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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...

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Main Authors: Mat Radzi, Siti Fairuz, Abdul Karim, Muhammad Khalis, Saripan, M. Iqbal, Abd Rahman, Mohd Amiruddin, Osman, Nurul Huda, Dalah, Entesar Zawam, Mohd Noor, Noramaliza
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers 2020
Online Access:http://psasir.upm.edu.my/id/eprint/89287/1/CANCER.pdf
http://psasir.upm.edu.my/id/eprint/89287/
https://ieeexplore.ieee.org/abstract/document/9139455/authors#authors
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spelling my.upm.eprints.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
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
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.
format Article
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
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
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
title 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_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_sort impact of image contrast enhancement on stability of radiomics feature quantification on a 2d mammogram radiograph
publisher Institute of Electrical and Electronics Engineers
publishDate 2020
url http://psasir.upm.edu.my/id/eprint/89287/1/CANCER.pdf
http://psasir.upm.edu.my/id/eprint/89287/
https://ieeexplore.ieee.org/abstract/document/9139455/authors#authors
_version_ 1710677177722208256
score 13.211869

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