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???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
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dc.contributor.advisor | 丁建均 | zh_TW |
dc.contributor.advisor | Jian-Jiun Ding | en |
dc.contributor.author | 周敬庭 | zh_TW |
dc.contributor.author | Ching-Ting Chou | en |
dc.date.accessioned | 2024-01-26T16:29:06Z | - |
dc.date.available | 2024-01-27 | - |
dc.date.copyright | 2024-01-26 | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-01-17 | - |
dc.identifier.citation | [1] Rafael C. Gonzalez • Richard E. Woods "Digital Image Processing" 4E, pp. 328-332
[2] Peixuan Zhang & Fang Li(2014)" A New Adaptive Weighted Mean Filter for Removing Salt-and-Pepper Noise" IEEE Signal Processing Letters. pp.1280 - 1283 [3] Ian T. Young& Lucas J. van Vliet (1995) " Recursive implementation of the Gaussian filter " Signal Processing. pp.139-151 [4] G. Deng & L.W. Cahill (1993)"An adaptive Gaussian filter for noise reduction and edge detection". [5] https://zh.wikipedia.org/wiki/%E9%AB%98%E6%96%AF%E6%BF%BE%E6%B3%A2%E5%99%A8 [6] Johannes P.F. D''Haeyer "Gaussian Filtering of Images: A Regularization Approach." Signal Processing, pp. 169-181. [7] http://www.faadooengineers.com/online-study/post/ece/digital-image-processing/1129/boundary-descriptors [8] Meyer, F., & Beucher, S. "Morphological segmentation." Journal of Visual Communication and Image Representation, 1990. [9] Bhutada, S., Yashwanth, N., Dheeraj, P., & Shekar, K. . "Opening and closing in morphological image processing." World Journal of Advanced Research and Reviews, 2022. [10] Khairul Anuar Mat Said*, Asral Bahari Jambek*and Nasri Sulaiman (2016)"A study of image processing using morpholoical opening and closing processes " International Journal of Control Theory and Applications pp.15-21 [11] C. Ronse & H.J.A.M. Heijmans "The algebraic basis of mathematical morphology: II. Openings and closings" CVGIP: Image Understanding pp.74-97 [12] https://jason-chen-1992.weebly.com/home/-morphology [13] https://medium.com/%E9%9B%BB%E8%85%A6%E8%A6%96%E8%A6%BA/%E5%BD%A2%E6%85%8B%E5%AD%B8-morphology-%E6%87%89%E7%94%A8-3a3c03b33e2b [14] Szabo, T. L. (2014). "Diagnostic Ultrasound Imaging: Inside Out." pp. 1-37 [15] Rumack, C. M., & Levine, D. (2017). "Diagnostic Ultrasound, 2-Volume Set" (5th ed.). August 8, 2017. [16] https://www.uscultrasound.com/what-is-ultrasound-and-how-does-it-work/ [17] Andersson KE & Arner A.(2004) "Urinary bladder contraction and relaxation: physiology and pathophysiology." PP.935-986 [18] Banker, Hiral, and Selvarajan, Santosh K. "Prostate Imaging." [19] Z, Keqin, X, Zhishun, Z, Jing, W, Haixin, Z, Dongqing, & S, Benkang.(2007) "Clinical significance of intravesical prostatic protrusion in patients with benign prostatic enlargement." pp.1096-1099. [20] Su Hwan Shin, Jong Wook Kim, Jin Wook Kim, Mi Mi Oh, Du Geon Moon. "Defining the Degree of Intravesical Prostatic Protrusion in Association With Bladder Outlet Obstruction" Department of Urology, Korea University Guro Hospital, Seoul, Korea. [21] Lee, C. H., & Ha, H. K. (2014). "Intravesical prostatic protrusion as a predictor of early urinary continence recovery after laparoscopic radical prostatectomy. "International Journal of Urology pp.653-656. [22] https://commons.wikimedia.org/wiki/File:Prostate.jpg | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91433 | - |
dc.description.abstract | 藉由電腦視覺以及醫療資訊兩個領域的結合,傳統超音波影像在醫療領域中很多都藉由人為判讀,但本次研究希望藉由電腦視覺的影像分析技術,提出一套可公式化的方法來處理超音波影像並提出判讀超音波影像的方法,研究主題主要針對膀胱及攝護腺的凹陷指數來判讀前列腺向膀胱內膨出的狀態,我們主要使用的識別方法包含:雜訊濾除、邊緣偵測、動態閥值、形態學、最短距離評估來做處理。並使用PCA距離測量及中心點垂直距離等方法來做量測標準。
我們藉由濾除雜訊,找尋相對位子,對膀胱輪廓的規則整理,閥值比較以及分割合併還有橢圓近似等方法,能夠推斷出膀胱超音波影像的凹陷病變指數(IPP)並藉由橢圓近似還原出膀胱原本該有的樣子。 | zh_TW |
dc.description.abstract | By combining computer vision and medical information, traditional ultrasound images in the medical field are often interpreted manually. However, in this study, we aim to propose a formulaic approach for processing ultrasound images and present a method for interpreting ultrasound images through computer vision techniques. The research focuses on the depression index of the bladder and prostate to assess the condition of the prostate protruding into the bladder. The identification methods used primarily include noise elimination, edge detection, dynamic threshold, morphology, and shortest distance evaluation. Additionally, we employ PCA distance measurement and vertical distance from the centroid as measurement standards.
