Image analysis. Daniel Smutek

Transcription

1 Image analysis Daniel Smutek

2

3 Biological imaging Radiology (clinical imaging) Endoscopy Microscopy Maps from biomedical signals electroencephalography (EEG) magnetoencephalography (MEG)

4 Clinical/Medical Imaging X-ray (Projection radiography) -contrast, double contrast Computed tomography (CT) Ultrasound Magnetic resonance imaging (MRI) Nuclear medicine Positron emission tomography (PET)

5 Invasive/Non-invasive radiation exposure, adverse reactions to contrast agents (barium sulfate vs iodine agents) X-ray CT Nuclear Imaging Ultrasound MRI vs contrast agents (gadolinium-based, microbubbles)

6 Radiation exposure Examination Relative dose Chest X-ray 1 Head CT 75 Abdomen CT 265 Chest CT 290 Chest, Abdomen and Pelvis CT 495 Cardiac CT angiogram 500 CT colonography (virtual colonoscopy) 250

7 Digitalization: Sampling Aividing the image in pixels. Sampling gives us resolution of a digital image (e.g. in megapixels Mpx). It is necessary to choose an appropriate resolution to preserve all the information that we want to capture Quantization - Assigning a numerical value to every pixel. The value represents color (brightness) of the pixel. E.g. 8-bit quantization offers 2 8 = 256 numerical values for each pixel Binary reprezentation coding the image in bites for writing into a data file 7

8 Resolution given by samling 256x256 pixels 128x128 pixels 64x64 pixels 32x32 pixels 16x16 pixels 8x8 pixels

9 No. shades of grey given by quantization 256 shades 16 shades 8 shades 4 shades 2 shades - black and white only

10 Noise

11 Brightness and contrast The brightness value is assigned to each pixel Grayscale images typically 1 byte for each px which can represent 256 shades of gray Color images Three channls Red, Green, Blue Every chanell has its own range of values E.g. TrueColor images 1 byte (256 values) for Red channel, 1 byte (256 values) for Green channel, 1 byte (256 values) for Blue channel. In total 3 bytes = 24 bites, therefore Truecolor images provides 2 24 or 16,777,216 color variation Contrast - define as a brightness difference between two pixels 11

12 Image histogram Graphical representation showing amount of pixels in the image for each brightness value Useful when we want to determine if the image is too dark (underexposed) or too bright (overexposed) or if it has a small dynamic range (low overall contrast)

13 Native image

14 Decrease of brightness

15 Increase of brightness

16 Změna jasu Zvýšení jasu přičtení konstanty >0 16

17 Změna jasu Snížení jasu odečtení konstanty >0 17

18 Native image

19 Decrease of contrast

20 Increase of contrast

21 Změna kontrastu Zvýšení kontrastu násobení konstantou >1 21

22 Změna kontrastu Snížení kontrastu dělení konstantou >1 22

23 Histogram Equalization - CT image intelligent histogram adjustment that gives much better contrast Native image After equalization 23

24 Computer Aided Diagnosis (CAD) Diagnosis

25 Thyroid and its Function Hyperfunction - orbitopathy Hypofunction - cretenism

26

27 2D histogram co-occurrence matrix obraz grey levels kookurenční matice (2D histogram) pro (1;0) 1D histogram

28 Haralick Texture Features for 2D Image Segmentation co-occurrence matrix : normalization : Haralick features : We used 15 texture features: entropy texture contrast texture correlation texture homogeneity inverse difference moment... e.g. texture homogeneity :

29

30 DSA digital subtraction angiography After injection of contrast medium - Subtraction mask Contrast adjustment

31

32 32

33 Segmentation of CT 3D Images in Medicine diagnosing organs segmentation Future-CAD project

34 Texture-based segmentation of 3D Images 2D texture image segmentation well established 3D texture segmentation can use more information

35 Example Results of Segmentation original image hand-made segmentation automatic segmentation using our method

36 36

37 39

38 40

39 41

40 42

41 43

42 PACS Picture archiving and communication system computers or networks dedicated to the storage, retrieval, distribution and presentation of images integration with hospital information system radiology information system (RIS).

43 DICOM Digital Imaging and Communications in Medicine Standard for medical imaging handling storing printing transmitting file format definition network communications protocol (TCP/IP based)

44 DICOM format (Digital Imaging and Communications in Medicine) - header Image Type (0008,0008) 1-n CS [ORIGINAL\PRIMARY\M\NORM] Study Date (0008,0020) 1 DA [ ] Study Time (0008,0030) 1 TM [18:03: ] Modality (0008,0060) 1 CS [UZ] Patient's Name (0010,0010) 1 PN [xxx] Patient's ID (0010,0020) 1 LO [RC] Slice Thickness (0018,0050) 1 DS [6] Trigger Time (0018,1060) 1 DS [242.5] Series Number (0020,0011) 1 IS [9] Acquisition Number (0020,0012) 1 IS [1] Instance (form...image) Number (0020,0013) 1 IS [8] Image Position (Patient) (0020,0032) 3 DS [ \ \ ] Image Orientation (Patient) (0020,0037) 6 009\ \ \ ] DS [ \ \ e- Slice Location (0020,1041) 1 DS [ ] Rows (0028,0010) 1 US [256] Columns (0028,0011) 1 US [192] Pixel Spacing (0028,0030) 2 DS [ \ ] Bits Allocated (0028,0100) 1 US [16] Bits Stored (0028,0101) 1 US [12] High Bit (0028,0102) 1 US [11] Pixel Data (7FE0,0010) 1 OW [49152 * 2 bytes at offset 23518]

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63 Registration

64

65 Find 9 heads

66 Literature Špunda, Dušek & kol.: Zdravotnická informatika, Karolinum, Praha, 2007 Kasal, Svačina: Lékařská informatika (Karolinum, Praha, 1998) Nekula & kol.: Radiologie, 3. vyd. (Univerzita Palackého, Olomouc, 2005) Urbánek: Nukleární medicína, 4. vyd. (Gentiana, Jilemnice, 2002) Huang: PACS and Imaging Informatics Basic Principles and Applications, 1st edition (Wiley, New Jersey, 2004) Shortliffe, Cimino: Biomedical Informatics Computer Applications in Health Care and Biomedicine, 3rd edition (Springer, New York, USA, 2006) Poznámky a prezentace z přednášek a seminářů, e- learningové materiály ze stránek ústavů 1. LF UK / / (biofyzika, fyziologie, nukleární medicína, radiologie) 79