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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞峰(Ruey-Feng Chang) | |
| dc.contributor.author | Yu-Chiao Sun | en |
| dc.contributor.author | 孫羽喬 | zh_TW |
| dc.date.accessioned | 2021-06-07T17:52:30Z | - |
| dc.date.copyright | 2012-08-27 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-19 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15804 | - |
| dc.description.abstract | In recent years, the computer-aided diagnostic (CAD) is developed gradually providing second opinion for radiologists' diagnosis. The CAD system should produce well segmentation result of tumor to precisely evaluate the tumor size, and the tumor can be further classified into benign and malignant by the extracted feature of shape. Concerning the previous techniques of the semi-automatic segmentation (e.g., level-set), the seed usually needs to be manually initialized at the appropriate position for the better segmentation result. Besides, most of the segmentation systems consider the detection procedure as the preceding step, and the segmentation approach is subsequently employed on the located region of interest. In this study, a fully automatic system integrating the tumor detection and segmentation steps is proposed, and a set of representative seeds are computerized for the whole image segmentation based on the level-set method. Meanwhile, the novel multi-seed mechanism assists the level set segmentation in acquiring the satisfactory result. The proposed system consists of three phases. First, a mean-shift clustering method and the affinity approach are applied to generate and extract the most representative seeds. Next, the level-set method based on the selected seeds is employed to obtain several suspected regions. Finally, the features of all the suspected regions are calculated and further analyzed by support vector machine (SVM) classifier to extract the target tumor from the normal ones. 120 cases (68 for benign cases and 52 for malignant cases) are used for evaluating the proposed system. The sensitivity is 95% (114/120) with the benign cases equal to 0.91 and the malignant cases to 1.00. In summary, the experimental results demonstrate the high efficiency and robustness of the proposed method. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-07T17:52:30Z (GMT). No. of bitstreams: 1 ntu-101-R99945021-1.pdf: 9191008 bytes, checksum: 02046b7256b182c2b9b561f01716dd86 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 1 INTRODUCTION 1
2 MATERIALS 5 2.1 Patients and Lesion Characters . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3 PROPOSED METHODS 7 3.1 Seeds Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.1.1 Seed Generation via Mean-shift . . . . . . . . . . . . . . . . . . 10 3.1.2 Seeds elimination . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.3 Representative Seed Extraction via Affinity Propagation . . . . . 14 3.2 Suspected Tumor Segmentation . . . . . . . . . . . . . . . . . . . . . . 16 3.2.1 Sigmoid Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.2 Gradient Magnitude Filter . . . . . . . . . . . . . . . . . . . . . 18 3.2.3 Level Set Method . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2.4 Postprocessing with Opening Operator . . . . . . . . . . . . . . 20 3.3 Tumor Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.3.1 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.3.2 Support Vector Machine for Suspected Tumor Classification . . . 23 3.4 Performance Evaluation of Tumor Segmentation . . . . . . . . . . . . . 25 4 EXPERIMENTAL RESULTS AND DISCUSSION 27 4.1 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.1.1 Tumor Detection Performance . . . . . . . . . . . . . . . . . . . 28 4.1.2 Segmentation Performance . . . . . . . . . . . . . . . . . . . . . 30 4.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 5 CONCLUSION AND FUTURE WORK 41 REFERENCES 43 | |
| dc.language.iso | en | |
| dc.title | 基於Level-set方法的全自動乳房超音波影像腫瘤偵測與切割 | zh_TW |
| dc.title | Fully Automatic Tumor Detection and Segmentation Based on Level-set Method Using Breast Sonography | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張允中(Yun-Chung Chang),黃俊升(Chun-Sheng Huang) | |
| dc.subject.keyword | 電腦輔助診斷系統,全自動腫瘤偵測與切割, | zh_TW |
| dc.subject.keyword | computer-aided system,automatic tumor detection and segmentation, | en |
| dc.relation.page | 58 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2012-08-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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