Impact of image contrast enhancement on stability of radiomics feature quantification on a 2D mammogram radiograph
The present work aimed to evaluate the reproducibility of radiomics features derived from manual delineation and semiautomatic segmentation after enhancement using the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Histogram Equalization (AHE) techniques on a benign tumor of t...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/89287/ http://psasir.upm.edu.my/id/eprint/89287/1/CANCER.pdf |
| _version_ | 1848860816568221696 |
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| author | Mat Radzi, Siti Fairuz Abdul Karim, Muhammad Khalis Saripan, M. Iqbal Abd Rahman, Mohd Amiruddin Osman, Nurul Huda Dalah, Entesar Zawam Mohd Noor, Noramaliza |
| author_facet | Mat Radzi, Siti Fairuz Abdul Karim, Muhammad Khalis Saripan, M. Iqbal Abd Rahman, Mohd Amiruddin Osman, Nurul Huda Dalah, Entesar Zawam Mohd Noor, Noramaliza |
| author_sort | Mat Radzi, Siti Fairuz |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The present work aimed to evaluate the reproducibility of radiomics features derived from manual delineation and semiautomatic segmentation after enhancement using the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Histogram Equalization (AHE) techniques on a benign tumor of two-dimensional (2D) mammography images. Thirty mammogram images with known benign tumors were obtained from The Cancer Imaging Archive (TCIA) datasets and were randomly selected
as subjects. The samples were enhanced for semiautomatic segmentation sets using the Active Contour Model in MATLAB 2019a before analysis by two independent observers. Meanwhile, the images without any enhancement were segmented manually. The samples were divided into three categories: (1) CLAHE
images, (2) AHE images, and (3) manual segmented images. Radiomics features were extracted using algorithms provided by MATLAB 2019a software and were assessed with a reliable intra-class correlation coefficient (ICC) score. Radiomics features for the CLAHE group (ICC = 0.890 ± 0.554, p < 0.05) had the highest reproducibility compared to the features extracted from the AHE group (ICC = 0.850 ± 0.933, p < 0.05) and manual delineation (ICC = 0.673 ± 0.807, p > 0.05). Features in all three categories were
more robust for the CLAHE compared to the AHE and manual groups. This study shows the existence in variation for the radiomics features extracted from tumor region that are segmented using various image enhancement techniques. Semiautomatic segmentation with image enhancement using CLAHE algorithm
gave the best result and was a better alternative than manual delineation as the first two techniques yielded reproducible descriptors. This method should be applicable for predicting outcomes in patient with breast cancer. |
| first_indexed | 2025-11-15T12:51:15Z |
| format | Article |
| id | upm-89287 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:51:15Z |
| publishDate | 2020 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-892872021-09-03T05:29:08Z http://psasir.upm.edu.my/id/eprint/89287/ Impact of image contrast enhancement on stability of radiomics feature quantification on a 2D mammogram radiograph Mat Radzi, Siti Fairuz Abdul Karim, Muhammad Khalis Saripan, M. Iqbal Abd Rahman, Mohd Amiruddin Osman, Nurul Huda Dalah, Entesar Zawam Mohd Noor, Noramaliza The present work aimed to evaluate the reproducibility of radiomics features derived from manual delineation and semiautomatic segmentation after enhancement using the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Histogram Equalization (AHE) techniques on a benign tumor of two-dimensional (2D) mammography images. Thirty mammogram images with known benign tumors were obtained from The Cancer Imaging Archive (TCIA) datasets and were randomly selected as subjects. The samples were enhanced for semiautomatic segmentation sets using the Active Contour Model in MATLAB 2019a before analysis by two independent observers. Meanwhile, the images without any enhancement were segmented manually. The samples were divided into three categories: (1) CLAHE images, (2) AHE images, and (3) manual segmented images. Radiomics features were extracted using algorithms provided by MATLAB 2019a software and were assessed with a reliable intra-class correlation coefficient (ICC) score. Radiomics features for the CLAHE group (ICC = 0.890 ± 0.554, p < 0.05) had the highest reproducibility compared to the features extracted from the AHE group (ICC = 0.850 ± 0.933, p < 0.05) and manual delineation (ICC = 0.673 ± 0.807, p > 0.05). Features in all three categories were more robust for the CLAHE compared to the AHE and manual groups. This study shows the existence in variation for the radiomics features extracted from tumor region that are segmented using various image enhancement techniques. Semiautomatic segmentation with image enhancement using CLAHE algorithm gave the best result and was a better alternative than manual delineation as the first two techniques yielded reproducible descriptors. This method should be applicable for predicting outcomes in patient with breast cancer. Institute of Electrical and Electronics Engineers 2020 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/89287/1/CANCER.pdf Mat Radzi, Siti Fairuz and Abdul Karim, Muhammad Khalis and Saripan, M. Iqbal and Abd Rahman, Mohd Amiruddin and Osman, Nurul Huda and Dalah, Entesar Zawam and Mohd Noor, Noramaliza (2020) Impact of image contrast enhancement on stability of radiomics feature quantification on a 2D mammogram radiograph. IEEE Access, 8. art. no. 127730. pp. 1-12. ISSN 2169-3536 https://ieeexplore.ieee.org/abstract/document/9139455/authors#authors 10.1109/ACCESS.2020.3008927 |
| spellingShingle | Mat Radzi, Siti Fairuz Abdul Karim, Muhammad Khalis Saripan, M. Iqbal Abd Rahman, Mohd Amiruddin Osman, Nurul Huda Dalah, Entesar Zawam Mohd Noor, Noramaliza Impact of image contrast enhancement on stability of radiomics feature quantification on a 2D mammogram radiograph |
| title | Impact of image contrast enhancement on stability of radiomics feature quantification on a 2D mammogram radiograph |
| title_full | Impact of image contrast enhancement on stability of radiomics feature quantification on a 2D mammogram radiograph |
| title_fullStr | Impact of image contrast enhancement on stability of radiomics feature quantification on a 2D mammogram radiograph |
| title_full_unstemmed | Impact of image contrast enhancement on stability of radiomics feature quantification on a 2D mammogram radiograph |
| title_short | Impact of image contrast enhancement on stability of radiomics feature quantification on a 2D mammogram radiograph |
| title_sort | impact of image contrast enhancement on stability of radiomics feature quantification on a 2d mammogram radiograph |
| url | http://psasir.upm.edu.my/id/eprint/89287/ http://psasir.upm.edu.my/id/eprint/89287/ http://psasir.upm.edu.my/id/eprint/89287/ http://psasir.upm.edu.my/id/eprint/89287/1/CANCER.pdf |