Breast Tumor Segmentation on Ultrasonography Based on 3D Region Growing Method
碩士 === 東海大學 === 資訊工程學系 === 103 === Breast cancer is the most common cancer in the woman. There is an upward trend in the number of such cases in the past years. Early diagnosis and early treatment is the most effective way of reducing mortality caused by breast cancer. Ultrasound is the common exami...
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ndltd-TW-103THU003940112016-08-19T04:10:07Z http://ndltd.ncl.edu.tw/handle/89861886175712856976 Breast Tumor Segmentation on Ultrasonography Based on 3D Region Growing Method 基於區域成長法的3D超音波乳房腫瘤切割 Lin, Wan-Ting 林琬婷 碩士 東海大學 資訊工程學系 103 Breast cancer is the most common cancer in the woman. There is an upward trend in the number of such cases in the past years. Early diagnosis and early treatment is the most effective way of reducing mortality caused by breast cancer. Ultrasound is the common examination technology because of fast, cheap and noninvasive. The difficult part is that there are many noises and speckles on the ultrasonic images. Malignant and benign breast tumors are present different shape and size, thus this study propose a robust segmentation method to assist the physician on contouring tumor boundary. The proposed method first utilizes a pre-processing procedure to reduce the noises and speckle in imaging. After that, three-dimensional (3D) region growing method is applies to segment the tumor area. Finally, the proposed method made the area smoother and correctly though a post processing step. This study evaluated total of 30 tumor cases and four practical similarity measures (similarity index, overlap fraction, overlap value, and extraction fraction) are used to evaluate the result between the manually determined contours, an automatically method named virtual organ computer-aided analysis (VOCAL) and the proposed segmentation method. Huang, Yu-Len 黃育仁 2015 學位論文 ; thesis 38 en_US |
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碩士 === 東海大學 === 資訊工程學系 === 103 === Breast cancer is the most common cancer in the woman. There is an upward trend in the number of such cases in the past years. Early diagnosis and early treatment is the most effective way of reducing mortality caused by breast cancer. Ultrasound is the common examination technology because of fast, cheap and noninvasive. The difficult part is that there are many noises and speckles on the ultrasonic images. Malignant and benign breast tumors are present different shape and size, thus this study propose a robust segmentation method to assist the physician on contouring tumor boundary. The proposed method first utilizes a pre-processing procedure to reduce the noises and speckle in imaging. After that, three-dimensional (3D) region growing method is applies to segment the tumor area. Finally, the proposed method made the area smoother and correctly though a post processing step. This study evaluated total of 30 tumor cases and four practical similarity measures (similarity index, overlap fraction, overlap value, and extraction fraction) are used to evaluate the result between the manually determined contours, an automatically method named virtual organ computer-aided analysis (VOCAL) and the proposed segmentation method.
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author2 |
Huang, Yu-Len |
author_facet |
Huang, Yu-Len Lin, Wan-Ting 林琬婷 |
author |
Lin, Wan-Ting 林琬婷 |
spellingShingle |
Lin, Wan-Ting 林琬婷 Breast Tumor Segmentation on Ultrasonography Based on 3D Region Growing Method |
author_sort |
Lin, Wan-Ting |
title |
Breast Tumor Segmentation on Ultrasonography Based on 3D Region Growing Method |
title_short |
Breast Tumor Segmentation on Ultrasonography Based on 3D Region Growing Method |
title_full |
Breast Tumor Segmentation on Ultrasonography Based on 3D Region Growing Method |
title_fullStr |
Breast Tumor Segmentation on Ultrasonography Based on 3D Region Growing Method |
title_full_unstemmed |
Breast Tumor Segmentation on Ultrasonography Based on 3D Region Growing Method |
title_sort |
breast tumor segmentation on ultrasonography based on 3d region growing method |
publishDate |
2015 |
url |
http://ndltd.ncl.edu.tw/handle/89861886175712856976 |
work_keys_str_mv |
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