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Medical Imaging and Pattern Recognition. Lecture 3 Image Formats Oleh Tretiak. This Lecture. Digital Images Applications of Digital Images Image Formats General Medical. Introduction. A digital picture is a picture stored in binary (bits).

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medical imaging and pattern recognition

Medical Imaging and Pattern Recognition

Lecture 3

Image Formats

Oleh Tretiak

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

this lecture
This Lecture
  • Digital Images
  • Applications of Digital Images
  • Image Formats
    • General
    • Medical

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

introduction
Introduction
  • A digital picture is a picture stored in binary (bits).
  • The picture resides in a digital storage system as a file.
    • A file is a sequence of bytes
    • One byte consists of 8 bits
  • A picture is a rectangular array of pixels

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

pixels grey values and quantization
Pixels, Grey Values and Quantization
  • Conceptually, a monochrome (black and white) image is a function f(x, y), sampled over a two-dimensional grid.
  • Each sample value is called a pixel (picture element).
  • Conceptually, the function is real-valued and has a continuous range. This is called the grey value of the pixel.
  • On a computer, it is represented with a finite number of bits. This is called quantization.
  • Most frequently, the digital quantity is interpreted as a nonnegative integer represented by a byte (8 bits).

0 ≤ v < 28 (256)

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

example
Example

Pixel sequence in file

Raw picture format, 256x256, 1 byte per pixel

[ip:KPI/Notes/Lecture 3] ojt% ls -l lena.raw

-rw-r--r-- 1 ojt staff 65536 27 Sep 20:29 lena.raw

[ip:KPI/Notes/Lecture 3] ojt% hexdump -x lena.raw | more

0000000 6464 6567 6466 6966 6a68 696b 6a6a 6c6b

0000010 6b6a 6764 6162 5d5a 5d5e 5d63 6b6e 7c8b

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

calculations
Calculations
  • In the so-called raw format, the file contains only the gray values of the pixels.
  • Bits/picture = Rows x Columns x bits/pixel
  • Bytes/picture = Rows x Columns x bytes/pixel
  • Example:
    • For the previous slide, 256 rows, 256 columns, 1 byte per pixel.
    • Bytes = 256x256x1 = 65536

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

examples
Examples

256x256, 4 bit, 32 kB

256x256, 1 bit, 8 kB

256x256, 4 bit, 32 kB

128x128, 4 bit, 16 kB

256x256, 2 bit, 32 kB

256x256, 8 bit, 65 kB

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

pixels quantization and quality
Pixels, Quantization, and Quality
  • A given picture can be represented with different numbers of pixels and various numbers of bits per pixel.
  • Fewer pixels produces lower quality
  • Fewer bits per pixel produces lower quality
  • There is a tradeoff between quality and picture storage requirements.

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

uses of digital pictures
Uses of Digital Pictures
  • Photography
  • Medical Imaging
    • X-ray
    • CT
    • Ultrasound
  • Movies
    • DVD
    • Television

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

why standard formats
Why Standard Formats?
  • Interoperability
    • Image made by Nikon, viewed on computer made by Apple.
  • Advantages of standards
    • Competition among vendors (lower prices)
    • Creation of markets
    • Multiple vendors - product cycle safety

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

useful data about photograph
Useful Data About Photograph
  • Size (rows and columns)
  • Size of print (cm)
  • Size of subject (cm)
  • Color/BW
  • What color?

File format should contain these data

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

slide12
TIFF
  • Tagged Image File Format
  • Proprietary, now owned by Adobe
  • Many different options (easy to write, hard to read)
  • File contains information about
    • Rows and columns
    • How many components (colors, overlays)
    • Bits per channel
  • Example: lena.raw - 65,536 Bytes, lena.tif - 66,304 B
  • Extra storage (768 Bytes) used to store information about image.

