Digital Image Fundamentals - University Of Western Ontario

2y ago
21 Views
3 Downloads
7.49 MB
52 Pages
Last View : 2m ago
Last Download : 2m ago
Upload by : Eli Jorgenson
Transcription

Digital ImageFundamentalsComputer Science DepartmentThe University of Western OntarioPresenter: Mahmoud El-SakkaCS2124/CS2125:Introduction to Medical ComputingFall 2012October 31, 20121

Digital Image FundamentalsObjective During the last few lectures, various medical image modalities havebeen introduced to you, including: X-rayAngiographyFluoroscopyComputed Axial Tomography (CAT), or simply Computed Tomography (CT)Magnetic Resonance Imaging (MRI)UltrasoundNuclear medicineIn this lecture, an attempt will be made to: Explain the common thing among all these modalities Demonstrate the main differences between these modalities The image (to see inside the body without invasion or surgery)Why do we need all these modalities?Explore ways to maximize the benefit from these images Mahmoud R. El-Sakka2CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsWhat is an Image? An image is a way of representing the information in a given sceneDescribe the informationrepresented in the imageWhat is the dimension of each ofthese two image? three-dimensional imagetwo-dimensional imagesomething else Mahmoud R. El-Sakka3CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsWhat is an Image Composed of? Generally, an image is composed ofdiscrete units called picture elements(or simply pels, or pixels)Each pixel occupies a smallrectangular region of the image anddisplays one color at a timePixels are arranged so that theyform a two-dimensional array Mahmoud R. 773748191102112112 112 119 115 111114 106 106 101 99105 95 93 89 8897 94 87 88 8499 89 86 86 8295 87 78 75 7490 76 64 64 6180 63 53 55 6285 55 49 59 6791 44 46 64 8377 50 57 68 9568 56 60 75 10273 64 72 92 10384 80 88 98 10594 97 98 105 109105 109 110 113 31135135138140CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsWhat is an Image Composed of? Images are constructed by adjustingthe color of individual pixelsHow many pixels do we have inthis image?How many different images (thathave the same size as the pictureshown) can be generated? Mahmoud R. 773748191102112112 112 119 115 111114 106 106 101 99105 95 93 89 8897 94 87 88 8499 89 86 86 8295 87 78 75 7490 76 64 64 6180 63 53 55 6285 55 49 59 6791 44 46 64 8377 50 57 68 9568 56 60 75 10273 64 72 92 10384 80 88 98 10594 97 98 105 109105 109 110 113 31135135138140CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsColor components in Images Images can be ColorGrayBinary(each pixel displays one multi-color vlaue)(each pixel displays one shade of gray)(each pixel displays one of two colors black/white) Mahmoud R. El-Sakka6CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsColor components in Images If we divided each of the main three axes to 256 quantization levels,how many colors will we have in total? Mahmoud R. El-Sakka7CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsHow Do We See Things? An object is seen/recognized from the visible light reflected from itWithout light, we can not seeIf white light is shone onto agreen object, most wavelengthsare absorbed, except greenlight is reflected fromthe objectDo not forget that white light canbe decomposed into seven colors Mahmoud R. El-Sakka8ColoursAbsorbedCS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Acquisition Image acquisition is very similar to how we see things illuminatingthe scene by an energy source recording the reflected or transmitted energy using sensors Mahmoud R. El-Sakka9CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Acquisition A digital sensor can only measure a discrete set of energy levelsQuantization is the process of converting a continuous analogue signalinto a digital representation of this signalAt the end, you get some numbers, not colors Mahmoud R. El-Sakka10CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Acquisition Image viewers convert these numbers into color before displayingthemA digital image is always an approximation of a real world scene255 255 255 255 255 255 255 255 255 255 255 255255 255 255 255 255 255 255 255 255 255 255 255255 255 255223 255 255 255 255 255 255 255255 255 255727495 255 255 255 255 255 255255 25575979973 255 255 255 255 255 255255 25599 127 150 172 255 255 255 255 255 255255 255 127 150 175 175 175 255 255 255 255 255255 255 127 150 200 200 175 17598 255 255 255255 255 127 150 200 200 175 1759448 255 255255 255 127 150 150 175 127 1279248 255 255255 25548 255 25573 127 127 127255 255 255757370959890737580 255 255 255255 255 255 255 255 255 255 255 255 255 255 255255 255 255 255 255 255 255 255 255 255 255 255 Mahmoud R. El-Sakka11CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Acquisition In the case of humans, the energy source is the visible light, which is a kindof electromagnetic waveThere are various electromagnetic waves, for example:Based on the wavelength of the energy source used, you get variousmedical image modalitiesFor each electromagnetic waves, we need a special sensor to record andquantized reflected energy (humans can not see any electromagneticwaves, other than the visible spectrum) Mahmoud R. El-Sakka12CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Acquisition In a vacuum, electromagnetic waves travel with a speedequal to the speed of the light (300,000,000 meters/second)The relation between wavelength, frequency and speed isSpeed Wavelength FrequencyHigh frequencies mean shorter wavelengths more details Lesspenetration Mahmoud R. El-Sakka13CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Acquisition Let us revisit the medical image modalities that we know X-rayAngiographyFluoroscopyComputed Axial Tomography (CAT), or simply Computed Tomography (CT)Magnetic Resonance Imaging (MRI)UltrasoundNuclear medicineThe right modality should be used to