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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞峰 | |
| dc.contributor.author | SZU-HSIEN LEE | en |
| dc.contributor.author | 李思賢 | zh_TW |
| dc.date.accessioned | 2021-06-08T01:14:53Z | - |
| dc.date.copyright | 2014-08-21 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-08-13 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/18612 | - |
| dc.description.abstract | 電腦輔助乳房腫瘤偵測系統可以有效率地輔助放射科醫生檢查乳房超音波影像，所以電腦輔助乳房腫瘤偵測系統被廣泛運用在乳房超音波影像的腫瘤偵測。為了發展出用於腫瘤偵測的電腦輔助乳房腫瘤偵測系統，我們採用條件隨機域模型對被放射科醫師用語意式標籤標示過的乳房超音波影像做像素分類。當腫瘤標籤的像素被決定後，我們使用孔洞填充和連通分量方法把腫瘤的候選區域組成同區域。為了減少預測區域的偽陽性，在訓練基於向量支持機器的腫瘤偵測器時，使用形態學和材料特徵來計算腫瘤候選區域為真實腫瘤的可能性。我們蒐集103個案(63個不正常個案和40個正常個案)去評估本實驗的效果。經由實驗結果，本方法達到腫瘤偵測率95%和每張圖平均有0.24個偽陽性個數。結合兩種特徵組合的品質因數(FOM)達到0.89，與其他現行方法相比時有顯著性差異(p-value < 0.05)。總結，實驗結果顯示我們提出的電腦輔助乳房腫瘤偵測系統可以有效地應用在臨床上。 | zh_TW |
| dc.description.abstract | Computer-aided detection systems (CADe) have been widely developed for the detection of lesions in breast ultrasound (BUS) images due to the capability and efficiency of aiding the radiologists in reviewing the breast ultrasonography. To develop a CADe system for lesion detection, we propose to adopt the conditional random fields (CRFs) model to perform the pixel classification task of BUS images with semantic labels described by experienced radiologists. Once the lesion label of the pixels can be determined, we perform hole-filling and connected component labeling to group the regions of lesion candidates. To reduce the false positives of suspected regions, morphological and textural features are used for the evaluation of the likelihood of the lesion candidate while training a lesion detector based on support vector machine (SVM). We collect 103 cases (63 for abnormal cases and 40 for normal cases) to evaluate the effectiveness of the proposed method. As the result, the proposed method achieves the sensitivity of 95% with false positive rate at 0.24 FPs/image. The figure of merit (FOM) of the combination of two feature sets is 0.89 which significantly outperforms the recent state-of-the-art methods (p-value < 0.05). In summary, the experiment results support our proposed system to be applied in the clinical use of computer-aided lesion detection. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T01:14:53Z (GMT). No. of bitstreams: 1 ntu-103-R01922103-1.pdf: 1421168 bytes, checksum: 4b9b58a98fffeb828a218a17a4c9f338 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 摘要 iii Abstract iv Table of Contents vi List of Figures vii List of Tables ix Chapter 1 Introduction 1 Chapter 2 Material 6 2.1 Patients and Lesion Characteristics 6 2.2 Data collection for 2-D breast sonogram 7 Chapter 3 The Proposed Method 9 3.1 Learning Graphical Model for Semantic Pixels Clustering 11 3.1.1 Annotation of Semantic Labels for Training BUS Images 12 3.1.2 CRFs Model Learning for Semantic Pixels Prediction 14 3.1.3 CRFs model Testing 16 3.2 Feature Extraction for Candidate Regions 18 3.2.1 Morphology Features 19 3.2.2 Texture Features 21 3.3 False-Positive Reduction using Lesion Detector 24 3.4 Detailed Implementation of State-of-the-art Approaches for Lesion Detection 26 3.5 Statistical Analysis 28 Chapter 4 Experimental Results and Discussion 30 4.1 Experimental Results 30 4.2 Discussion 43 Chapter 5 Conclusion and Future Works 46 References 48 | |
| dc.language.iso | en | |
| dc.title | 基於條件隨機域方法的語意式乳房超音波腫瘤偵測 | zh_TW |
| dc.title | Semantic Lesion Detection for Breast Ultrasound Based on Conditional Random Fields | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃俊升,張允中 | |
| dc.subject.keyword | 乳房腫瘤,電腦輔助偵測,條件隨機域, | zh_TW |
| dc.subject.keyword | breast lesion,computer-aided detection,conditional random fields, | en |
| dc.relation.page | 53 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2014-08-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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