1. Biometrics: Implementation and Challenges & Computer-aided Diagnosis (CAD) system: An Interface between Engineering and Medical Science By
Dr.G.R.Sinha,
Senior grade IEEE Member
Professor (Electronics & Tele. ) & Associate Director
Faculty of Engineering & Technology
Shri Shankaracharya Technical campus, Bhilai, INDIA

2. Outline Biometrics: Implementation and Challenges
Introduction, Types, Traits, Challenges and scope of Research
CAD: An Interface between Engineering and Medical Science
Introduction, Need of CAD, Applications of CAD, CAD metrics, Breast Cancer Detection
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

3. 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”.
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

4. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

5. Innovative & Inspiring Equation
E =mc2 m = Motivation c = Commitment E = Excellence
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

6. Research Flow Diagram
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

7. Biometrics: Implementation and Challenges
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

8. Simple example of biometrics
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

9. Security Related Questions
Is he really who he claims to be?
Is this person authorized to use this facility?
Is he in the watch list posted by the government?
Answer: Use Efficient Biometric Security Technology
“Biometrics is a science of recognizing an individual based on his or her physical or behavioral traits”
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

10. Biometric Traits
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

11. Examples
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

12. A Biometric System
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

13. System Architecture Database Template Repository Acquire & Digitize
Biometric Data
Extract High Quality
Biometric Features
Rep. Template
Database
Template
Repository
Matcher
Extract High Quality
Biometric Features
Decision
Output
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

14. Implemented in two ways
Types of Biometrics
Unimodal: Deals with single trait.
Multimodal: Involves multiple modalities.
Implemented in two ways
Verification (1:1)
Identification (1:N)
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

15. Verification (1:1) Match? YES NO Person Image Template Scan Encode
Match?
YES NO
Are you who you
claim to be?
01101
01000
10010
ID token
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

16. Identification (1:N) Match? YES (found) Are you in our database?
Person Image Template
Scan
Encode
Match?
YES (found)
NO (not found)
Database
Are you in our database?
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

17. Face Location and shape of facial attributes:
Eyes, eyebrows, nose, lips etc.
Limitations:
1. Improper pose and Illumination
2. Template update problem
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

18. Fingerprint Pattern of ridges and valleys on fingertip Limitation:
Large computation
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

19. Iris Iris, is bounded by pupil and the white portion of eye
Each Iris is believed to be distinctive
Limitations:
Considerable user participation
Expensive
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

20. Signature The way a person signs Accepted all throughout the world
Limitations:
Professional forgers may reproduce signatures
Possibility of changing in emotional and physical conditions
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

21. Voice Physical + behavioral biometric Depends on:
Shape and size of vocal tracts, mouth,
nasal cavities and lips
Limitations:
Behavioral part changes from age, emotional state and medical conditions (cold)
Background noise
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

22. Comparative Biometric Market
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

23. Comparison
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

24. ISL Biometrics
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

25. Challenges in Biometrics
Large intra-class variability
Noisy and distorted images
System performance (error rate, speed etc.)
Attacks on the biometric system
Every biometric characteristic has some limitations
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

26. Threats to Biometrics The Modern Burglar
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

27. Imperfect Images
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

28. Human Characteristics
Universality - Everyone should have this trait/feature
Uniqueness - No two persons should share the same trait
Collectability – The trait can be measured
Permanence – Should display low variance with time
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

29. Vulnerabilities Override Final Decision Y/N Stored Templates
Modify Template
Override Feature Extractor
Intercept the channel
Sensor
Feature
Extractor
Matcher
Y/N
Application Device
Synthesized
Feature Vector
Override Final Decision
Replay Old Data
Fake Biometric
Override Matcher
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

30. Multimodal Biometrics
Multimodal biometrics improves performance
A combination of uncorrelated modalities (e.g. fingerprint, iris and face etc.) is expected to result in improvement in performance than a combination of correlated modalities.
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

31. Feature Level Score Level Decision Level
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

32. Architecture of Multimodal Biometrics
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

33. Fusion
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

34. contd..
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

35. Major Challenges Big Data Retrieval Time Awareness
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

36. Parallel Image Processing may help
Client
Server
Communication medium
Node-1
Node-2
Node-3
Node-4
Node-5
Node-6
Fractal Image Compressor
Parallel Fractal Image Compression
Input Image
Image De-compressor
Decompressed Image
Fractal image Decompression
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

