Intelligent-Based Algorithm in Contour Extraction of Ultrasound Image
碩士 === 元智大學 === 電機工程學系 === 94 === Automatically detecting tumors and extracting lesion boundaries in ultrasound images are challenging tasks due to the variance in shape and the interference from speckle noise. In this study, we present an adaptive-fuzzy-logic-filter based algorithm (AFLFBA) and a s...
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ndltd-TW-094YZU054420222016-06-01T04:21:08Z http://ndltd.ncl.edu.tw/handle/83301421725514710444 Intelligent-Based Algorithm in Contour Extraction of Ultrasound Image 智慧型演算法於超音波影像輪廓圈選 Wei-Che Fan 范偉哲 碩士 元智大學 電機工程學系 94 Automatically detecting tumors and extracting lesion boundaries in ultrasound images are challenging tasks due to the variance in shape and the interference from speckle noise. In this study, we present an adaptive-fuzzy-logic-filter based algorithm (AFLFBA) and a support-vector-machine based algorithm (SVMBA). In AFLFBA, the adaptive fuzzy logic filter (AFLF) was utilized to suppress speckle noise and enhance the lesion boundary features effectively. The SVMBA was proposed to process the low-contrast and also tiny lesions. Each pixel within an ultrasound image is classified as being either a lesion or speckle noise according to the SVM model. In the following, we can use image processing techniques such as adaptive thresholding, connected component, and disk expansion to extract the significant lesions. The experimental results show that the average of the true positive area overlap between the designed contour and the contour obtained by the AFLFBA and SVMBA are higher than 92%. The analyzed results of the clinical images show that the extracted lesions contour were close to the experienced clinician''s manual delineation. Furthermore, based on the iterative design, these algorithms can be performed for multiple lesion extraction in a single image successfully. Chih-Min Lin 林志民 2006 學位論文 ; thesis 69 en_US |
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碩士 === 元智大學 === 電機工程學系 === 94 === Automatically detecting tumors and extracting lesion boundaries in ultrasound images are challenging tasks due to the variance in shape and the interference from speckle noise. In this study, we present an adaptive-fuzzy-logic-filter based algorithm (AFLFBA) and a support-vector-machine based algorithm (SVMBA).
In AFLFBA, the adaptive fuzzy logic filter (AFLF) was utilized to suppress speckle noise and enhance the lesion boundary features effectively. The SVMBA was proposed to process the low-contrast and also tiny lesions. Each pixel within an ultrasound image is classified as being either a lesion or speckle noise according to the SVM model. In the following, we can use image processing techniques such as adaptive thresholding, connected component, and disk expansion to extract the significant lesions. The experimental results show that the average of the true positive area overlap between the designed contour and the contour obtained by the AFLFBA and SVMBA are higher than 92%. The analyzed results of the clinical images show that the extracted lesions contour were close to the experienced clinician''s manual delineation. Furthermore, based on the iterative design, these algorithms can be performed for multiple lesion extraction in a single image successfully.
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author2 |
Chih-Min Lin |
author_facet |
Chih-Min Lin Wei-Che Fan 范偉哲 |
author |
Wei-Che Fan 范偉哲 |
spellingShingle |
Wei-Che Fan 范偉哲 Intelligent-Based Algorithm in Contour Extraction of Ultrasound Image |
author_sort |
Wei-Che Fan |
title |
Intelligent-Based Algorithm in Contour Extraction of Ultrasound Image |
title_short |
Intelligent-Based Algorithm in Contour Extraction of Ultrasound Image |
title_full |
Intelligent-Based Algorithm in Contour Extraction of Ultrasound Image |
title_fullStr |
Intelligent-Based Algorithm in Contour Extraction of Ultrasound Image |
title_full_unstemmed |
Intelligent-Based Algorithm in Contour Extraction of Ultrasound Image |
title_sort |
intelligent-based algorithm in contour extraction of ultrasound image |
publishDate |
2006 |
url |
http://ndltd.ncl.edu.tw/handle/83301421725514710444 |
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