Medical Image Processing and CAD

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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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”.


Dr G R Sinha


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Research Approach

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Research Outcome

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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


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CAD can         

diagnose monitor analyze, interpret plan, design, instruct clarify Learn Efficiently

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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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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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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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Anatomy to Physiology

 Anatomy: Body regions, organs, blood vessels, etc.  Physiology: Functions, metabolism, oxygen concentration, blood flow, etc.

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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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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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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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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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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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Imaging Process Reconstruction

Filtering

Raw data

“Raw data”

Processing

Signal acquisition

Analysis

Quantitative output

123…………… 2346………….. 65789………… 6578…………..

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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


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CAD interprets

 Detection of the abnormality  Classification: likelihood that the abnormality represents a malignancy

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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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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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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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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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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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Detection of Malignant Masses

benign

malignant

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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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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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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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CAD Characterization of a CT

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Contd..

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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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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


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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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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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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


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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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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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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


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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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CBMIR

Image Database

Feature Extraction

Image Features

Similarity Retrieval Query Image Feedback Algorithm User Evaluation

Query Results 43


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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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Performance of CAD

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Innovative & Inspiring Equation

E =mc2 m = Motivation c = Commitment E = Excellence

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Thank You for Kind Attention Any Queries Please!!

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