Denoising histological images for a better colour image segmentation / Sulastri Putit
De-noising can be generally defined as removing noise in image. According to (Pierrick Coupe et.al, 2008), image de-noising is used to improve the accuracy of various image processing algorithms such as registration or segmentation. Generally, the quality of the artifact correction influences perfor...
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| Format: | Thesis |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/87238/1/87238.pdf |
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| Summary: | De-noising can be generally defined as removing noise in image. According to (Pierrick Coupe et.al, 2008), image de-noising is used to improve the accuracy of various image processing algorithms such as registration or segmentation. Generally, the quality of the artifact correction influences performance of the image processing procedure. So, the noise removal aims at improving the image quality for visual inspection. The preservation of relevant image information is important especially in medical context. The problem statements of the study have been defined as the problem of noise, the problem of colour image segmentation and the problem of conventional method in preparing histological images for analysis. In order to achieve better colour for image segmentation, the objectives of the study have to fulfill first which is to perform de-noising on colour histological images, then to perform colour image segmentation on histological images and finally to evaluate the effect of denoising on colour image segmentation. In this study, the de-noising concept for colour image preprocessing and segmentation are proposed since these two processes has been widely accepted as an important component of the image mining in order to enhance the images. |
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