37. Soft computing tools help to great extent
Computers are used to emulate the reasoning, problem-solving, creativity, and planning behaviors of human beings so that they can solve problems that are too large or too complex to be solved with traditional techniques.
The tools are:
Fuzzy Sets
Neuron Networks
Expert Systems (Knowledge-Based Systems)
Genetic Algorithms
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

38. CAD: An Interface between Engineering and Medical Science
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

39. Conventional Medical Diagnosis System
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
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

40. Screening Challenge high volume small viewing time
Complex image interpretation
high volume
small viewing time
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

41. CAD + Radiologist Radiologist CAD Radiologist + CAD missed missed
oversight
Detected
Marked
Detected
missed
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

42. Whereas CAD can diagnose monitor analyze, interpret plan, design,
instruct
clarify
Learn
Efficiently
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

43. CAD combines
Creation, recognition, representation, collection, organization, transformation, communication, evaluation and control of information
The art, science, engineering and human dimensions
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

44. Why CAD?
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

45. Health Informatics Needed
Increasing patient expectation and education
Increasing litigation
Demand for transparent processes
Clinical governance and audit
Unmanageable cognitive burden
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

46. Components of Medical Imaging
Body Region, Organ, Tissue, Cell
Energy sources
Detectors
Image formation
Display
User Interface
Connection to other Systems
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

47. Anatomy to Physiology
Anatomy: Body regions, organs, blood vessels, etc.
Physiology: Functions, metabolism, oxygen concentration, blood flow, etc.
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

48. 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.
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

49. 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.
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

50. 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
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

51. Biometrics & CAD: Scope and Challenges Dr G R Sinha 15th June, 2013

52. Computer Aided Diagnosis (CAD)
CAD has become one of the major support assisting medical experts in diagnosis through images
CAD tools are used for measurement, display and analysis of both the structural and functional aspects of the body
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

53. 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. )
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

54. CAD in Primary Care Patient visits the doctor with a complaint
If required, the patient is then referred by the doctor for specific imaging in order to diagnose the problem
The images are analyzed by the experts to arrive at a diagnosis
The diagnosis report is used by doctor for planning treatment
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

55. 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
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

56. Imaging Process Raw data Reconstruction Filtering “Raw data”
                         
Processing
Signal
acquisition
Analysis
123……………
2346…………..
65789…………
6578…………..
Quantitative output
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

57. Converting an image into data
User extracted qualitative features
User extracted quantitative features
Examination
Level: Feature 1
Feature 2
Feature 3 .
Finding: Feature 1
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

58. CAD interprets CAD may interpret: Detection of the abnormality
Classification: likelihood that the abnormality represents a malignancy
Classification, comparison, or analysis of images is performed almost always in terms of a set of features extracted from the images. Usual this is necessary for one of the following reasons:
Reduction of dimensionality: an 8-bit per pixel image of size 256x256 pixels has 25665,536 =10157,826 possible realisations. Clearly, it is worth –while to express structure within and similarities between images in ways that depends on fewer, higher-level representations of their pixels and relationship. It will important to show that the reduction nevertheless preserves information important to the task.
Incorporation of cues from human perception. Much is known about the effects of basic stimuli on the visual system. In many situations, we have considerable insight into how humans analyse images (essential in the training of radiologist and photo interpreters). Use of the right kinds of features would allow for the incorporation of that experience into automated analysis.
Transcend the limit of human perception. Though we can very easily understand many kinds of images, there are properties (e.g. some textures) of images that we cannot perceive visually, but which could be useful in characterising them. Features can be constructed from various manipulations of the images that make those properties evident.
Need for invariance. The meaning and the utility of an image are often unchanged when the image is perturbed in various way. Changes in one or more of scale, location, brightness and orientation for example and the presence of noise, artefacts and intrinsic variation are image alteration to which well-designed featured are wholly or partially invariant.
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

59. 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.
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