visualize what you want to see Mahmoud R. El-Sakka14CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Acquisition Mahmoud R. El-Sakka15CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Size A typical x-ray digital image of size 1024 1024 (1 M. Pixel)requires at least 1 MB of storage space To upload this uncompressed image over a 1 Mbits/second modem, itwould take at least 8 seconds Now think of an endless number of such images!!Needless to mention uncompressed CT and MRI images!!Indeed there is an urgent need to apply image compressionschemes to reduce the size of such images Mahmoud R. El-Sakka16CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsWhat Does Compression Mean? In real life, compression meansmaking things smaller byapplying pressure Image compression is not aboutphysically squashing images,but about finding ways torepresent it in fewer bytes Mahmoud R. El-Sakka17CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsWhat Does Compression Mean? From this point of view, compression can be defined as a processintended to yield a compact representation of a given objectThe objective of image compression is to achieve compact digitalimage representation, with no, or at most minimal, perceived loss ofpicture quality Mahmoud R. El-Sakka18CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsWhat are Lossless and Lossy Compressions? Image compression schemes can be classified as Lossless schemes Compressing an image and expanding itagain produces an image which isidentical bit-by-bit to the original image All the information is preservedLossy schemes Compressing an image and expandingit again produces an image which isclose to the original image, i.e., it is notan exact match Some information might be lost Mahmoud R. El-Sakka19CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsData Versus Information Data and information are not synonymousData is the means by which information is conveyedVarious amounts of data may be used to represent the sameamount of informationThink of dataas raw material information as final product Mahmoud R. El-Sakka20CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsData Redundancies Data redundancy is a central issue in digital imagecompressionIn digital image compression, three basic data redundanciescan be identified and exploited Psychovisualredundancy Encoding redundancy Inter-pixel (a.k.a. spatial) redundancy Image compression is achieved when one, or more, of theseredundancies are reduced Mahmoud R. El-Sakka21CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsPsychovisual Redundancy256 grey levels (8 bits per pixel)128 grey levels (7 bpp)64 grey levels (6 bpp)32 grey levels (5 bpp)16 grey levels (4 bpp)8 grey levels (3 bpp)4 grey levels (2 bpp)2 grey levels (1 bpp) Mahmoud R. El-Sakka22CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsEncoding Redundancy The 512 400 stop sign image Is a gray-scale imageNeeds 512 400 bytes (200 KB) torepresent its pixel values Mahmoud R. El-Sakka23CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsEncoding Redundancy The image has only 4 gray-scale values,which are 0, 67, 189, and 255Pixel values can be represented as follows: “0” to be represented by (00)2 “67” to be represented by (01)2 “189” to be represented by (10)2 “255” to be represented by (11)2in this case, only 512 400 2/8 bytes(50 KB) are neededPixel values can also be represented as follows: “0” to be represented by (000)2 (8,156 cases) “67” to be represented by (1)2 (169,320 cases) “189” to be represented by (001)2 (2,567 cases) “255” to be represented by (01)2 (24,757 cases)in this case, only 8,156 3/8 169,320 1/8 2,567 3/8 24,757 2/8 bytes(30.64 KB) are needed Mahmoud R. El-Sakka24CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsInterpixel Redundancy The difference between adjacent pixels can be used to represent animage by applying this scheme to the stop sign image using thefollowing code:“0–255” to be represented by (0000)2 (178 cases) “255–0” to be represented by (0001)2 (178 cases) “0–67” to be represented by (0010)2 (202 cases) “67–0” to be represented by (0011)2 (202 cases) “189–255” to be represented by (010)2 (208 cases) “255–189” to be represented by (011)2 (208 cases) “255–255”, “189–189”, “67–67”, or “0–0” to be represented by (1)2 (203,623cases)in this case, only 1 (to encode the first pixel in the image) 178 4/8 178 4/8 202 4/8 202 4/8 208 3/8 208 3/8 203,623 1/8 bytes (25.38 KB) are needed Mahmoud R. El-Sakka25CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Compression Schemes Image compression schemes can be classified into: Statistical-based compression Prediction-based compression Lempel-Ziv encoding (LZ77, LZ78, LZW)Transform-based compression Prediction by Partial Matching (PPM)Two-Dimensional Run Length encoding (2D-RL)Dictionary-based compression Differential Pulse Code Modulation (DPCM)Binary Tree Predictive Coding (BTPC)Context-based compression Huffman encoderArithmetic encoderWavelet Transform (WT)Discrete Cosine Transform (DCT)Burrows–Wheeler Transform (BWT)Quantization-based compression Scalar quantizationVector Quantization (VQ) Mahmoud R. El-Sakka26CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Compression Schemes Mixture between two or more of the these schemes is also possible Joint Photographic Experts Group, (JPEG and JPEG-2000, JPEG-LS)Joint Bi-level Image Experts Group (JBIG)Set Partitioning In Hierarchical Trees (SPIHT)Context Adaptive Lossless Image Compression (CALIC)Graphic Interchange Format (GIF)Portable Network Graphics (PNG)ZIP compression .The list can go on and on As a representative, only one dictionary-based compressionexample will be given Mahmoud R. El-Sakka27CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsDictionary-based Compressions How many words do we have in a dictionary? Pocketdictionary: about 25,000 words Full-size dictionary: about 60,000 