based active contour region–based active contour image processing segmentation masses breast cancer mammography |
| url | https://www.mdpi.com/2073-8994/9/11/277 |
| work_keys_str_mv | AT marcinciecholewski malignantandbenignmasssegmentationinmammogramsusingactivecontourmethods |