Through the elimination of noise, determination of relative positions, regularization of bladder contours, threshold comparison, segmentation and merging, and elliptical approximation, we can infer the intravesical prostatic protrusion (IPP) of bladder ultrasound images. By utilizing elliptical approximation, we aim to reconstruct the original appearance of the bladder from the ultrasound images. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-01-26T16:29:06Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2024-01-26T16:29:06Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii Contents v List of Figures vii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Main Contribution 2 1.3 Organization 2 Chapter 2 Reviews of Computer vision image Process Algorithms 3 2.1 Filtering method 3 2.1.1 Mean Filter 4 2.1.2 Median Filter 4 2.1.3 Gaussian filter 5 2.2 Boundary Descriptors 5 2.3 Morphology 7 2.3.1 Erosion 7 2.3.2 Dilation 8 2.3.3 Opening 9 2.3.4 Closing 10 Chapter 3 Reviews of Biomedical Knowledge 12 3.1 Ultrasound 12 3.2 Biomedical Domain Concept 12 3.2.1 Biomedical Ultrasound Image 12 3.2.2 Bladder 13 3.2.3 Prostate 14 3.2.3 Intravesical Prostatic Protrusion(IPP) 14 Chapter 4 Process Image Method 16 4.1 Introduction 16 4.2 Preprocess Image 18 4.2.1 Remove text data 19 4.2.2 Image Segmentation 19 4.2.3 Removing unnecessary label 20 4.3 Process Image 21 4.3.1 Noise Filtering 21 4.3.2 Chose Threshold 22 4.3.3 Remove Irrelevant Regions 25 4.3.4 Find Bladder Main Region 28 4.3.5 First Extension 29 4.3.6 Compare and expand of bladder 32 4.3.7 Find Final Bladder Position 37 4.3.8 Get Bladder Turning Point 38 4.3.9 Estimated Bladder Health Image 41 Chapter 5 Scoring Compare 45 5.1 Physician's marking calculation 45 5.2 Midpoint vertical distance 46 5.3 Ellipse PCA distance 48 5.4 Unit Conversion 51 5.5 Result Compare 52 5.6 Error attribution 54 Chapter 6 Experimental Results 58 Chapter 7 Conclusion 62 Reference 64 | - |
dc.language.iso | en | - |
dc.title | 前列腺向膀胱內膨出之超音波影像分析 | zh_TW |
dc.title | Intravesical Prostatic Protrusion Ultrasound Image Analysis | en |
dc.type | Thesis | - |
dc.date.schoolyear | 112-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 簡鳳村;許文良;曾易聰 | zh_TW |
dc.contributor.oralexamcommittee | Feng-Tsun Chien;Wen-Liang Hsue;Yi-Chong Zeng | en |
dc.subject.keyword | 膀胱凹陷,攝護腺病變特徵,前列腺向膀胱內膨出,超音波影像,醫療影像處理, | zh_TW |
dc.subject.keyword | Bladder indentation,features of prostatic lesions,Intravesical Prostatic Protrusion (IPP),ultrasound image analysis,medical image processing, | en |
dc.relation.page | 65 | - |
dc.identifier.doi | 10.6342/NTU202400113 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2024-01-18 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 電信工程學研究所 | - |
Appears in Collections: | 電信工程學研究所 |
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ntu-112-1.pdf | 2.06 MB | Adobe PDF | View/Open |
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