Medical Image Processing & Neural Networks Laboratory 1 Medical Image Processing Chapter 2 Digital Image Fundamentals 國立雲林科技大學 資訊工程研究所 張傳育 (Chuan-Yu Chang ) 博士 Office: ES 709 TEL: ext

Medical Image Processing & Neural Networks Laboratory 2 Structure of the Human Eye

Medical Image Processing & Neural Networks Laboratory 3 Structure of the Human Eye (cont.) Distribution of rods and cones in the retina

Medical Image Processing & Neural Networks Laboratory 4 Image Formation in the Eye Graphical representation of the eye looking at a palm tree

Medical Image Processing & Neural Networks Laboratory 5 Image Formation in the Eye (cont.) Brightness adaptation and Discrimination

Medical Image Processing & Neural Networks Laboratory 6 Image Formation in the Eye (cont.)

Medical Image Processing & Neural Networks Laboratory 7 Image Formation in the Eye (cont.) Typical Weber ratio as a function of intensity

Medical Image Processing & Neural Networks Laboratory 8 Image Formation in the Eye (cont.)

Medical Image Processing & Neural Networks Laboratory 9 Image Formation in the Eye (cont.)

Medical Image Processing & Neural Networks Laboratory 10 Optical illusion Image Formation in the Eye (cont.)

Medical Image Processing & Neural Networks Laboratory 11 Light and the Electromagnetic Spectrum

Medical Image Processing & Neural Networks Laboratory 12 =c/v : wavelength v: frequency c: speed of light (2.998*10 8 m/s) Light and the Electromagnetic Spectrum (cont.)

Medical Image Processing & Neural Networks Laboratory 13 Chapter 2: Digital Image Fundamentals

Medical Image Processing & Neural Networks Laboratory 14 Chapter 2: Digital Image Fundamentals

Medical Image Processing & Neural Networks Laboratory 15 Chapter 2: Digital Image Fundamentals

Medical Image Processing & Neural Networks Laboratory 16 Chapter 2: Digital Image Fundamentals Digital Image Acquisition Process Chapter 2: Digital Image Fundamentals Digital Image Acquisition Process

Medical Image Processing & Neural Networks Laboratory 17 Chapter 2: Digital Image Fundamentals Image Sampling and Quantization  To create a digital image, we need to convert the continuous sensed data into digital form. This involves two processes: Sampling  Digitizing the coordinate values Quantization  Digitizing the amplitude values

Medical Image Processing & Neural Networks Laboratory 18 Chapter 2: Digital Image Fundamentals Image Sampling and Quantization Chapter 2: Digital Image Fundamentals Image Sampling and Quantization

Medical Image Processing & Neural Networks Laboratory 19 Chapter 2: Digital Image Fundamentals

Medical Image Processing & Neural Networks Laboratory 20 Chapter 2: Digital Image Fundamentals Representing Digital Images  The result of sampling and quantization is a matrix of real numbers.

Medical Image Processing & Neural Networks Laboratory 21 Chapter 2: Digital Image Fundamentals

Medical Image Processing & Neural Networks Laboratory 22 Chapter 2: Digital Image Fundamentals Spatial Resolution  The smallest discernible detail in an image.  Line pair  Size: 1024*1024

Medical Image Processing & Neural Networks Laboratory 23 Chapter 2: Digital Image Fundamentals

Medical Image Processing & Neural Networks Laboratory 24 Chapter 2: Digital Image Fundamentals Gray-Level Resolution  The smallest discernible change in gray level.  The # of gray levels is usually an integer power of 2.

Medical Image Processing & Neural Networks Laboratory 25 Chapter 2: Digital Image Fundamentals False contouring

Medical Image Processing & Neural Networks Laboratory 26 Chapter 2: Digital Image Fundamentals Isopreference curves  Points lying on an isopreference curves correspond to images of equal subjective quality

Medical Image Processing & Neural Networks Laboratory 27 Chapter 2: Digital Image Fundamentals

Medical Image Processing & Neural Networks Laboratory 28 Chapter 2: Digital Image Fundamentals Zooming  Zooming may be views as oversampling.  Zooming requires two steps: Step 1: the creation of new pixel location. Step 2: the assignment of gray level to those new locations.  Nearest neighbor interpolation  Look for the closest pixel in the original image and assign its gray level to the new pixel in the grid.  Pixel replication  To double the size of an image, we can duplicate each column/ row  Biliner interpolation  Using the four nearest neighbors of a point.

Medical Image Processing & Neural Networks Laboratory 29 Zooming (cont.) Example 2.4  Using nearest neighbor gray-level / bilinear interpolation

Medical Image Processing & Neural Networks Laboratory 30 Chapter 2: Digital Image Fundamentals Shrinking  Shrinking may be views as undersampling. Row-column deletion To shrink an image by one-half, we delete every other row and column.

Medical Image Processing & Neural Networks Laboratory 31 Some basic relationships between pixels  Neighbors of a pixel 4-neighbors of p: N 4 (p)  (x+1, y), (x-1, y), (x, y+1), (x, y-1) diagonal-neighbors of p: N D (p)  (x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1, y-1) 8-neighbors of p: N 8 (p)  (x+1, y), (x-1, y), (x, y+1), (x, y-1), (x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1, y-1) p p p Some basic relationships between pixels

Medical Image Processing & Neural Networks Laboratory 32 Some basic relationships between pixels (cont.) If two pixels are connected, it must be determined  If they are neighbors and  If their gray levels satisfy a specified criterion of similarity.

Medical Image Processing & Neural Networks Laboratory 33  Adjacency: two pixels p and q with value from V are 4-adjacency: if q is in the set N 4 (p). 8-adjacency : if q is in the set N 8 (p). m-adjacency: if (i) q is in N 4 (p) or (ii) q is in N D (p) and the set N 4 (p)∩ N 4 (q) has no pixels whose values are from V.  To eliminate the ambiguities arise when 8-adjacency is used. Some basic relationships between pixels (cont.)

Medical Image Processing & Neural Networks Laboratory 34  Digital path (or curve) Path is a sequence of distinct pixels with coordinates  (x 0, y 0 ), (x 1, y 1 ), …,(x n, y n )  n is the length of the path  If (x 0, y 0 )=(x n, y n ), the path is closed path.  Connectivity Connected component  Regions If R is a connected set.  Boundary (border, contour) The boundary of a region R is the set of pixels in the region that have one or more neighbors that are not in R. The boundary of a finite region forms a closed path  Edge The edges are formed from pixels with derivative values that exceed a preset threshold. Some basic relationships between pixels (cont.)

Medical Image Processing & Neural Networks Laboratory 35 Distance measure  Pixels: p=(x,y), q=(s,t), z=(v, w)  Euclidean distance between p and q is defined as  D 4 distance (city-block distance) between p and q is defined as Some basic relationships between pixels (cont.)

Medical Image Processing & Neural Networks Laboratory 36  D 8 distance (chessboard distance) between p and q is defined as  Example: D 8 distance<= Some basic relationships between pixels (cont.)

Medical Image Processing & Neural Networks Laboratory 37  D m distance between p and q is defined as the shortest m-path between the points. Assume that p, p 2, and p 4 are 1. p3 p4p1p2pp3 p4p1p2p 0 p40p2p0 p40p2p 0 p41p2p0 p41p2p 1 p40p2p1 p40p2p 1 p41p2p1 p41p2p m-path=2 m-path=3 m-path=4 Some basic relationships between pixels (cont.)