words If we will give a sequential number for each of thesewords, Pocketdictionary: we only need 15 bits (0 to 32,767) Full-size dictionary: we only need 16 bits (0 to 64,535) Mahmoud R. El-Sakka28CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsDictionary-based Compressions Assume that an English word consists of 6 letters (on average)Consider having an English dictionary containing half a millionwords (without their definition) This dictionary is searched for each word need to be encodedIf a match is found, this word is encoded by a pointer to that word(needs 20 bits) 1 bit to say that match was found19 bits as a dictionary index to that wordOtherwise, the word is encoded without any compression, i.e., a raw word(needs 50 bits on average), 1 bit to say that match was not found7 bits to identify the length of the word6 characters per word (on average) 7 bits per characters Mahmoud R. El-Sakka29CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsDictionary-based CompressionsAfter reading and compressing N words, the size of thecompressed file will be (on average)N (20 P 50 (1-P)) bits,where P is the probability of a match Without compression, we needN 49 bitsto encode these N words To achieve compression, the following relation must be holdN (20 P 50 (1-P)) N 49or in other word, P must be greater that 1/30 !! Mahmoud R. El-Sakka30CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsCompression / Decompression For each compression program, there is a matching decompressionprogram (both programs work hand-in-hand)Images can be compressed once and decompressed many timeTo view a compressed image, you have to have the correctdecompressor Distribution issue Mahmoud R. El-Sakka31CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsPACS A picture archiving and communication system (PACS) is a medicalimaging technology which provides economical storage andconvenient access to images from multiple modalitiesA PACS consists of four major components: Electronic images and reports are transmitted digitally via PACS acquisition devises (imaging systems), e.g., X-ray, MRI and CT equipmenta secured network for the transmission of patients information,archives for the storage/retrieval of images/reports, andworkstations for interpreting and reviewing imageseliminating the need to manually file retrieve or transport film jacketsNon-image data, such as scanned documents, may be incorporatedusing consumer industry standard formats like PDFThe universal format for PACS image is DICOM format (DigitalImaging and Communications in Medicine) Mahmoud R. El-Sakka32CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsPACS Mahmoud R. El-Sakka33CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsDICOM The Digital Imaging and Communications in Medicine (DICOM)standard was created to aid in the distribution and viewing of medicalimagesA single DICOM file contains both a header (which stores information about the patient's name, the type ofscan, image dimensions, etc), as well asall of the image data, which can be compressed using a variety of lossy orlossless compression schemes, including JPEG,JPEG Lossless,JPEG 2000, andRun-length encoding (RLE)DICOM does not store the image data in one file (*.img) and theheader data in another file (*.hdr) Mahmoud R. El-Sakka34CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Enhancement Image enhancement is the process of making images more usefulThe reasons for doing this include: Removing noise from imagesMaking images more visually appealingHighlighting important details in imagesThere are various techniques to enhance imagesI will shed some light on some of these techniques via examples Mahmoud R. El-Sakka35CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Enhancement (Example 1) To examine the fine blood vessel structures at the top of a patient’shead, an iodine medium is injected into the blood stream before takingthe X-rayTo enhance this image, an X-ray image for the same area is taken justbefore injecting the iodine medium (mask image)The mask image is subtracted from the image taken after the injectionThe contrast of the resulted image is stretchedBefore injection Mahmoud R. El-SakkaAfter injectionDifference36Contrast stretchedCS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Enhancement (Example 2) A nuclear whole body bone scan isused to detect diseases such asbone infection and tumorsThe image is difficult to enhance due to The narrow intensity dynamic rangeThe high noise contentTo enhance such an image, we need toapply several enhancement techniques Mahmoud R. El-Sakka37CS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Enhancement (Example 2) Get the 2nd derivative of the image using the Laplacian operatorAdd them together to get Original α 2nd derivative image (sharpened but too noisy) Original image Mahmoud R. El-Sakka 2nd derivative (scaled)38Original 2nd derivativeCS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Enhancement (Example 2) Extract the edges of the Original α 2nd derivative image using the Sobel operatorSmooth the Sobel image (averaging)Original 2nd derivative Mahmoud R. El-SakkaEdges using Sobel39Smoothed edge imageCS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Enhancement (Example 2) Multiply the smoothed image by the Original α 2nd derivative imagexSmoothed edge image Mahmoud R. El-Sakka Original 2nd derivative40Multiplied imageCS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Enhancement (Example 2) Add the original image to the generated multiplied image to get a sharpened imagewith less noise Original image Mahmoud R. El-Sakka Multiplied image41sharpened image/less noiseCS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Enhancement (Example 2) The contrast of the resulted image is stretchedsharpened image/less noise Mahmoud R. El-Sakka42Final resultCS 2124/2125: Introduction to Medical Computing

