1. 醫療影像處理在診斷上之應用 嘉義大學資工系 教授 柯建全 時間 : 2009 年 5 月 13 日

2. Outline  Introduction  Object of medical image processing  Imaging devices  applications  Related techniques for Medical imaging  Research Results  Future works

3. Introduction  What is Medical imaging?  Why do we need digital image processing?  What kind of problems are often caused in medical images? Blurring caused by respiratory or motion Low contrast caused by imaging device or resolution Complicated textures  Research trends have been transferred from 2-D to 3-D reconstruction

4. Introduction (continue)  Integrate all possible methods in the filed of DIP, pattern recognition, and computer graphics  Qualitative  Quantitative  Three categories of imaging in different modalities Structural image Functional image Molecular image

5. Object  Help physicians diagnose Reduce inter- and intra-variability  Produce qualitative and quantitative assessment by computer technologies  Determine appropriate treatments according to the analyses  Surgical simulation or skills to reduce possible erros

6. Medical Imaging Modalities  X-ray  Ultrasound: non-invasive  Computed tomography  Magnetic resonance imaging  SPECT (Single photon emission tomography)  PET( Positron emission tomography)  Microscopy

8. X-ray

9. Ultrasound  2-D sonography  3-D sonography  Doppler color sonography A series of 2-D projection Reconstruction  4-D sonography

12. Computed tomography

13. MRI  可以觀察活體三度空間的斷層影像  磁振影像取影像時可以適當控制而得到不 同參數的影像，如溫度、流場 (flow) 、水 含量、分子擴散 ( diffusion) 、 灌流 (perfusion) 、化學位移 (chemical shift) 、 功能性 (functional MRI) 及不同核種如 氫、碳、磷

14. MRI-structural and functional image

16. Related techniques  Image processing Segmentation Registration Feature Extraction  Shape feature  Texture Motion tracking  Pattern recognition Supervised learning Un-supervised learning Neuro network Fuzzy Support vector machine(SVM) Genetic algorithm

17. Related techniques  3-D graphic Virtual diagnose or visualization Fusion between different modalities Bio-medical visualization

18. SPECT-functional image

19. PET(Positron Emission Tomography )  PET 以分子細胞學為基礎，將帶有特殊標記的葡 萄糖合成藥劑注入受檢者體內，利用 PET 掃瞄儀 的高解析度與靈敏度作全身的掃描，藉由癌細胞 分裂迅速，新陳代謝特別旺盛，攝取葡萄糖達到 正常細胞二至十倍，造成掃描圖像上出現明顯的 「光點」  能於癌細胞的早期 ( 約 0.5 公分 ) 準確地判定癌細 胞，提供醫師作為診斷及治療的依據，診斷率高 達 87-91 ％， 30 歲以上的成年人及有癌症家族史 的民眾，建議每隔 1 ～ 2 年做一次 PET 檢查。

20. PET (Positron emission tomography)

22. Applications in a hospital  Assist surgeon plan surgical operation or diagnose  Picture archiving system (PACS) 將醫療系統中所有的影像，以數位化的方式儲存，並經 由網路傳遞至同系統中，供使用者於遠側電腦螢幕閱讀 影像並判讀。  Telemedicine  Surgical simulation: Medical Visualization ， Surgical augmented Reality, Medical- purpose robot, Surgery Simulation ， Image Guided Surgery ， Computer Aided Surgery  Estimate the location, size and shape of tumor

23. PACS System

24. Virtual Surgery

26. Related techniques  Classification of normal or abnormal tissues such as carcinoma Pre-processing: Contrast enhancement, noise removal, and edge detection Lesion segmentation: extract contours of interest  thresholding  2-D segmentation  3-D segmentation based on voxel data  Color image processing

27. Our study  Contour detection and blood flow measurements in cardiac nuclear medical imaging  Virtual colonoscopy  Bone tumor segmentation with MRI and virtual display  Breast carcinoma based on histology

28. 原始系列影像原始系列影像 影像放大影像放大 影像去雜訊影像去雜訊 影像強化影像強化 左心室輪 廓偵測 心室功能 計算 影像前處理

30. (a) 強化後影像 (b) 心臟血流變化區域 (c) 心臟區域輪廓

32. Background Region

33. Contours within a sequence of frames

34. Result Tab 4.1 心室功能量測參數

35. Virtual colonscopy-Browsing or navigation within a colon  Helical CT – patients injected contrast medium  Re-sampling — Voxel-based  Interpolation  Automatic segmentation (seed) threshloding  Determination of the skeleton of the colon  Connected-Component Labeling  Surface rendering and volume rendering  Extraction of suspicious sub-volumes for diagnosis

37. Automatic segmentation

38. Determination of the skeleton of the colon

39. Display and measurement

40. Bone tumor segmentation with MRI and virtual display—Contrast medium  Otsu thresholding Region growing  Tri-linear interpolation  Morphological post-processing Morphological post-processing  Surface rendering  Measurement

41. Histogram of T1 weighted and T2 weighted

44. (a) 0 度 (b) 45 度

45. Classification of Breast Carcinoma

48. 正常異常 系統判斷為正常 126 系統判斷為異常 111 準確性 敏感度有效性 76.67%64.71%92.31%

49. Requirements for medical image processing system in clinical diagnosis  Automatic and less human interaction  Qualitative and quantitative measurements  Stable and reliable (experiments with much more cases)  Performance evaluation True positive, true negative, false positive, false negative Accuracy, sensitivity, and specificity Receiving operating characteristic curve (An index for evaluating the effectiveness of classification  Optimal classification threshold  Area under ROC approach 1 – better classification

50. ROC curve

51. Analyses of prognosis on breast cancer for a stained tissue  Microscopy with different resolution (400 or 100) for a stained tissue  Fluorescent microscopy in detecting the number of chromosome  Immunohistochemistry(IHC)

52. Preliminaries or problems ?  Blurring often caused by patient motion or respiration  Clinical opinion or idea obtained from an experienced surgeon  Non-absolute answers at some specific conditions  Trade-off between complexity and performance  Large variations for different image modality  Automation is necessary so as to help physicians  Prove identification accuracy — comparison between manual and image processing

53.  Thanks for your attention!