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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/26512Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 陳中明 | |
| dc.contributor.author | Hsin-Yu Hsieh | en |
| dc.contributor.author | 謝欣郁 | zh_TW |
| dc.date.accessioned | 2021-06-08T07:13:09Z | - |
| dc.date.copyright | 2008-08-05 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-07-29 | |
| dc.identifier.citation | 參考文獻
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/26512 | - |
| dc.description.abstract | 乳癌已連續八年居於台灣女性惡性腫瘤死因第四位，惟有早期發現早期治療能提升治癒率。目前常用的乳癌篩檢工具為乳房X光攝影及乳房超音波影像，然而這兩種方法有空間解析度與敏感度不足的問題，無法偵測極早期的乳癌；雖然乳房磁振造影與正子斷層掃描都具有早期偵測乳癌的潛力，但單次檢測價格昂貴使其無法用於第一線的乳癌早期偵測。因此本研究發展了雙波段紅外線影像（DS-IR spectrogram），希望評估此被動式醫學影像技術用於偵測乳癌的效能。
DS-IR spectrogram是由美國海軍研究院斯華齡教授所提出的嶄新概念，其獨特能力來自一突破性的想法：以紅外線光譜比值（DS-IR spectral ratio）作為區分癌細胞與正常細胞的指標。由於癌細胞生長時多半會有血管增生現象，使其溫度高於正常細胞；根據普朗克輻射定律與維恩位移定律，溫度上升時，光譜高峰會由長波往中波移動，且中波紅外線（MIR，波長為3-5μm）的增加量會比長波紅外線（LIR，波長為8-12μm）要來得多，故只要癌細胞釋放出MIR跟LIR照相機可以偵測到的MIR跟LIR光子，就能藉由DS-IR spectral ratio與時俱增的傾向作為乳癌特徵。由此可知DS-IR spectrogram並非單憑體表溫度的改變去決定病灶位置，能提供比傳統紅外線熱影像更可靠的資訊。 獲得DS-IR spectrogram的主要步驟如下：首先是不同波段的紅外線影像對位，本研究使用了線性的仿射轉換跟非線性的薄板仿樣法進行對位。其次，根據對位後的MIR與LIR影像，以核心演算法（Deterministic neighborhood-based BSS algorithm）計算出每一像素隱含的惡性腫瘤比例，藉此偵測乳癌。 為了評估DS-IR spectrogram偵測乳癌的效能，本研究使用一台MIR照相機（波長為3-5μm）跟一台LIR照相機（波長為8-9.2μm）建構出第一套雙波段紅外線乳房攝影系統，自2007年7月起於台大醫院公館院區進行大型臨床試驗，並從招收到的樣本中選出100個進行分析。實驗結果顯示DS-IR spectrogram具有偵測乳癌的潛力，惟需更清楚掌握其隱含資訊，未來將有機會以此被動式的醫學影像系統偵測乳癌。 | zh_TW |
| dc.description.abstract | Breast cancer has been ranked the fourth leading cause of cancer deaths for females in Taiwan for the past eight years. Common imaging modalities used to detect breast cancers at present are mammogram and breast sonography. However, they both do not have a sufficient spatial resolution and are not sensitive enough to detect breast cancers. Although the MRI and the PET have the potential for early detection of breast cancers, they are both very costly and not usually used as the first-line defense. Therefore, this thesis proposes to develop a passive medical imaging modality, called dual-spectrum IR (DS-IR) spectrogram, for detection of malign tumors in breast.
The DS-IR spectrogram was invented by Dr. Harold Szu. The unique capability of the DS-IR spectrogram comes from using spectral ratio as the signature to distinguish cancer cells from normal ones. Since the cancer cells tend to have higher temperatures than the normal cells because of the angiogenesis process involved in the growth of cancer cells, the spectrum peak up-shifts from long wavelength infrared toward medium wavelength infrared due to Planck radiation physics and Wien’s displacement law. As long as there were a few amount of MIR photons emitting from the malign tumor cells and detected by the dual IR cameras, one may identify the malign tumor cells by using the trend of DS-IR