SE1CA5

1. SE1CA5 Medical Image Analysis One Lecture Dr Virginie F. Ruiz

2. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 2 Medical Image Systems The last few decades of the 20th century has seen the development of: Computed Tomography (CT) Magnetic Resonance Imaging (MRI) Digital Subtraction Angiography Doppler Ultrasound Imaging Other techniques based on nuclear emission e.g: PET: Positron Emission Tomography SPECT: Single Photon Emission Computed Tomography Provide a valuable addition to radiologists imaging tools towards ever more reliable detection and diagnosis of diseases. More recently conventional x-ray imaging is challenged by the emerging flat panel x-ray detectors. Medical imaging has experienced, during the last few decades, the development and commercialisation of a pletiora of new imaging technologies: computed tomography, MR Imaging, digital subtraction angiography, Doppler ultrasound imaging and various imaging techniques based on nuclear emission (PET,SPECT…). They all have been valuable addition to the radiologists arsenal of imaging tools towards ever more reliable detection and diagnosis of disease. More recently, conventional x-ray imaging technology itself is being challenged by the emerging possibilities offered by flat panel x-ray detectors. This course is to give some ideas and methods of image processing and analysis that are to work in the field of medical imaging. Medical imaging has experienced, during the last few decades, the development and commercialisation of a pletiora of new imaging technologies: computed tomography, MR Imaging, digital subtraction angiography, Doppler ultrasound imaging and various imaging techniques based on nuclear emission (PET,SPECT…). They all have been valuable addition to the radiologists arsenal of imaging tools towards ever more reliable detection and diagnosis of disease. More recently, conventional x-ray imaging technology itself is being challenged by the emerging possibilities offered by flat panel x-ray detectors. This course is to give some ideas and methods of image processing and analysis that are to work in the field of medical imaging.

3. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 3 General image processing whether it is applied to: Robotics Computer vision Medicine etc. will treat: imaging geometry linear transforms shift invariance frequency domain digital vs continuous domains segmentation histogram analysis etc that apply to any image modality and any application

4. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 4 General image analysis regardless of its application area encompasses: incorporation of prior knowledge classification of features matching of model to sub-images description of shape many other problems and approaches of AI... While these classic approaches to general images and to general applications are important, the special nature of medical images and medical applications requires special treatments.

5. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 5 Special nature of medical images Derived from method of acquisition the subject whose images are being acquired Ability to provide information about the volume beneath the surface though surface imaging is used in some applications Image obtained for medical purposes almost exclusively probe the otherwise invisible anatomy below the skin. Information may be from: 2D projection acquired by conventional radiography 2D slices of B-mode ultrasound full 3D mapping from CT, MRI, SPECT, PET and 3D ultrasound. The special nature of medical images derives as much from their method of acquisition as it does from the subjects whose images are being acquired. While surface imaging is used in some applications (e.g. examination of properties of the skin), medical imaging has been distinguished primarily by its ability to provide information about the volumes beneath the surface (from the discovery of x-ray some 100 years ago). Image are obtained for medical purposes almost exclusively to probe the otherwise invisible anatomy below the skin. This information may be in the form of: 2 dimensional projection acquired by traditional radiography 2D slices of B-mode ultrasound or full 3D mappings such as those provided by CT, RMI, SPECT, PET and 3D ultrasound.The special nature of medical images derives as much from their method of acquisition as it does from the subjects whose images are being acquired. While surface imaging is used in some applications (e.g. examination of properties of the skin), medical imaging has been distinguished primarily by its ability to provide information about the volumes beneath the surface (from the discovery of x-ray some 100 years ago). Image are obtained for medical purposes almost exclusively to probe the otherwise invisible anatomy below the skin. This information may be in the form of: 2 dimensional projection acquired by traditional radiography 2D slices of B-mode ultrasound or full 3D mappings such as those provided by CT, RMI, SPECT, PET and 3D ultrasound.

6. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 6 difficulties/specificities Radiology: perspective projection maps physical points into image space but, detection and classification of objects is confounded to over- and underlying tissue (not the case in general image processing). Tomography: 3D images bring both complication and simplifications 3D topography is more complex than 2D one. problem associated with perspective and occlusion are gone. Additional limitation to image quality: distortion and burring associated with relatively long acquisition time (due to anatomical motion). reconstruction errors associated with noise, beam hardening etc. All these and others account for the differences between medical and non medical approaches to processing and analysis. In the case of radiology, perspective projection maps physical points into image space in the same way as photography, but the detection and classification of objects is confounded by the presence of overlying or underlying tissue, a problem rarely considered in general image analysis. In the case of tomography, 3D images bring both complications and simplifications to the processing and analysis relative to two dimensional ones: topology of 3D is more complex than 2D ones problems associated with perspective projection and occlusion are gone In addition to these geometrical differences, medical images typically suffer more from the problems of discretisation, where larger pixels (voxels in 3D) and lower resolution combine to reduce fidelity. Additional limitations to image quality arise from the distortions and burring associated with relatively long acquisition times in the face of inevitable anatomical motion – primarily cardiac and pulmonary. reconstruction errors associated with noise, beam hardening, etc. These and other differences between medical and non medical techniques of image acquisition account for many of the differences between medical and non-medical approaches to processing and analysis.In the case of radiology, perspective projection maps physical points into image space in the same way as photography, but the detection and classification of objects is confounded by the presence of overlying or underlying tissue, a problem rarely considered in general image analysis. In the case of tomography, 3D images bring both complications and simplifications to the processing and analysis relative to two dimensional ones: topology of 3D is more complex than 2D ones problems associated with perspective projection and occlusion are gone In addition to these geometrical differences, medical images typically suffer more from the problems of discretisation, where larger pixels (voxels in 3D) and lower resolution combine to reduce fidelity. Additional limitations to image quality arise from the distortions and burring associated with relatively long acquisition times in the face of inevitable anatomical motion – primarily cardiac and pulmonary. reconstruction errors associated with noise, beam hardening, etc. These and other differences between medical and non medical techniques of image acquisition account for many of the differences between medical and non-medical approaches to processing and analysis.

7. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 7 Advantage of dealing with medical images: knowledge of what is and what is not normal human anatomy. selective enhancement of specific organs or objects via injection of contrast-enhancing material. All these differences affect the way in which images are processed and analysed. Validation of medical image processing and analysis techniques is also a major part of medical application validating results is always important the scarcity of accurate and reliable independent standards create another challenge for medical imaging field. The fact the medical image processing deal mostly with living body bring other major differences in comparison to computer or robot vision. The object of interest are soft and deformable with 3D shapes whose surfaces are rarely rectangular, cylindrical or spherical and whose features rarely include planes or straight lines that are so frequent in technical vision applications There are however major advantages in dealing with medical images that contribute in a substantial way to the analysis design. The available knowledge of what is and what is not normal human anatomy is one of them. Recent advances in selective enhancement of specific organs or other objects of interest via the injection of contrast-enhancing material represent other advances. All these differences affect the way in which images are effectively processed and analysed. Validation of developed medical image processing and analysis techniques is a major part of any medical application. While validating the results of any methodology is always important, the scarcity of accurate and reliable independent standards creates yet another challenge for medical imaging field.The fact the medical image processing deal mostly with living body bring other major differences in comparison to computer or robot vision. The object of interest are soft and deformable with 3D shapes whose surfaces are rarely rectangular, cylindrical or spherical and whose features rarely include planes or straight lines that are so frequent in technical vision applications There are however major advantages in dealing with medical images that contribute in a substantial way to the analysis design. The available knowledge of what is and what is not normal human anatomy is one of them. Recent advances in selective enhancement of specific organs or other objects of interest via the injection of contrast-enhancing material represent other advances. All these differences affect the way in which images are effectively processed and analysed. Validation of developed medical image processing and analysis techniques is a major part of any medical application. While validating the results of any methodology is always important, the scarcity of accurate and reliable independent standards creates yet another challenge for medical imaging field.

8. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 8 Processing and Analysis Medical image processing Deals with the development of problem specific approaches to enhancement of raw medical data for the purposes of selective visualisation as well as further analysis. Medical image analysis Concentrates on the development of techniques to supplement the mostly qualitative and frequently subjective assessment of medical images by human experts. Provides a variety of new information that is quantitative, objective and reproducible Medical image processing deals with the development of problem specific approaches to enhancement of raw medical data for the purposes of selective visualisation as well as further analysis. Medical image analysis then concentrates on the development of techniques to supplement the mostly qualitative and frequently subjective assessment of medical images by human experts with a variety of new information that is quantitative, objective and reproducibleMedical image processing deals with the development of problem specific approaches to enhancement of raw medical data for the purposes of selective visualisation as well as further analysis. Medical image analysis then concentrates on the development of techniques to supplement the mostly qualitative and frequently subjective assessment of medical images by human experts with a variety of new information that is quantitative, objective and reproducible

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14. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 14 Virtual sinus endoscopy of chronic sinusitis. The red structure means inflammatory portion. The trip starts from right nasal cavity and goes through right maxillary sinus and ends at right frontal sinus. Virtual sinus endoscopy of chronic sinusitis. The red structure means inflammatory portion. The trip starts from right nasal cavity and goes through right maxillary sinus and ends at right frontal sinus.

15. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 15 This animation is derived from MRI data of a patient with a glioma 1. This demonstrates planning of a stereotactic procedure using computerized simulation 2. This shows three alternative approaches for a surgical removal of the tumour. 3. This demonstrates registration of vessels derived from a phase contrast angiogram and anatomy derived from double-echo MR scans. This animation is derived from MRI data of a patient with a glioma 1. This demonstrates planning of a stereotactic procedure using computerized simulation 2. This shows three alternative approaches for a surgical removal of the tumour. 3. This demonstrates registration of vessels derived from a phase contrast angiogram and anatomy derived from double-echo MR scans.

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18. Mammogram 1 Mammogram 2

19. Mammogram 1

20. Mammogram 2

21. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 21 Contrast Stretching To enhance low-contrast images Contrast Stretching Low-contrast images occurs often due to poor non-uniform lightening conditions or due to non-linearity or small dynamic range of the sensor. The figure shows a typical contrast stretching transformation, which can be express as: ...... The parameters a and b can be obtained by examining the histogram of the image. For example, the grey scale intervals where pixels occur most frequently would be stretch most to improve the overall visibility of a scene. The slope of the transformation is chosen greater than unity in the region of stretch For dark region stretch alpha>1 and a of order L/3. For mid-region stretch beta>1, and b of order (2/3)L. For bright region stretch gamma>1. Contrast Stretching Low-contrast images occurs often due to poor non-uniform lightening conditions or due to non-linearity or small dynamic range of the sensor. The figure shows a typical contrast stretching transformation, which can be express as: ...... The parameters a and b can be obtained by examining the histogram of the image. For example, the grey scale intervals where pixels occur most frequently would be stretch most to improve the overall visibility of a scene. The slope of the transformation is chosen greater than unity in the region of stretch For dark region stretch alpha>1 and a of order L/3. For mid-region stretch beta>1, and b of order (2/3)L. For bright region stretch gamma>1.

22. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 22 A practical application of this tool is given below. The original image is a CT Image with pixel values ranging from 1024 to 1862. The histogram plotted ranges from 1000 to 1862. Applying the Window and Level technique with parameters Window=100 and Level=1100 yields Depicted below is the original image and the result after applying the Window and Level technique with parameters Window=38 and Level=74. A practical application of this tool is given below. The original image is a CT Image with pixel values ranging from 1024 to 1862. The histogram plotted ranges from 1000 to 1862. Applying the Window and Level technique with parameters Window=100 and Level=1100 yields Depicted below is the original image and the result after applying the Window and Level technique with parameters Window=38 and Level=74.

23. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 23 Thresholding: special case of clipping, and the output becomes binary Image segmentation is the process of dividing an image into regions. This process is problem-oriented. Examples of segmentation are illustrated using two types of images of the heart, cineangiocardiographic and a nuclear medicine images. In the first case, the challenge is to separate the blood (light) area from the rest. In the second case, the problem is to separate the live tissue (light) area from the rest. The simplest and most widely used segmentation method is thresholding. It consists of setting background values for pixels below a threshold value T and a different set values for the foreground. If the input image is f(x,y) and thresholded image is g(x,y), the equation of the thresholding operator is given by: Thresholding is a special case of clipping where a=b=t and the output becomes binary. Example, a seemingly binary image, such as a printed page, does not give binary output when canned because of sensor noise and background illumination variations. Thresholding is used to make such an image image binary. Image segmentation is the process of dividing an image into regions. This process is problem-oriented. Examples of segmentation are illustrated using two types of images of the heart, cineangiocardiographic and a nuclear medicine images. In the first case, the challenge is to separate the blood (light) area from the rest. In the second case, the problem is to separate the live tissue (light) area from the rest. The simplest and most widely used segmentation method is thresholding. It consists of setting background values for pixels below a threshold value T and a different set values for the foreground. If the input image is f(x,y) and thresholded image is g(x,y), the equation of the thresholding operator is given by: Thresholding is a special case of clipping where a=b=t and the output becomes binary. Example, a seemingly binary image, such as a printed page, does not give binary output when canned because of sensor noise and background illumination variations. Thresholding is used to make such an image image binary.

24. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 24 Here the example of an angiogram thresholding In the experiment below, the variation on the threshold value causes a large variation on the area of the foreground pixels. This is a difficult problem to solve. Shown below are the original image and the results after applying different thresholds values (118, 128, 138) to it. The areas of each thresholded image are depicted below. Level Area 118 19,670 pixels 128 16,969 pixels 14,462 pixels .Here the example of an angiogram thresholding In the experiment below, the variation on the threshold value causes a large variation on the area of the foreground pixels. This is a difficult problem to solve. Shown below are the original image and the results after applying different thresholds values (118, 128, 138) to it. The areas of each thresholded image are depicted below. Level Area 118 19,670 pixels 128 16,969 pixels 14,462 pixels .

25. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 25 Here the spect-heart. The original image has been first expanded or "zoomed" by a factor of 4. Shows the threshold images at 118, 128, 138 Depicted below are the original image and the results after applying different thresholds values (118, 128, 138) to it. The original image has been first expanded or "zoomed" by a factor of 4. The areas of each thresholded image are depicted below. Level Area 118 731 pixels 128 659 pixels 138 588 pixels Here the spect-heart. The original image has been first expanded or "zoomed" by a factor of 4. Shows the threshold images at 118, 128, 138 Depicted below are the original image and the results after applying different thresholds values (118, 128, 138) to it. The original image has been first expanded or "zoomed" by a factor of 4. The areas of each thresholded image are depicted below. Level Area 118 731 pixels 128 659 pixels 138 588 pixels

26. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 26 Logarithmic contrast enhancement to brighten dark images, apply a logarithmic colour-table. map the pixel values of original: A common contrast enhancement procedure to brighten dark images is the application of a logarithmic colour table. Shown below is the original image for our experiment and the logarithm colour table. A common contrast enhancement procedure to brighten dark images is the application of a logarithmic colour table. Shown below is the original image for our experiment and the logarithm colour table.

27. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 27 Exponential contrast enhancement When images too bright, a contrast enhancement table like the exponential function can be used to darken the image. Shown below is the original image for our experiment and the exponential colour table. ... .... We want to map the pixel values of the original image using the exponential colour table. Performing this operation yields When images too bright, a contrast enhancement table like the exponential function can be used to darken the image. Shown below is the original image for our experiment and the exponential colour table. ... .... We want to map the pixel values of the original image using the exponential colour table. Performing this operation yields

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29. Dr V.F. Ruiz SE1CA5 Medical Image Analysis 29 Below is an original grey scale or monochrome image and next to it is the same image but with a grey level ramp inserted in it. This is done so that the colour table can be visualised directly in the image display. The technique to generate the image with a built-in grey-ramp is very useful to understand how the colours are mapped in the display. a)Original image; b)With an grey-ramp Shown below are two images using two different pseudo colour tables. The image on the left uses the "rainbow" colour table and the one on the right uses the "SApseudo" colour table. a)Rainbow colour table; b)SApseudo colour table Below is an original grey scale or monochrome image and next to it is the same image but with a grey level ramp inserted in it. This is done so that the colour table can be visualised directly in the image display. The technique to generate the image with a built-in grey-ramp is very useful to understand how the colours are mapped in the display. a)Original image; b)With an grey-ramp Shown below are two images using two different pseudo colour tables. The image on the left uses the "rainbow" colour table and the one on the right uses the "SApseudo" colour table. a)Rainbow colour table; b)SApseudo colour table

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