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

image coding compression
Image Coding (Compression)
  • Why compress?
    • Store more pictures in same memory
    • Spend less time sending picture over web
  • Lossless compression:
    • Decompress file and get the same picture, bit - for - bit
    • Typically, two-fold compression only for gray value images.
  • Lossy compression:
    • Decompress and get something similar.
    • Any amount of compression is possible.
    • Tradeoff between image quality and compression.

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

lossless compression
Lossless Compression
  • Many ways have been developed
  • In practice, Lempel-Ziv (zip, gzip, etc) is the only one used.
  • Get 2-fold compressions over raw format
  • Can be included in TIFF

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

slide15
JPEG
  • JPEG = joint photographic experts group, standard released in 1992.
  • In practice (almost) only lossy image compression scheme used in practice.
  • Standard has many options, only one is used in practice.

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

example of jpeg compression
Example of JPEG compression

Very high quality:

compression = 2.33

Photoshop Image

Very low quality:

compression = 115

Produced by MATLAB

with Quality = 0

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

block diagram jpeg dct
Block Diagram — JPEG DCT
  • Image is divided into 8x8 blocks
  • The discrete cosine transform (DCT) is computed of each block.
  • The transform values are encoded.

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

jpeg dct decoder
JPEG DCT Decoder

The values for each block are decoded.

Decoded values are inversely transformed (inverse DCT), producing 8x8 pixel blocks.

The blocks are assembled into a picture.

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

jpeg files
JPEG Files
  • In practice (2004)
    • DCF - Design rules for the Camera File system (DCF)
      • Can include motion pictures, sound.
    • EXIF - Exchangeable image file format for digital still cameras
      • Data about time picture taken, focal length, etc.
      • File can be uncompressed (TIFF)
    • Compressed data in JPEG format

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

slide20
MIPR Lecture 3

Copyright Oleh Tretiak, 2004

slide21
MIPR Lecture 3

Copyright Oleh Tretiak, 2004

medical image environment
Medical Image Environment
  • Imaging devices, procedure rooms
    • X-ray, CT, MRI, Ultrasound, Nuclear
  • Patient information system
    • Patient history, images, scheduling/management/billing
    • Reliable massive storage devices.
  • Reading stations
    • Radiologists view images and history, generate reports.
  • All connected through network and storage

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

dicom standard
DICOM Standard
  • Digital Imaging and Communication in Medicine
  • Ongoing standard activity
  • Sponsored by the American College of Radiology (ACR) and National Electronics Manufacturers Association (NEMA)
  • 22 workgroups

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

features of dicom
Features of DICOM
  • Unit of data: Imaging Procedure
    • Procedure includes collection of images with specified goal, and includes specification of disease, organ, imaging device, contrast agent, etc.
  • The DICOM standard is object-oriented.

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

slide25
MIPR Lecture 3

Copyright Oleh Tretiak, 2004

what are medical images
What are “Medical Images”
  • The basic unit is an imaging procedure
    • Can consist of several images
  • Data can be “three dimensional”
    • Multiple slices, e. g. CT
  • Auxiliary non-image data
    • Patient history
    • Contrast agent
    • Date, time, ...

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

dicom and other standards
DICOM and Other Standards
  • Image standards in DICOM are from other sources
    • TIFF, JPEG
    • Distinguishing features:
      • 12, 16 bits per pixel
      • Schemes for dealing with three dimensions (not covered by other standards)

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

ultrasound of breast lesion
Ultrasound of Breast Lesion

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

issues
Issues
  • File size
  • Image quality
  • Interoperability

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

technology trends telemedicine pacs
Technology Trends: Telemedicine, PACS
  • Trend toward complex systems
    • PACS - picture archiving and communications
  • Critical resources
    • Expensive imaging devices, procedures
    • Medical expertise
  • Technological solutions
    • Bring patient to scanner
    • Bring image to expert
  • Digital imaging, databases, networks, standards are an essential part of the answer

MIPR Lecture 3

Copyright Oleh Tretiak, 2004

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