spectral ratio over time as the feature. Two essential steps are involved in deriving the DS-IR spectrograms. One is registration of the MIR and LIR images accomplished by using linear affine transformation and nonlinear thin-plate spline. The other step is detection of the breast cancers based on the registered MIR and LIR images. Deterministic neighborhood-based BSS algorithm proposed by Dr. Harold Szu is realized to derive the probability distributions of cancer cells and normal cells. To evaluate the performance of the DS-IR imaging system, we have built the first clinical DS-IR imaging system using one MIR(3~5 | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T07:13:09Z (GMT). No. of bitstreams: 1 ntu-97-R95548029-1.pdf: 5598550 bytes, checksum: b575ce745fcdcbd760f669a35c7764aa (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | 目錄
口試委員會審定書 I 謝誌 I 中文摘要 III 英文摘要 IV 目錄 V 圖目錄 VIII 表目錄 XI 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 文獻回顧 3 1.4 研究目的 5 1.5 研究架構 6 第二章 基礎理論 7 2.1 乳癌的成因與分類 7 2.1.1 病理分類 8 2.1.2 乳癌分期系統 10 2.2 現行的乳癌檢查方法 11 2.2.1 乳房X光攝影（Mammography） 11 2.2.2 乳房超音波（Ultrasound） 13 2.2.3 核磁共振造影（Magnetic Resonance Imaging，MRI） 14 2.2.4 正子斷層掃描（Positron Emission Tomography，PET） 15 2.3 紅外線影像 15 2.3.1 紅外線取像概論 16 2.3.2 紅外線熱影像與熱輻射理論 17 2.3.3 紅外線熱影像之判讀標準 19 2.4 雙波段紅外線影像原理 20 第三章 研究材料與方法 25 3.1 研究材料 26 3.1.1 臨床試驗收案流程 26 3.1.2 臨床試驗拍攝流程 27 3.2 系統設計與建構 27 3.2.1 硬體架構 28 3.2.2 軟體演算法流程 30 3.3 影像對位 32 3.3.1 控制點的選取 33 3.3.2 仿射轉換法（Affine transformation） 34 3.3.3 薄板仿樣法（Thin-Plate Spline） 36 3.3.4 影像重新取樣 39 3.4 影像分析核心演算法 40 3.4.1 基於解析解 40 3.4.2 基於時空概念（Spatiotemporal approach） 42 第四章 研究成果與討論 43 4.1 DS-IR Spectrogram乳房攝影系統 43 4.2 影像對位方法之評估 44 4.2.1 不同對位方法之結果比較 45 4.2.2 多標記點實驗結果 46 4.2.3 討論影像對位與影像分析結果之關聯性 48 4.3 以DS-IR Spectrogram偵測乳癌之成效 49 4.3.1 影像分析統計結果與討論 50 4.3.2 基於時空概念分析結果 53 第五章 結論與展望 55 5.1 結論 55 5.2 未來研究方向 56 參考文獻 58 附錄A 臨床試驗受試者說明及同意書 63 附錄B 臨床試驗問卷 68 附錄C DS-IR Spectrogram實驗結果 69 C.1 惡性腫瘤（DCIS）實驗結果 69 C.2 惡性腫瘤（IDC）實驗結果 73 C.3 惡性腫瘤（其他類）實驗結果 90 C.3.1 惡性腫瘤類別：ILC 90 C.3.2 惡性腫瘤類別：Mucinous adenocarcinoma 91 C.3.3 惡性腫瘤類別：Metaplastic carcinoma 91 C.3.4 惡性腫瘤類別：Adocrine carcinoma 92 C.4 良性腫瘤實驗結果 93 圖目錄 圖 2. 1：乳房組織示意圖。 7 圖 2. 2：（a）DCIS示意圖；（b）IDC示意圖。 9 圖 2. 3：（a）乳房X光攝影示意圖；（b）常規之乳房X光攝影面向。 12 圖 2. 4：超音波影像之分區示意圖。 13 圖 2. 5：乳房磁振造影示意圖。 14 圖 2. 6：紅外線在電磁波光譜中的分段（Qi，2006）。 16 圖 2. 7：Planck曲線圖（Szu，2007）。 18 圖 2. 8：線性混合模型的向量圖（Szu，2006）。 21 圖 3. 1：系統流程及組織分工圖。 25 圖 3. 2：FLIR 紅外線照相機。 27 圖 3. 3：DS-IR spectrogram乳房攝影系統之示意藍圖。 28 圖 3. 4：DS-IR spectrogram 乳房攝影系統之校正影像。 29 圖 3. 5：演算法流程圖。 31 圖 3. 6：搜尋最佳控制點示意圖。 34 圖 3. 7：薄板仿樣法示意圖（Bookstein，1989）。 36 圖 3. 8：（a）座標轉換前後關係示意圖；（b）雙線性內插法說明圖。 39 圖 4. 1：本研究所設計並製作之DS-IR Spectrogram乳房攝影系統。 44 圖 4. 2：不同波段紅外線照相機拍攝之影像，（a）LIR；（b）MIR。 44 圖 4. 3：影像對位控制點示意圖，（a）第一組控制點；（b）第二組控制點。 46 圖 4. 4：惡性腫瘤case 57，病灶位置L11/3， 49 圖 4. 5：多時間點樣本，病灶位置R11/3。 54 圖C. 1：惡性腫瘤case 8，病灶位置L12/1。 69 圖C. 2：惡性腫瘤case 69，病灶位置R8.5/3。 70 圖C. 3：惡性腫瘤 case 71，病灶位置R’t UOQ。 