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: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9139455/ |
| _version_ | 1818727514070056960 |
|---|---|
| author | Siti Fairuz Mat Radzi Muhammad Khalis Abdul Karim M Iqbal Saripan Mohd Amiruddin Abd Rahman Nurul Huda Osman Entesar Zawam Dalah Noramaliza Mohd Noor |
| author_facet | Siti Fairuz Mat Radzi Muhammad Khalis Abdul Karim M Iqbal Saripan Mohd Amiruddin Abd Rahman Nurul Huda Osman Entesar Zawam Dalah Noramaliza Mohd Noor |
| author_sort | Siti Fairuz Mat Radzi |
| collection | DOAJ |
| 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 | 2024-12-17T22:15:18Z |
| format | Article |
| id | doaj.art-c1ed2bed57df44a1ba93e7d44bb6919b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T22:15:18Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c1ed2bed57df44a1ba93e7d44bb6919b2022-12-21T21:30:38ZengIEEEIEEE Access2169-35362020-01-01812772012773110.1109/ACCESS.2020.30089279139455Impact of Image Contrast Enhancement on Stability of Radiomics Feature Quantification on a 2D Mammogram RadiographSiti Fairuz Mat Radzi0https://orcid.org/0000-0003-2141-7799Muhammad Khalis Abdul Karim1https://orcid.org/0000-0002-5357-4193M Iqbal Saripan2https://orcid.org/0000-0002-3005-5331Mohd Amiruddin Abd Rahman3https://orcid.org/0000-0003-0951-9450Nurul Huda Osman4Entesar Zawam Dalah5Noramaliza Mohd Noor6https://orcid.org/0000-0001-8419-5383Department of Physics, Faculty of Science, Universiti Putra Malaysia, Selangor, MalaysiaDepartment of Physics, Faculty of Science, Universiti Putra Malaysia, Selangor, MalaysiaDepartment of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Selangor, MalaysiaDepartment of Physics, Faculty of Science, Universiti Putra Malaysia, Selangor, MalaysiaDepartment of Physics, Faculty of Science, Universiti Putra Malaysia, Selangor, MalaysiaClinical Support Service and Nursing Sector, Dubai Health Authority, Dubai, United Arab EmiratesDepartment of Radiology, Faculty of Medicine, Universiti Putra Malaysia, Selangor, MalaysiaThe 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.https://ieeexplore.ieee.org/document/9139455/Breast cancerradiomicscontrast limited adaptive histogram equalization (CLAHE)adaptive histogram equalization (AHE)semiautomatic segmentation |
| spellingShingle | Siti Fairuz Mat Radzi Muhammad Khalis Abdul Karim M Iqbal Saripan Mohd Amiruddin Abd Rahman Nurul Huda Osman Entesar Zawam Dalah Noramaliza Mohd Noor Impact of Image Contrast Enhancement on Stability of Radiomics Feature Quantification on a 2D Mammogram Radiograph IEEE Access Breast cancer radiomics contrast limited adaptive histogram equalization (CLAHE) adaptive histogram equalization (AHE) semiautomatic segmentation |
| 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 |
| topic | Breast cancer radiomics contrast limited adaptive histogram equalization (CLAHE) adaptive histogram equalization (AHE) semiautomatic segmentation |
| url | https://ieeexplore.ieee.org/document/9139455/ |
| work_keys_str_mv | AT sitifairuzmatradzi impactofimagecontrastenhancementonstabilityofradiomicsfeaturequantificationona2dmammogramradiograph AT muhammadkhalisabdulkarim impactofimagecontrastenhancementonstabilityofradiomicsfeaturequantificationona2dmammogramradiograph AT miqbalsaripan impactofimagecontrastenhancementonstabilityofradiomicsfeaturequantificationona2dmammogramradiograph AT mohdamiruddinabdrahman impactofimagecontrastenhancementonstabilityofradiomicsfeaturequantificationona2dmammogramradiograph AT nurulhudaosman impactofimagecontrastenhancementonstabilityofradiomicsfeaturequantificationona2dmammogramradiograph AT entesarzawamdalah impactofimagecontrastenhancementonstabilityofradiomicsfeaturequantificationona2dmammogramradiograph AT noramalizamohdnoor impactofimagecontrastenhancementonstabilityofradiomicsfeaturequantificationona2dmammogramradiograph |