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

Outline

Introduction Object of medical image processing Imaging devices applications Related techniques for Medical

imaging Research Results Future works

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

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

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

Medical Imaging Modalities

X-ray Ultrasound: non-invasive Computed tomography Magnetic resonance imaging SPECT (Single photon emission

tomography) PET( Positron emission tomography) Microscopy

X-ray

Ultrasound

2-D sonography 3-D sonography Doppler color sonography

A series of 2-D projection Reconstruction

4-D sonography

Computed tomography

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

MRI-structural and functional image

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

Related techniques

3-D graphic Virtual diagnose or visualization Fusion between different modalities Bio-medical visualization

SPECT-functional image

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。

PET (Positron emission tomography)

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

PACS System

Virtual Surgery

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

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

原始系列影像

影像放大

影像去雜訊

影像強化

左心室輪廓偵測

心室功能計算

影像前處理

(a)強化後影像 (b)心臟血流變化區域 (c)心臟區域輪廓

Background Region

Contours within a sequence of frames

Result

Tab 4.1 心室功能量測參數

No. EF ES ED PER PFR

1 16.3 558 ml 667ml -0.7 0.4

2 37.4 256ml 775ml -1.12 1.87

3 53.5 56ml 120ml -0.56 2.67

4 84.3 60ml 380ml -1.33 4.21

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

Automatic segmentation

Determination of the skeleton of the colon

Display and measurement

Bone tumor segmentation with MRI and virtual display—Contrast medium

Otsu thresholding Region growing

Tri-linear interpolation Morphological post-processing Surface rendering Measurement

Histogram of T1 weighted and T2 weighted

Classification of Breast Carcinoma 開始

輸入組織影像(1524*1012)

色彩分離(RGB)

影像分割(Gray level、Otsu、Laplacian)

貝式網路判斷

特徵參數分析(導管比例、管腔個數、組織紋理...)

結束

正常 異常

系統判斷為正常 12 6

系統判斷為異常 1 11

準確性 敏感度 有效性

76.67% 64.71% 92.31%

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

ROC curve

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)

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

Thanks for your attention!