Medical Image Processing

Medical Image
Processing-
Introduction

Technology beyond the Dreams™ Copyright © 2006 Pantech Solutions Pvt
Medical Imaging
• The study of medical imaging is concerned with
Interaction of all forms of radiation with
tissue.
• The development of appropriate technology to
extract clinically useful information, usually in the
form of an image from the observed
technology.
• Non-invasive visualization of internal organs, tissue,
etc.
– Is endoscopy an imaging modality?
• Image – a 2D signal f(x,y) or 3D f(x,y,z)

Major Modalities
• Projection X-ray (Radiography)
• X-ray Computed Tomography (CT)
• Magnetic Resonance Imaging
• Ultrasound
• Nuclear Medicine ( PET)
• Magnetic Resonance Angiography
• Fundus Images

X-ray
• Image Formation
• A beam of X-rays is directed through a patient
onto a film.
• The film provides a measure of the ray attenuation in tissue.

Sample X-ray Slices

Computer Tomography
• Image Formation The object is viewed from a number of
different angles and then a cross-sectional image of it can be
computed (reconstructed).

3D Reconstruction in CT

Sample CT Slices

Magnetic Resonance
Imaging (MRI)

Sample MRI

Ultrasound

Sample Ultrasound Image

Positron Emission Tomography
(PET)

Sample PET Images

Magnetic Resonance Angiography
• Image Formation Imagine the blood vessels (moving spins)
using MRI scanner.
 PCA -(phase contract angiography)
 CEA -(contrast enhanced angiography)
 CTA -(computer tomography angiography)
 DSA- (digital subtraction angiography)
 TOF -(time-of-flight)

Samples of Angiography

Fundus photography
• Fundus photography (also called fundography[
) is the creation of a
photograph of the interior surface of the eye, including the retina, optic disc,
macula,
• Fundus photography is performed by a fundus camera, which basically
consists of a specialized low power microscope with an attached camera. and
posterior pole

Sample Fundus Images
Fundus photography is used by optometrists,
ophthalmologists, and trained medical professionals for
monitoring progression of a disease, diagnosis of a disease

Basic Medical Image
Processing System
Acquisition
Processing
Visualizing/Reporting
Pre processing
Segmentation
Detection
Analyzing
Diagnosis

Acquisition of Images
• X-RAY
• MRI
• CT
• MRA
• PET
• MAMMOGRAM
• ULTRASOUND…..and More.

Preprocessing of Images
• Filtering
• Image Denoising
• Image Registration
• Image enhancement

List of algorithms for
Preprocessing
• Median filter
• Mean filter
• Wavelet Based Denoising
• Wavelet based Image registration

Segmentation

List of Segmentation
Algorithms
• K Means
• Fuzzy
• Neuro Fuzzy
• Neural Network based Segmentation
• Snake algorithm
• Thresholding based Segmentation

Detection

Analysis

Classification/Diagnoses

List of Applications
• Medical Image Compression
• Medical Image Denosing
• Image Filtering
• Medical Image Retrieval
• Mammograms
• Tumor detection
• Medical Image fusion

Medical Image Compression
Why Compress Medical Images ?
• Growing need for storage
• Efficient data transmission
• Telemedicine
• Tele-radiology applications
• Real time Tele-consultation.
• PACS (Picture archiving and communication
systems)

Challenges unique to medical
images.
• Compression Algorithms
• Lossy / Lossless
• Medical Images should always be stored in
lossless format.
• Erroneous Diagnostics and its legal implications.

Low motion areas  lossy
High motion areas  lossless
Future improvements
Lossless
Lossy

Encoding using “slices” (group of macroblocks):
– First slice for high motion areas
– Second slice for low motion areas
Slices may be encoded at different rates
Future improvements
First slice
Second
slice

Medical Image Fusion-
PET/CT
PET Image of the
Chest
CT Image of the
Chest
PET/CT Fused
Image of the
Chest
Multiple Modality images are registered and overlaid or
merged to provide additional information

Medical Image Retrieval
What for ?
• Medical assistance
– Use of similar cases in order to assist medical
detection/diagnosis, treatment, …
• Difficult cases detection/diagnosis assistance
• Treatment choice assistance
 Education
• Better understanding of diseases and
phenomena using multiple similar
examples
• Better training by the recognition of similar
symptoms – different origins cases
• Updating and creating medical multimedia
atlases
 Research
• Medical Image Data mining
• New pathological trends,
• Extract new global characteristics by type of
population and geographic regions

Medical Image Retrieval - Example

1. Differentiation of benign from malignant lessions
2. Staging of malignant disease
3. Differentiation of reccurent malignant disease
from therapy induced changes
4. Monitoring the response to therapy
Tumor Imaging

- Weight lost (15 kg)
- CT and endo ultrasound suspicion
of malignancy
- high risk patient undergoing dialysis
- PET negative
- clinical follow up without progression
Differentiation of benign from malignant lessions

Differentiation of reccurent malignant disease from
therapy induced changes
MRI PET

• At least one microaneurysm - earliest clinically detectable lesion
• Retinal hemorrhages
• Hard or soft exudates
Diabetic Retinopathy-Ultrasound
Imaging

Healthy Retina Diabetic Retinopathy

Most of the Medical Image processing system consist of
1)Preprocessing
2)Enhancement
3)Filtering , Denoising and Morphological Operations
4)Segmentation
5)Detection
Conclusion

Thank you!Thank you!
Any doubts?

For more details
– www.pantechsolutions.net
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– http://www.scribd.com/pantechsolutions
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Medical Image Processing

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