Medical Image Processing and CAD
By
Dr. G. R.Sinha Senior Grade IEEE Member, ISTE/IE/IETE National Award Recipient, IEEE Distinguished Speaker & CSI Resource Person for Digital Image Processing Professor (Electronics & Tele. ) & Associate Director Faculty of Engineering & Technology, Shri Shankaracharya Technical Campus
Chhattisgarh Swami Vivekanand Technical University Bhilai INDIA Email:
[email protected],
[email protected]
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Outline
Inspiring Thought CAD: Overview, Challenges, Application Medical Image Processing in Breast Cancer Detection Performance Evaluation of CAD
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Inspiring Thought
Take up one idea. Make that one idea your life. Think of it, dream of it. Live on that idea. Let the brain, muscles, nerves, every part of the body be full of that idea. This the way to “SUCCESS”.
Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
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Research Approach
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Research Outcome
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
CAD: An Interface between Engineering and Medical Science
Human expertise is a scarce resource whose supply is never guaranteed
Human get tired, forget, or simply becomes indolent
Humans are inconsistent in their day to day decisions for the same set of data
Human can lie, die, and hide
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Screening Challenge
Complex image interpretation
High volume
Small viewing time
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CAD + Radiologist Radiologist
CAD missed
Radiologist + CAD missed
oversight
Detected
Marked
Detected
missed 8
Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
CAD can
diagnose monitor analyze, interpret plan, design, instruct clarify Learn Efficiently
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
CAD combines
Creation, recognition, representation, collection, organization, transformation, communication, evaluation and control of information
The art, science, engineering and human dimensions
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Health Informatics Needed Increasing patient expectation and education Increasing litigation
Demand for transparent processes Clinical governance and audit
Unmanageable cognitive burden
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Components of Medical Imaging Body Region, Organ, Tissue, Cell Energy sources Detectors Image formation Display User Interface Connection to other Systems
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Anatomy to Physiology
Anatomy: Body regions, organs, blood vessels, etc. Physiology: Functions, metabolism, oxygen concentration, blood flow, etc.
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Magnetic resonance imaging (MRI)
MRI: Non-invasive method used to render images of the inside of an object used to demonstrate pathological or other physiological alterations of living tissues. Pathology: Study and diagnosis of disease through examination of organs, tissues, cells and bodily fluids Physiology: Study of the mechanical and physical functions of living organisms.
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
MRI versus CT A computed tomography (CT) also known as computed axial tomography (CAT) uses X-rays as ionizing radiation to acquire images for examining tissue such as bone and calcifications (calcium based) within the body (carbon based flesh), or of structures (vessels, bowel) MRI uses non-ionizing radio frequency (RF) signals to acquire its images and is best suited for non-calcified tissue.
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Positron Emission Tomography (PET) PET: Nuclear medicine medical imaging technique which produces a three-dimensional image of functional processes or Metabolic Activities in the body. To conduct the scan, a short-lived radioactive tracer isotope is injected into the living subject (usually into blood circulation). The data set collected in PET is much poorer than CT, so reconstruction techniques are more difficult
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
CAD helps in Visualization: Enhancement for visual analysis Detection: Detect the presence of disease manifestation Localization and Segmentation: Localize or segment the spatial regions containing disease manifestation Other utilities can be used for measurement of various structures from images (length, volume etc. )
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
CAD for Clinical decision making Making sound clinical decisions requires: right information, right time, right format Clinicians face a surplus of information: ambiguous, incomplete, or poorly organized Clinicians are particularly susceptible to errors of omission
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Imaging Process Reconstruction
Filtering
Raw data
“Raw data”
Processing
Signal acquisition
Analysis
Quantitative output
123…………… 2346………….. 65789………… 6578…………..
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Converting an image into data User extracted qualitative features User extracted quantitative features
Examination Level:
Finding:
Feature 1 Feature 2 Feature 3 . . Feature 1 Feature 2 . . 21
Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
CAD interprets
Detection of the abnormality Classification: likelihood that the abnormality represents a malignancy
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
CAD for Breast Cancer A mammogram is an X-ray of breast tissue used for detection of lumps, changes in breast tissue or calcifications when they're too small to be found in a physical exam. Abnormal tissue shows up a dense white on mammograms.
The left scan shows a normal breast while the right one shows malignant calcifications.
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Image Analysis in CAD
Lesion / Abnormality Segmentation
Organ Segmentation
- Breast Images - Thoracic Images
- Breast Boundary - Lungs - Colon
- Nodule - Polyps
Classification
Feature Extraction - Texture - Shape - Geometrical properties
Evaluation & Interpretation
- Malignant - Benign
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Breast Anatomy Breasts consist mainly of fatty tissue interspersed with connective tissue There are also less conspicuous parts lobes ducts lymph nodes
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Micro-calcification and Cancer Micro-calcifications: Tiny deposits of calcium can appear anywhere in a breast and can be seen in a mammogram Most women have one or more areas of micro-calcifications of various sizes Majority of calcium deposits are harmless A small percentage may be precancerous or cancer Some of the cells begin growing abnormally and may spread through the breast, to the lymph or to other parts of the body Common type of breast cancer begins in the milk-production ducts, but cancer may also occur in the lobules or in other breast tissue
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Mammography
Uses a low-dose x-ray system to examine breasts Mammography replaces x-ray film by solidstate detectors that convert x-rays into electrical signals which are used to produce images Mammography can show changes in the breast up to two years before a physician can feel
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Detection of Malignant Masses
benign
malignant
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Difficult Case
Heterogeneously dense breast The fibroglandular tissue (white areas) may hide the tumor The breasts of younger women contain more glands and ligaments resulting in dense breast tissue
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Easier Case
With age, breast tissue becomes fattier and has less number of glands Cancer is relatively easy to detect in this type of breast tissue
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
CAD Characterization for Lung Cancer Establish the link between computer-based image features of lung nodules in CT scans and visual descriptors defined by human experts (semantic concepts) for automatic interpretation of lung nodules, e.g. Lung nodule has a “solid” texture and has a “sharp” margin
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
CAD Characterization of a CT
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Contd..
