medical image analysis chapter 1 introduction

Medical Image AnalysisChapter 1 Introduction

Chuan-Yu Chang (張傳育 )Ph.D.

Dept. of Computer and Communication Engineering

National Yunlin University of Science & Technology

[email protected]

http://mipl.yuntech.edu.tw

Office: ES709

Tel: 05-5342601 Ext. 4337

2醫學影像處理實驗室 (Medical Image Processing Lab.) Chuan-Yu Chang Ph.D.

Medical Image Analysis Atam P. Dhawan, Ph.D.

1. Introduction2. Image Formation3. Interaction of Electromagnetic Radiation

with Matter in Medical Imaging4. Medical Imaging Modalities5. Image Reconstruction6. Image Enhancement7. Image Segmentation8. Image Representation and Analysis9. Image Registration10. Image Visualization11. Current and Future Trends in Medical

Imaging and Image AnalysisExercises in MATLABReferencesImage Databases on Website


Imaging in Medical Sciences

Imaging is an essential aspect of medical sciences for visualization of anatomical structures and functional or metabolic (新陳代謝 ) information of the human body.

Structural and functional imaging of human body is important for understanding the human body anatomy, physiological processes, function of organs, and behavior of whole or a part of organ under the influence of abnormal physiological conditions or a disease.


Medical Imaging Radiological sciences in the last two decades have witnessed a rev

olutionary progress in medical imaging and computerized medical image processing.

Advances in multi-dimensional medical imaging modalities X-ray Mammography X-ray Computed Tomography (CT) Single Photon Computed Tomography (SPECT) Positron Emission Tomography (PET) Ultrasound Magnetic Resonance Imaging (MRI) functional Magnetic Resonance Imaging (fMRI)

Important radiological tools in diagnosis and treatment evaluation and intervention of critical diseases for significant improvement in health care.


Medical Imaging and Image Analysis

The development of imaging instrumentation has inspired the evolution of new computerized image reconstruction, processing and analysis methods for better understanding and interpretation of medical images.

The image processing and analysis methods have been used to help physicians to make important medical decision through physician-computer interaction.

Recently, intelligent or model-based quantitative image analysis approaches have been explored for computer-aided diagnosis to improve the sensitivity and specificity of radiological tests involving medical images.


A Multidisciplinary Field

Medical imaging in diagnostic radiology has evolved as a result of the significant contributions of a number of different disciplines from basic sciences, engineering, and medicine.

A number of computer vision methods have been developed for a variety of applications in image processing, segmentation, analysis and recognition.

However, computerized image reconstruction, processing and analysis methods have been developed for medical imaging applications Require specialized knowledge of a specific medical imaging modality

that is used to acquire images. The application-domain knowledge has been used in developing

models for accurate analysis and interpretation.


A Multidisciplinary Paradigm

Physiology and CurrentUnderstanding

Physics of Imaging

Instrumentationand Image Acquisition

Computer Processing,Analysis and Modeling

Applications andIntervention

生物醫學影像的智慧分析與判讀，須對影像的取得過程及原理有一定的認識。


Medical Imaging Modalities

The objective of medical imaging is to acquire useful information about the physiological processes or organs of the body by using external or internal sources of energy.

Energy source Internal

Nuclear medicine imaging use an internal energy source through an emission process to image the human body.

Radioactive pharmaceuticals (放射性藥劑 )are injected into the body to interact with selected body matter or tissue to form an internal source of radioactive energy that is used for imaging.

Ex. Single Photon Emission Computed Tomography (SPECT), PET External

Anatomical imaging are based on the attenuation coefficient( 衰減係數 ) of radiation passing through the body

Ex. X-ray radiographs and Computed Tomography (CT) Combination

MRI uses external magnetic energy to stimulate selected atomic nuclei. The excited nuclei become the internal source of energy to provide electromagnetic signals for imaging through the process of relaxation.


Electromagnetic Radiation Spectrum

10-10

Radio Waves

TV Waves

Radar Waves

Microwaves Infrared Rays

Visible Light

Ultraviolet Rays

X-rays Gamma Rays

102 101 1 10-1 10-2 10-3 10-4 10-6 10-7 10-8

Wavelength in meters

Frequency in Hz

10-5 10-9 10-10 10-11 10-12 10-13 10-14 103

106 107 109 1010 1011 1012 1014 1015 1016 1013 1017 1018 1019 1020 1021 1022 105 108

Energy in eV

10-9 10-8 10-6 10-5 10-4 10-3 10-1 1 101

10-2 102 103 104 105 106 107 10-7

MRI

X-ray Imaging

Gamma-ray Imaging

Cosmic Rays


Medical Imaging Modalities

Radiation/Imaging Source External

X-ray Radiography X-ray CT Ultrasound Optical: Reflection, Transillumination

Internal SPECT PET

Mixed MRI, fMRI Optical Fluorescence Electrical Impedance


Classification of medical imaging modalities

Source of EnergyUsed for Imaging

ExternalInternal

Combination:External andInternal

Nuclear Medicine:Single PhotonEmission Tomography(SPECT)

Nuclear Medicine:Positron EmissionTomography(PET)

Magnetic ResonanceImaging: MRI, PMRI,FMRI

Optical FluorescenceImaging

Electrical ImpedanceImaging

MedicalImaging

Modalities

X-Ray Radiographs

X-Ray Mammography

X-Ray ComputedTomography

Optical Transmissionand TransilluminationImaging

Ultrasound Imaging andTomography


Medical Imaging Information Anatomical

X-Ray Radiography X-Ray CT MRI Ultrasound Optical

Functional/Metabolic SPECT PET fMRI, pMRI Ultrasound Optical Fluorescence Electrical Impedance


Medical Imaging Modalities (cont.) There is no perfect imaging modality for all

radiological applications and needs. Each medical imaging modality is limited by the

corresponding physics of energy interactions with human body, instrumentation and often physiological constraints.

