Detection of calcifications in digital breast tomosynthesis images using a new approach of image filtering / Syafiqah Aqilah Saifudin

Breast cancer is the world's second leading cause of death among cancers primarily affecting women. Calcifications are the most important subject in the early stages of breast cancer detection; thus, calcification detection is important because it leads to early treatment and increases the surv...

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Main Author:	Saifudin, Syafiqah Aqilah
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Research Experimentation
Online Access:	https://ir.uitm.edu.my/id/eprint/88629/1/88629.pdf
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id	my-uitm-ir.88629
record_format	uketd_dc
spelling	my-uitm-ir.886292024-01-16T07:49:39Z Detection of calcifications in digital breast tomosynthesis images using a new approach of image filtering / Syafiqah Aqilah Saifudin 2023 Saifudin, Syafiqah Aqilah Research. Experimentation Breast cancer is the world's second leading cause of death among cancers primarily affecting women. Calcifications are the most important subject in the early stages of breast cancer detection; thus, calcification detection is important because it leads to early treatment and increases the survival rate for breast cancer patients. Digital Breast Tomosynthesis (DBT) is the screening technique that move the device to specific angles and produced images that have been shown to improve the overlapping issue in mammogram. This is important to obtain a clearer view of calcifications. However, due to movement of device to a specific angle as well as patients' movement, blurry artefact and noises might unavoidably produce. The primary goal of this research is to propose a calcifications detection system based on convolutional neural networks (CNN) while employing a novel approach to image filtering techniques on DBT images. To overcome the unsatisfied quality of the images, this study proposed new filtering techniques for DBT image enhancement. The new filtering techniques are adaptations of Non-Linear Unsharp Masking (NLUM) and Fuzzy Weighted Median Filter (FWMF) to enhance DBT images and calcifications. Furthermore, the improved DBT images will be fed into the CNN model adapted from the previous study. Following that, the performance of both the enhancement stage and the detection process is evaluated, and finally, the radiologist performs the validation process. The proposed enhancement techniques out performed conventional filtering techniques for both NLUM and FWMF, which produced average PSNR values of 66.39915 and 96.226764, respectively. The newly proposed Adaptive FWMF can be used to enhance calcifications, but modified NLUM can be used to enhance the entire DBT image, including the clear form of breast glands, breast fat, and ligament, according to qualitative results. The filtering techniques are then applied to the CNN model, and the mean Average Precision (mAP) and Log Average Miss Rate (LAMR) values are recorded in the results. The outcome of this study demonstrated the capability and reliability of the proposed methods particularly for radiologists, of overcoming the problems and limitations associated with the diagnosis of breast cancer disease, both in terms of the visibility of calcifications and the detection process of calcifications. 2023 Thesis https://ir.uitm.edu.my/id/eprint/88629/ https://ir.uitm.edu.my/id/eprint/88629/1/88629.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Sulaiman, Siti Noraini (Assoc Prof Ir. Ts. Dr.)
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Sulaiman, Siti Noraini (Assoc Prof Ir. Ts. Dr.)
topic	Research Experimentation
spellingShingle	Research Experimentation Saifudin, Syafiqah Aqilah Detection of calcifications in digital breast tomosynthesis images using a new approach of image filtering / Syafiqah Aqilah Saifudin
description	Breast cancer is the world's second leading cause of death among cancers primarily affecting women. Calcifications are the most important subject in the early stages of breast cancer detection; thus, calcification detection is important because it leads to early treatment and increases the survival rate for breast cancer patients. Digital Breast Tomosynthesis (DBT) is the screening technique that move the device to specific angles and produced images that have been shown to improve the overlapping issue in mammogram. This is important to obtain a clearer view of calcifications. However, due to movement of device to a specific angle as well as patients' movement, blurry artefact and noises might unavoidably produce. The primary goal of this research is to propose a calcifications detection system based on convolutional neural networks (CNN) while employing a novel approach to image filtering techniques on DBT images. To overcome the unsatisfied quality of the images, this study proposed new filtering techniques for DBT image enhancement. The new filtering techniques are adaptations of Non-Linear Unsharp Masking (NLUM) and Fuzzy Weighted Median Filter (FWMF) to enhance DBT images and calcifications. Furthermore, the improved DBT images will be fed into the CNN model adapted from the previous study. Following that, the performance of both the enhancement stage and the detection process is evaluated, and finally, the radiologist performs the validation process. The proposed enhancement techniques out performed conventional filtering techniques for both NLUM and FWMF, which produced average PSNR values of 66.39915 and 96.226764, respectively. The newly proposed Adaptive FWMF can be used to enhance calcifications, but modified NLUM can be used to enhance the entire DBT image, including the clear form of breast glands, breast fat, and ligament, according to qualitative results. The filtering techniques are then applied to the CNN model, and the mean Average Precision (mAP) and Log Average Miss Rate (LAMR) values are recorded in the results. The outcome of this study demonstrated the capability and reliability of the proposed methods particularly for radiologists, of overcoming the problems and limitations associated with the diagnosis of breast cancer disease, both in terms of the visibility of calcifications and the detection process of calcifications.
format	Thesis
qualification_level	Master's degree
author	Saifudin, Syafiqah Aqilah
author_facet	Saifudin, Syafiqah Aqilah
author_sort	Saifudin, Syafiqah Aqilah
title	Detection of calcifications in digital breast tomosynthesis images using a new approach of image filtering / Syafiqah Aqilah Saifudin
title_short	Detection of calcifications in digital breast tomosynthesis images using a new approach of image filtering / Syafiqah Aqilah Saifudin
title_full	Detection of calcifications in digital breast tomosynthesis images using a new approach of image filtering / Syafiqah Aqilah Saifudin
title_fullStr	Detection of calcifications in digital breast tomosynthesis images using a new approach of image filtering / Syafiqah Aqilah Saifudin
title_full_unstemmed	Detection of calcifications in digital breast tomosynthesis images using a new approach of image filtering / Syafiqah Aqilah Saifudin
title_sort	detection of calcifications in digital breast tomosynthesis images using a new approach of image filtering / syafiqah aqilah saifudin
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/88629/1/88629.pdf
_version_	1794192134611402752

Detection of calcifications in digital breast tomosynthesis images using a new approach of image filtering / Syafiqah Aqilah Saifudin

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