70 圖C. 4：惡性腫瘤 case 77，病灶位置L12.5/4。 71 圖C. 5：惡性腫瘤 case 84，病灶位置R7/2。 71 圖C. 6：惡性腫瘤 case 92，病灶位置R12.5-2.5/1-5。 72 圖C. 7：惡性腫瘤 case 93，病灶位置L12/3。 72 圖C. 8：惡性腫瘤 case 15，病灶位置R8/2、R11/3。 73 圖C. 9：惡性腫瘤 case 26，病灶位置L8.5/3。 73 圖C. 10：惡性腫瘤 case 32，病灶位置R12/4。 74 圖C. 11：惡性腫瘤 case 39，病灶位置R10/4。 74 圖C. 12：惡性腫瘤 case 40，病灶位置R12/2、R3/0.5。 75 圖C. 13：惡性腫瘤 case 51，病灶位置L2/6.5。 75 圖C. 14：惡性腫瘤 case 52，病灶位置L3/1。 76 圖C. 15：惡性腫瘤 case 55，病灶位置R11/4。 76 圖C. 16：惡性腫瘤 case 56，病灶位置L’t（all breast）。 77 圖C. 17：惡性腫瘤 case 57，病灶位置L11/3。 77 圖C. 18：惡性腫瘤 case 59，病灶位置L3.5/4。 78 圖C. 19：惡性腫瘤 case 60，病灶位置L12/3。 78 圖C. 20：惡性腫瘤 case 61，病灶位置L11/5。 79 圖C. 21：惡性腫瘤 case 62，病灶位置R’t（huge mass）。 79 圖C. 22：惡性腫瘤 case 63，病灶位置R4/1。 80 圖C. 23：惡性腫瘤 case 64，病灶位置R10/3。 80 圖C. 24：惡性腫瘤 case 67，病灶位置R10/3.5。 81 圖C. 25：惡性腫瘤 case 68，病灶位置L7/2。 81 圖C. 26：惡性腫瘤 case 70，病灶位置L’t。 82 圖C. 27：惡性腫瘤 case 72，病灶位置L1/2。 82 圖C. 28：惡性腫瘤 case 75，病灶位置L8.5/5。 83 圖C. 29：惡性腫瘤 case 76，病灶位置L2/4。 83 圖C. 30：惡性腫瘤 case 78，病灶位置L10/3。 84 圖C. 31：惡性腫瘤 case 79，病灶位置R10/4。 84 圖C. 32：惡性腫瘤 case 80，病灶位置R11.5-12/4。 85 圖C. 33：惡性腫瘤 case 83，病灶位置L12.5/3。 85 圖C. 34：惡性腫瘤 case 85，病灶位置R10-10.5/4-4.5。 86 圖C. 35：惡性腫瘤 case 86，病灶位置L11/4。 86 圖C. 36：惡性腫瘤 case 87，病灶位置R7/3、R12/1。 87 圖C. 37：惡性腫瘤 case 88，病灶位置R8-12/0-5。 87 圖C. 38：惡性腫瘤 case 89，病灶位置R12/3。 88 圖C. 39：惡性腫瘤 case 90，病灶位置L1/2。 88 圖C. 40：惡性腫瘤 case 91，病灶位置L9/6。 89 圖C. 41：惡性腫瘤 case 91，病灶位置L7.5/3。 89 圖C. 42：惡性腫瘤 case 65，病灶位置R1/2。 90 圖C. 43：惡性腫瘤 case 53，病灶位置L2/3。 91 圖C. 44：惡性腫瘤 case 54，病灶位置R9.5/3。 91 圖C. 45：惡性腫瘤 case 82，病灶位置R11.5/4。 92 圖C. 46：惡性腫瘤 case 43，病灶位置L12/2。 92 圖C. 47：良性腫瘤 case 66，病灶位置R1/2。 93 圖C. 48：良性腫瘤 case 73，病灶位置R2/4。 93 圖C. 49：良性腫瘤 case 74，病灶位置R10.5/3。 94 表目錄 表1：乳房原位癌組織型態分佈（行政院衛生署癌症登記報告，2005）。 9 表2：乳癌組織型態分佈（行政院衛生署癌症登記報告，2005）。 9 表3：乳癌臨床分期方式。 10 表4：評估對位方法。 45 表5：多標記點實驗之驗證數據。 46 表6：依腫瘤類別分類之DS-IR Spectrogram統計表。 50 表7：依腫瘤大小分類之DS-IR Spectrogram統計表。 52 | |
| dc.language.iso | zh-TW | |
| dc.subject | 診斷系統 | zh_TW |
| dc.subject | 乳癌 | zh_TW |
| dc.subject | 雙波段紅外線影像 | zh_TW |
| dc.subject | 影像對位 | zh_TW |
| dc.subject | Breast Cancer | en |
| dc.subject | Image registration | en |
| dc.subject | Diagnostic System | en |
| dc.subject | DS-IR Spectrogram | en |
| dc.title | 雙波段紅外線乳癌診斷系統之研製與評估 | zh_TW |
| dc.title | Evaluation and Implementation of Dual-Spectrum IR Spectrogram Diagnostic System on Breast Cancer Detection | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃俊升,張允中,許志宇,劉子銘 | |
| dc.subject.keyword | 乳癌,雙波段紅外線影像,影像對位,診斷系統, | zh_TW |
| dc.subject.keyword | Breast Cancer,DS-IR Spectrogram,Diagnostic System,Image registration, | en |
| dc.relation.page | 61 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2008-07-30 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
| Appears in Collections: | 醫學工程學研究所 | |
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| ntu-97-1.pdf Restricted Access | 5.47 MB | Adobe PDF |
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