The performance of an imaging modality for a specific test or application is characterized by sensitivity and specificity factor. Sensitivity of a medical imaging test is defined primarily by

its ability to detect true information. The specificity for a test depends on its ability to not detect

the information when it is truly not there.


Medical Imaging: From Physiology to Information Processing Understanding Imaging Medium

The information about imaging medium may involve static or dynamic properties of the biological tissue.

Tissue density is a static property, blood flow, perfusion and cardiac motion are examples of dynamic properties.

Physics of Imaging X-ray imaging modality uses transmission of X-rays through the

body as the basis of imaging. Single Photon Emission Computed Tomography (SPECT) uses

the emission of gamma rays resulting from the interaction of a radiopharmaceutical substance with the target tissue.

The SPECT and PET imaging modalities provide images that are poor in contrast and anatomical details.

X-ray CT images modality provides shaper images with high-resolution anatomical details.

The MR imaging modality provides high-resolution anatomical details with excellent soft-tissue contrast.


Medical Imaging: From Physiology to Information Processing (Cont.) Imaging Instrumentation

Defining the image quality in terms of signal-to-noise ratio, resolution and ability to show diagnostic information.

An intelligent image formation and processing technique should be the one that provides accurate and robust detection of features of interest without any artifacts to help diagnostic interpretation.

Data Acquisition Methods for Image Formation Image Processing and Analysis

Aimed at enhancement of diagnostic information to improve manual or computer-assisted interpretation of medical images.

Interactive and computer-assisted intelligent medical image analysis methods can provide effective tools to help the quantitative and qualitative interpretation of medical images for differential diagnosis, intervention and treatment monitoring.


General Performance Measures

A conditional matrix for defining four basic performance measures as defined in the text

True Positive

True Negative

FalseNegative

FalsePositive

True Condition

Object is

present.Object is

NOT present.

Object is

observed.

Object is

NOT observed.

ObservedInformation


General Performance Measures (cont.) Positive observation

The object was observed in the test. Negative observation

The object was not observed in the test. True condition

The actual truth, whereas an observation is the outcome of the test.


General Performance Measures (cont.) Four basic measures

True Positive Fraction (TPF): TPF=Notp/Ntp The ratio of the number of positive observations to the number

of positive true-condition cases. False Negative Fraction (FNF): FNF=Nofn/Ntp

The ratio of the number of negative observations to the number of positive true-condition cases.

False Positive Fraction (FPF): FPF=Nofp/Ntn The ratio of the number of positive observations to the number

of negative true-condition cases. True Negative Fraction (TNF): TNF=Notn/Ntn

The ratio of the number of negative observations to the number of negative true-condition cases.


General Performance Measures (cont.) It should be noted that

TPF+FNF=1 TNF+FPF=1

Sensitivity True Positive Fraction, TPF

Specificity True Negative Fraction. TNF

Accuracy=(TPF+TNF)/Ntot


ROC (Receiver Operating Characteristic) ROC (Receiver Operating Characteristic)

A graph between TPF and FPF is called ROC curve for a specific medical imaging or diagnostic test for detection of an object.

TNF

TPF

b

a

c


Example of Performance Measure

Assume that 100 female patients were examined through the X-ray mammography test.

The X-ray mammography images were observed by a physician to classify into one of the two classes: normal and cancer.

The object is to determine the basic performance measures of the X-ray mammography test for detection of breast cancer. Total number of patients=Ntot=100 Total number of patients with biopsy proven cancer (true cond

ition of object present)=Ntp=10 Total number of patients with biopsy proven normal tissue (tru

e condition of object NOT present)=Ntn=90


Example of Performance Measure (cont.)

Out of the patients with cancer Ntp, the number of patients diagnosed by the physician as having cancer = Number of True Positive cases = Notp =8.

Out of the patients with cancer Ntp, the number of patients diagnosed by the physician as normal = Number of False Negative cases = Nofn =2.

Out of the normal patients Ntn, the number of patients rated by the physician as normal = Number of True Negative cases = Notn =85.

Out of the normal patients Ntn, the number of patients rated by the physician as having cancer = Number of False Positive cases = Nofp =5.


Example of Performance Measure (cont.)

True Positive Fraction (TPF)= 8/10 = 0.8 False Negative Fraction (FNF)= 2/10 = 0.2 False Positive Fraction (FPF)= 5/90 = 0.0556 True Negative Fraction (TNF)= 85/90 = 0.9444

TPF+FNF=1.0 FPF+TNF=1.0


Biomedical Image Processing and Analysis A general-purpose biomedical image-processing

and image analysis system must have three basic components: Image-acquisition system

Usually converts a biomedical signal or radiation carrying the information of interest to a digital image.

Digital computer Usually large memory units that are used to store digital

images for further processing. Image display environment

The output image can be viewed after the required processing,


Biomedical Image Processing and Analysis (cont.) General schematic of biomedical image analysis system

Bio/Medical

ImageAcquisition

System

Scanner

Digital ComputerAnd

Image ProcessingUnit

DisplayUnit


Image Processing Task: Feature Enhancement

Enhanced image through feature

adaptive contrast enhancement algorithm

Enhanced image through histogram

equalization method

medical image analysis chapter 1 introduction

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