Image processing algorithms for medical applications : B
In order to reduce the amount of noise in an image, image processing algorithms need to be carried out. This is an important procedure as image processing may result in noise corruptions either during transmission, acquisition or conversion. Such noise corruptions diminish the images’ visual quality...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2019
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| Online Access: | http://hdl.handle.net/10356/78177 |
| _version_ | 1811692912526753792 |
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| author | Muhammad Shamir Kamaluddin |
| author2 | Mohammed Yakoob Siyal |
| author_facet | Mohammed Yakoob Siyal Muhammad Shamir Kamaluddin |
| author_sort | Muhammad Shamir Kamaluddin |
| collection | NTU |
| description | In order to reduce the amount of noise in an image, image processing algorithms need to be carried out. This is an important procedure as image processing may result in noise corruptions either during transmission, acquisition or conversion. Such noise corruptions diminish the images’ visual quality and could also cause inaccuracy in the details of the image. Hence, there is a need for image processing algorithms, which restores an image such that the image’s visual quality is improved, producing a more precise details of the subject. In the field of medicine, in particular, having an accurate depiction and clear medical image is extremely crucial as doctors rely on medical images to analyze a patient’s internal structure and to diagnose accordingly. Should there be a lack of clarity in the image, it could potentially lead to inaccurate analysis and improper diagnosis. In this thesis, noise removal will be done using eight filters: Mean, Median, Gaussian, Wiener, Guided, Non-local mean, Diffuse and Bilateral filter. The efficacy of each filter will be analyzed using Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Image Enhancing Factor (IEF). Each of the eight filters will be applied to four types of medical images namely, X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Ultrasound. These four types of medical images will be corrupted separately with eight different types of noises namely, Poisson, Gaussian, Speckle, Rayleigh, Uniform, Impulse, Local Variance and Salt and Pepper noise. The noise intensity of each type of noise will be varied from 25% to 70% using an interval of 15% when corrupting he medical images. The tests will be carried out in two types of image formats which are greyscale format and RGB format. In order to ensure that the results prove the filter’s capabilities, the experiment was carried out on a sample size of 200 medical images – 50 samples for each type of medical image. By analyzing the average performance of each filter on each medical image corrupted by a certain type of noise at varying noise intensity and testing them on two different image formats, a conclusion was derived on the type of filter that was most effective in denoising an image and most time efficient under each circumstance. |
| first_indexed | 2024-10-01T06:43:19Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/78177 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:43:19Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/781772023-07-07T16:31:08Z Image processing algorithms for medical applications : B Muhammad Shamir Kamaluddin Mohammed Yakoob Siyal School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision In order to reduce the amount of noise in an image, image processing algorithms need to be carried out. This is an important procedure as image processing may result in noise corruptions either during transmission, acquisition or conversion. Such noise corruptions diminish the images’ visual quality and could also cause inaccuracy in the details of the image. Hence, there is a need for image processing algorithms, which restores an image such that the image’s visual quality is improved, producing a more precise details of the subject. In the field of medicine, in particular, having an accurate depiction and clear medical image is extremely crucial as doctors rely on medical images to analyze a patient’s internal structure and to diagnose accordingly. Should there be a lack of clarity in the image, it could potentially lead to inaccurate analysis and improper diagnosis. In this thesis, noise removal will be done using eight filters: Mean, Median, Gaussian, Wiener, Guided, Non-local mean, Diffuse and Bilateral filter. The efficacy of each filter will be analyzed using Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Image Enhancing Factor (IEF). Each of the eight filters will be applied to four types of medical images namely, X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Ultrasound. These four types of medical images will be corrupted separately with eight different types of noises namely, Poisson, Gaussian, Speckle, Rayleigh, Uniform, Impulse, Local Variance and Salt and Pepper noise. The noise intensity of each type of noise will be varied from 25% to 70% using an interval of 15% when corrupting he medical images. The tests will be carried out in two types of image formats which are greyscale format and RGB format. In order to ensure that the results prove the filter’s capabilities, the experiment was carried out on a sample size of 200 medical images – 50 samples for each type of medical image. By analyzing the average performance of each filter on each medical image corrupted by a certain type of noise at varying noise intensity and testing them on two different image formats, a conclusion was derived on the type of filter that was most effective in denoising an image and most time efficient under each circumstance. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-13T02:31:09Z 2019-06-13T02:31:09Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/78177 en Nanyang Technological University 158 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Muhammad Shamir Kamaluddin Image processing algorithms for medical applications : B |
| title | Image processing algorithms for medical applications : B |
| title_full | Image processing algorithms for medical applications : B |
| title_fullStr | Image processing algorithms for medical applications : B |
| title_full_unstemmed | Image processing algorithms for medical applications : B |
| title_short | Image processing algorithms for medical applications : B |
| title_sort | image processing algorithms for medical applications b |
| topic | DRNTU::Engineering::Electrical and electronic engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision |
| url | http://hdl.handle.net/10356/78177 |
| work_keys_str_mv | AT muhammadshamirkamaluddin imageprocessingalgorithmsformedicalapplicationsb |