Medical Image Analysis Chapter 1 Introduction

1. 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 chuanyu@yuntech.edu.tw http://mipl.yuntech.edu.tw Office: ES709 Tel: 05-5342601 Ext. 4337

2. Medical Image Analysis Atam P. Dhawan, Ph.D. • Introduction • Image Formation • Interaction of Electromagnetic Radiation with Matter in Medical Imaging • Medical Imaging Modalities • Image Reconstruction • Image Enhancement • Image Segmentation • Image Representation and Analysis • Image Registration • Image Visualization • Current and Future Trends in Medical Imaging and Image Analysis Exercises in MATLAB References Image Databases on Website

3. 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.

4. Medical Imaging • Radiological sciences in the last two decades have witnessed a revolutionary 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.

5. 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.

6. 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.

7. A Multidisciplinary Paradigm 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。

8. 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.

9. Electromagnetic Radiation Spectrum

10. 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

11. Medical Imaging Modalities Source of Energy Used for Imaging Combination: External and Internal External Internal Magnetic Resonance Nuclear Medicine: X-Ray Radiographs Imaging: MRI, PMRI, Single Photon FMRI Emission Tomography (SPECT) X-Ray Mammography Optical Fluorescence Imaging Nuclear Medicine: Positron Emission X-Ray Computed Tomography Tomography (PET) Electrical Impedance Imaging Ultrasound Imaging and Tomography Optical Transmission and Transillumination Imaging Classification of medical imaging modalities

12. Medical Imaging Information • Anatomical • X-Ray Radiography • X-Ray CT • MRI • Ultrasound • Optical • Functional/Metabolic • SPECT • PET • fMRI, pMRI • Ultrasound • Optical Fluorescence • Electrical Impedance

13. 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.

14. 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.

15. 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.

16. True Condition Object is NOT present. Object is present. False Positive True Positive Object is observed. Observed Information True Negative False Negative Object is NOT observed. General Performance Measures • A conditional matrix for defining four basic performance measures as defined in the text

17. 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.

18. 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.

19. 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

20. TNF TPF a b c 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.

21. 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 condition of object present)=Ntp=10 • Total number of patients with biopsy proven normal tissue (true condition of object NOT present)=Ntn=90

22. 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.

23. 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

24. 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,

25. Bio/Medical Image Acquisition System Display Unit Digital Computer And Image Processing Unit Scanner Biomedical Image Processing and Analysis (cont.) • General schematic of biomedical image analysis system

26. Image Processing Task: Feature Enhancement Enhanced image through feature adaptive contrast enhancement algorithm Enhanced image through histogram equalization method