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Computer-aided characterization
Reader 1
Lobulation=4 Malignancy=5 “highly suspicious” Sphericity=2
Reader 2
Lobulation=1 “marked” Malignancy=5 “highly suspicious” Sphericity=4
Reader 3
Lobulation=2 Malignancy=5 “highly suspicious” Sphericity=5 “round”
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Characterization Parameters/Features Characteristic
Possible Scores
Calcification
1. Popcorn 2. Laminated 3. Solid 4. NonNon-central 5. Central 6. Absent
Internal structure
1. Soft Tissue 2. Fluid 3. Fat 4. Air
Lobulation
1. Marked 2. . 3. . 4. . 5. None
Malignancy
1. Highly Unlikely 2. Moderately Unlikely 3. Indeterminate 4. Moderately Suspicious 5. Highly Suspicious
Characteristic
Possible Scores
Margin
1. Poorly Defined 2. . 3. . 4. . 5. Sharp
Sphericity
1. Linear 2. . 3. Ovoid 4. . 5. Round
Spiculation
1. Marked 2. . 3. . 4. . 5. None
Subtlety
1. Extremely Subtle 2. Moderately Subtle 3. Fairly Subtle 4. Moderately Obvious 5. Obvious
Texture
1. NonNon-Solid 2. . 3. Part Solid/(Mixed) 4. . 5. Solid 35
Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Contd..
Shape Features
Size Features
Intensity Features
Texture Features
Circularity
Area
MinIntensity
Features calculated from cocooccurrence matrices
Roughness
Convex Area
Maxintensity
Gabor features
Elongation
Perimeter
SDIntensity
Markov Random Field features
Compactness
Convex Perimeter
Contrast
Eccentricity
Equiv Diameter
Solidity
Major Axis Length Minor Axis Length
RadialDistanceSD
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Challenges in computer-aided characterization Large number of training samples and features: dimensionality
Variation in Nodule size and boundaries
Different types of imaging acquisition parameters
Clinical evaluation: observer performance studies require collaboration with medical experts or hospitals
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Selection of Features SNR, PSNR, MSE and Entropy Shape Features: Euclidian distance, perimeter, convex perimeter, major and minor axis, rectangularity, convexity, solidity etc. Minor axis
Major axis
Texture Features: mean, variance, skewness etc. 38
Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Evaluation Parameters True Positive (TP): A case when the suspected abnormality is malignant i.e. the prediction is true. True Negative (TN): If there is no detection of abnormality in healthy person. A case where no symptoms were found truly. False Positives (FP): This is very crucial parameter which indicates that detection of abnormality is found in healthy person. The prediction of presence of abnormality is not true. False Negatives (FN): No detection of malignant lesion is found, proves to be false.
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Classification of Tissues Texture Feature Average intensity Tissue types
or
Average Smoothn Secon
Uniform Entropy
contrast ess
ity
d
gray value
mome
scale
nt
value Uncompressed
43.652
46.314
0.0319
0.451
0.2156
4.876
fatty
68.512
71.236
0.0672
2.451
0.2332
3.253
Non uniform
51.065
81.972
0.0976
8.364
0.5225
4.468
High density
48,173
68.153
0.06472
6.153
0.3273
3.857
and fatty
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Comparative Analysis
S. No.
Image segmentation
Entropy
SNR (dB)
method 1.
Region growing
1.5012
23.65
2.
Watershed algorithm
1.4372
31.74
3.
k-means clustering
1.5768
28.65 41
Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Contd…
Data base
Segmentation
TP
FP
FN
TN
methods
SSGIDB-1
SSGIDB-7
SSGIDB-23
SSGIDB-39
Sensitivit
Specificit
Accurac
y
y
y
(%)
(%)
(%)
FCM
1468
21
76
121
95.1
85.2
94.2.
ANN
1480
19
71
120
95.42
86.43
94.6
GA
1358
23
83
135
94.3
85.5
93.4
FCM
1478
19
65
153
95.8
89
95.2
ANN
1519
23
56
186
96.4
90
95.6
GA
1392
29
69
166
95.27
85.2
94.1
FCM
1392
37
69
156
95.3
80.8
93.6
ANN
1573
19
87
146
94.7
88.5
94.2
GA
1492
23
79
135
94.9
85.5
94.1
FCM
1479
27
73
152
95.2
85
94.2
ANN
1279
20
71
162
94.7
89
94.1
GA
1378
36
65
153
95.5
81
93.81
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
CBMIR
Image Database
Feature Extraction
Image Features
Similarity Retrieval Query Image Feedback Algorithm User Evaluation
Query Results 43
Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Features used in CBMIR systems Image features - texture features: statistical and structural -shape features Similarity measures -point-based and distribution based metrics
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Performance of CAD
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Innovative & Inspiring Equation
E =mc2 m = Motivation c = Commitment E = Excellence
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016
Thank You for Kind Attention Any Queries Please!!
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Medical Image Processing and CAD
Dr G R Sinha
NWNLPIP_21st January, 2016