Comparative Analysis on De-Noising of MRI Uterus Image for Identification of Endometrial Carcinoma
Purpose: The anatomical and physiological processes of the human body are pictured in radiology using different modalities. Magnetic Resonance Imaging (MRI) supports capturing the images of organs using magnetic field gradients. The quality of MR images is generally affected by various noises such...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tehran University of Medical Sciences
2023-06-01
|
| Series: | Frontiers in Biomedical Technologies |
| Subjects: | |
| Online Access: | https://fbt.tums.ac.ir/index.php/fbt/article/view/517 |
| _version_ | 1797742504945123328 |
|---|---|
| author | S Brindha Judith Justin |
| author_facet | S Brindha Judith Justin |
| author_sort | S Brindha |
| collection | DOAJ |
| description |
Purpose: The anatomical and physiological processes of the human body are pictured in radiology using different modalities. Magnetic Resonance Imaging (MRI) supports capturing the images of organs using magnetic field gradients. The quality of MR images is generally affected by various noises such as Gaussian, speckle, salt and pepper, Rayleigh, Rican etc. Removal of these noises from the MR images is essential for further diagnostic procedures.
Materials and Methods: In this article, Gaussian noise, speckle noise, and salt and pepper noise are added to the MR uterus image for which different filters are applied to remove the noise for precise identification of endometrial carcinoma.
Results: The different filters incorporated for the additive noise removal process are the bilateral filter, Non-Local Means (NLM) filter, anisotropic diffusion filter, and Convolution Neural Network (CNN). The efficiency of the filter is calculated by evaluating the response of the filter by gradually increasing the noise intensity of the MR images.
Conclusion: Further, peak Signal-to-Noise Ratio (SNR), structural similarity index measure, image quality index and computational cost parameters are computed and analyzed.
|
| first_indexed | 2024-03-12T14:41:46Z |
| format | Article |
| id | doaj.art-262b65c2083d4d43be5b8f70a572d219 |
| institution | Directory Open Access Journal |
| issn | 2345-5837 |
| language | English |
| last_indexed | 2024-03-12T14:41:46Z |
| publishDate | 2023-06-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Frontiers in Biomedical Technologies |
| spelling | doaj.art-262b65c2083d4d43be5b8f70a572d2192023-08-16T05:59:24ZengTehran University of Medical SciencesFrontiers in Biomedical Technologies2345-58372023-06-0110310.18502/fbt.v10i3.13159Comparative Analysis on De-Noising of MRI Uterus Image for Identification of Endometrial CarcinomaS Brindha0Judith Justin1Research Scholar, Department of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, IndiaDepartment of Biomedical Instrumentation Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India Purpose: The anatomical and physiological processes of the human body are pictured in radiology using different modalities. Magnetic Resonance Imaging (MRI) supports capturing the images of organs using magnetic field gradients. The quality of MR images is generally affected by various noises such as Gaussian, speckle, salt and pepper, Rayleigh, Rican etc. Removal of these noises from the MR images is essential for further diagnostic procedures. Materials and Methods: In this article, Gaussian noise, speckle noise, and salt and pepper noise are added to the MR uterus image for which different filters are applied to remove the noise for precise identification of endometrial carcinoma. Results: The different filters incorporated for the additive noise removal process are the bilateral filter, Non-Local Means (NLM) filter, anisotropic diffusion filter, and Convolution Neural Network (CNN). The efficiency of the filter is calculated by evaluating the response of the filter by gradually increasing the noise intensity of the MR images. Conclusion: Further, peak Signal-to-Noise Ratio (SNR), structural similarity index measure, image quality index and computational cost parameters are computed and analyzed. https://fbt.tums.ac.ir/index.php/fbt/article/view/517Endometrial CarcinomaAnisotropic DiffusionBilateral FilterNon-Local Means Filter |
| spellingShingle | S Brindha Judith Justin Comparative Analysis on De-Noising of MRI Uterus Image for Identification of Endometrial Carcinoma Frontiers in Biomedical Technologies Endometrial Carcinoma Anisotropic Diffusion Bilateral Filter Non-Local Means Filter |
| title | Comparative Analysis on De-Noising of MRI Uterus Image for Identification of Endometrial Carcinoma |
| title_full | Comparative Analysis on De-Noising of MRI Uterus Image for Identification of Endometrial Carcinoma |
| title_fullStr | Comparative Analysis on De-Noising of MRI Uterus Image for Identification of Endometrial Carcinoma |
| title_full_unstemmed | Comparative Analysis on De-Noising of MRI Uterus Image for Identification of Endometrial Carcinoma |
| title_short | Comparative Analysis on De-Noising of MRI Uterus Image for Identification of Endometrial Carcinoma |
| title_sort | comparative analysis on de noising of mri uterus image for identification of endometrial carcinoma |
| topic | Endometrial Carcinoma Anisotropic Diffusion Bilateral Filter Non-Local Means Filter |
| url | https://fbt.tums.ac.ir/index.php/fbt/article/view/517 |
| work_keys_str_mv | AT sbrindha comparativeanalysisondenoisingofmriuterusimageforidentificationofendometrialcarcinoma AT judithjustin comparativeanalysisondenoisingofmriuterusimageforidentificationofendometrialcarcinoma |