60. Image Analysis in CAD
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

61. Breast Anatomy
Breasts consist mainly of fatty tissue interspersed with connective tissue
There are also less conspicuous parts
lobes
ducts
lymph nodes
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

62. 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
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

63. Mammography Uses a low-dose x-ray system to examine breasts
Mammography replaces x-ray film by solid- state 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
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

64. Detection of Malignant Masses
benign
malignant
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

65. 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
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

66. 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
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

67. 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
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

68. CAD Characterization of a CT
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

69. Contd..
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

70. Computer-aided characterization
Reader 1
Reader 2
Reader 3
Lobulation=1 “marked”
Malignancy=5 “highly suspicious”
Sphericity=4
Lobulation=4
Malignancy=5 “highly suspicious”
Sphericity=2
Lobulation=2
Malignancy=5 “highly suspicious”
Sphericity=5 “round”
Show how outlines can also be different. Explain that for the same nodule, slices with biggest nodule can be different for different radiologists. Start talking about calculating image features of a nodule, go to the next slide.
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

71. Characterization Parameters/Features
Characteristic
Possible Scores
Margin
1. Poorly Defined
5. Sharp
Sphericity
1. Linear
2. .
3. Ovoid
4. .
5. Round
Spiculation
1. Marked
5. None
Subtlety
1. Extremely Subtle
2. Moderately Subtle
3. Fairly Subtle
4. Moderately Obvious
5. Obvious
Texture
1. Non-Solid
3. Part Solid/(Mixed)
5. Solid
Characteristic
Possible Scores
Calcification
1. Popcorn
2. Laminated
3. Solid
4. Non-central
5. Central
6. Absent
Internal structure
1. Soft Tissue
2. Fluid
3. Fat
4. Air
Lobulation
1. Marked
5. None
Malignancy
1. Highly Unlikely
2. Moderately Unlikely
3. Indeterminate
4. Moderately Suspicious
5. Highly Suspicious
Talk more about interpretation (and interpretation variability) of separate semantic characteristics and move to the next two slides to show a specific example.
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

72. Contd..
Shape Features
Size Features
Intensity Features
Texture Features
Circularity
Area
MinIntensity
Features calculated from co-occurrence 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
Describe 4 types of features used in a study. Explain how features are mapped to the semantic characteristics. Describe vector representation of a nodule after mapping is done {c1…c7, f1…f64} as input for automatic interpretation algorithm.
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

73. 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
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

74. Selection of Features SNR, PSNR, MSE and Entropy
Shape Features: Euclidian distance, perimeter, convex perimeter, major and minor axis, rectangularity, convexity, solidity etc.
Texture Features: mean, variance, skewness etc.
Major axis
Minor axis
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

75. 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.
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

76. Classification of Tissues
Texture Feature
Average intensity or gray scale value
Average contrast
value
Smoothness
Second moment
Uniformity
Entropy
Tissue types
Uncompressed and fatty
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
6.153
0.3273
3.857
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

77. Image segmentation method
Comparative Analysis
S. No.
Image segmentation method
Entropy
SNR (dB) 1.
Region growing
1.5012
23.65 2.
Watershed algorithm
1.4372
31.74 3.
k-means clustering
1.5768
28.65
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

78. Contd…
Data base
Segmentation methods TP FP FN TN
Sensitivity
(%)
Specificity
Accuracy
SSGIDB-1
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
SSGIDB-7
1478 65
153
95.8 89
95.2
1519 56
186
96.4 90
95.6
1392 29 69
166
95.27
94.1
SSGIDB-23 37
156
95.3
80.8
93.6
1573 87
146
94.7
88.5
94.2
1492 79
94.9
SSGIDB-39
1479 27 73
152 85
1279 20
162
1378 36
95.5 81
93.81
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

79. CBMIR Query Results Image Features Image Database Feature Extraction
Similarity Retrieval
Image Features
Image Database
Query Image
Query Results
Feedback
Algorithm
User Evaluation
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

80. Features used in CBMIR systems
Image features
- texture features: statistical and structural
shape features
Similarity measures
-point-based and distribution based metrics
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

81. Performance of CAD
Biometrics & CAD: Scope and Challenges Dr G R Sinha th June, 2013

82. Thank You
for
Kind Attention
Any Queries Please!!