Digital Image FundamentalsImage Enhancement (Example 2) Now, let us compare the original image to the final imageOriginal image Mahmoud R. El-Sakka43Final resultCS 2124/2125: Introduction to Medical Computing

Dig

Digital Image Fundamentals 21 Data Redundancies Data redundancy is a central issue in digital image compression In digital image compression, three basic data redundancies can be identified and exploited Psychovisual redundancy Encoding redundancy Inter-pixel (a.k.a. spatial) redundancy

Related Documents:

Digital Image Fundamentals Titipong Keawlek Department of Radiological Technology Naresuan University Digital Image Structure and Characteristics Image Types Analog Images Digital Images Digital Image Structure Pixels Pixel Bit Depth Digital Image Detail Pixel Size Matrix size Image size (Field of view) The imaging modalities Image Compression .

L2: x 0, image of L3: y 2, image of L4: y 3, image of L5: y x, image of L6: y x 1 b. image of L1: x 0, image of L2: x 0, image of L3: (0, 2), image of L4: (0, 3), image of L5: x 0, image of L6: x 0 c. image of L1– 6: y x 4. a. Q1 3, 1R b. ( 10, 0) c. (8, 6) 5. a x y b] a 21 50 ba x b a 2 1 b 4 2 O 46 2 4 2 2 4 y x A 1X2 A 1X1 A 1X 3 X1 X2 X3

A digital image is a 2D representation of a scene as a finite set of digital values, calledpicture elements or pixels or pels. The field of digital image processing refers to processing digital image by means of a digital computer. NOTE: A digital image is composed of finite number of elements like picture elements, image

Digital Image Fundamentals Human and Computer Vision We can’t think of image processing without considering the human vision system. We observe and evaluate the images that we process with our visual system. Digital Image Processing

02 –Digital Image Fundamentals prepared by jimmyhasugian. 1/25/2017 2 There are many image processing applications and . To create a digital image, we need to convert the continuoussensed data into digital form. Image Sampling and Quantization Sampling Quanti

DIGITAL IMAGE FUNDAMENTALS & IMAGE TRANSFORMS The field of digital image processing refers to processing digital images by means of a digital computer. An image may be defined as a two- dimensional function, f(x,y) where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or .

Digital image processing is the use of computer algorithms to perform image processing on digital images. As a . Digital cameras generally include dedicated digital image processing chips to convert the raw data from the image sensor into a color-corrected image in a standard image file format. I

aliments contenant un additif alimentaire des dispositions des alinéas a) et d) du paragraphe 4(1) ainsi que du paragraphe 6(1) de la Loi sur les aliments et drogues de même que, s'il y a lieu, des articles B.01.042, B.01.043 et B.16.007 du Règlement sur les aliments et drogues uniquement en ce qui a trait