Medical image analysis involves processing visual representations of the interior of the body created through modalities like MRI, CT scans, x-rays, and others. The goals of medical image analysis are to develop computational methods to analyze biomedical data, enable patient-specific modeling, and further understand disease processes. Key steps involve image segmentation to identify structures, statistical analysis using techniques like classification, clustering and shape analysis to help answer clinical questions, and image-based modeling of physiological functions.

1. Medical Image
Analysis and
Its Application
PRESENTED BY : SUBARNO PAL
DEPARTMENT : CSE
ROLL NO. : 105
UNDER GUIDANCE OF : PROF SUDIPTA ROY
SEMINAR : CS681
ACADEMY OF TECHNOLOGY

2. What is Medical Imaging?

Process of creating visual representations
of the interior of a body

Usefull for clinical analysis and medical
intervention

Visual representation of the function of
some organs or tissues
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3. Medical Imaging Modalities:
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MRI RADIOGRAPHY USG
CT-PET X-RAY
Fig 1. Different forms of Medical Image

4. Motive of Medical Image Analysis:

To develop computational methods and
algorithms to analyze and quantify biomedical
data

Image-based patient-specific modelling

Understanding of disease processes
1. The history of disease evolution
2. The influence on the course of a disease
of pharmacological and interventional
therapeutic procedures
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5. Overview of the steps involved in
medical image processing
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Fig 2. Overview of steps involved in Medical Image Analysis(2)

6. Medical Image Data Forms :

Uniformly sampled data images

Regular x-y-z spatial spacing

Particular meaning of the data at the
sample point depends on modality
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7. 3D Reconstruction of 2D Image :

Source http://goo.gl/s3tq41
Source medigo.com
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Fig 3: 2D MRI with 3D reconstruction of Brain

8. Segmentation of Medical Image:

Atlas-Based Segmentation

Shape-Based Segmentation

Image-Based Segmentation

Interactive Segmentation

Partitioning into different segments of
biologically relevant structures.
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9. Segmented view of MRI Image:

gray matter in
Blue

white matter in
Yellow

cerebrospinal
fluid in Red
Source http://goo.gl/D8zAOZ
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Fig 4. Segmentation applied on MRI of Brain

10. Atlases:

Specific model for a population of images

Parameters that are learned from a
training dataset.

Single template: Model medical images
as deformed versions of a single
template image

Multiple templates: Template divided for
a healthy population & diseased
population
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11. Statistical Analysis:

This includes modern topics :

Computer Vision

Machine Learning

Pattern Reconition

“Mine and detect subtle changes” in the
images to address clinical questions on
larger data sets.
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12. Satistical Methods to answer
Clinical Questions(1):

Group Analysis: Objective is to detect and
quantize abnormalities

Shape Analysis: Analysis of geometrical
properties of structures obtained from
different images.

Longitudinal Studies: Studies the same
person is imaged repeatedly.
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13. Satistical Methods to answer
Clinical Questions(2):

Classification: Interested in early diagnosis
of the pathology, done using texture
analysis, neural network methods and
data mining techniques etc.

Clustering: Analysis of Heterogeneous
disorders, methodological alternatives to
pattern classification.
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14. Image-based Physiological Modelling

Provides a predictive tool based on a physical
and biological understanding of the underlying
processes.

Develops a 3D, patient-specific,
elasticity-based model that describes the
deformation of the organ.
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15. CONCLUSIONS
 Prompt and Rapid Actions can be taken
 Accuracy Increased on Medical Analysis
 Clustering help identifying heterogeneous
cases
 Early Detection Possible
 Efficient Algorithms are still being worked out
 Large Data –set handling always a concern
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16. References:-
1) Wikipedia Medical Image Computing https://goo.gl/P23SrO
27-04-2016
1) Smitha P. , Shaji L. , Dr. Mini M. G. (2011) 'A Review of Medical
Image Classification Techniques', International Conference on
VLSI, Communication & Instrumentation (ICVCI) , International
Journal of Computer Applications® (IJCA), pp. 35-40
2) Youtube https://goo.gl/8TY2Ts 27-04-2016
3) Bonnie J. Nagel, Christopher D. Kroenke, 'THE USE OF
MAGNETIC RESONANCE SPECTROSCOPY AND
MAGNETIC RESONANCE IMAGING IN ALCOHOL
RESEARCH', National Institute on Alcohol Abuse and
Alcoholism (NIAAA) http://goo.gl/D8zAOZ 27-04-2016
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17. Thank You
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