Medical image processing

Medical Image Processing
G R Sinha, PhD
IEEE Senior Member, ACM Distinguished Speaker, IEEE Distinguished Speaker
Professor, Myanmar Institute of Information Technology Mandalay
Recipient of ISTE National Award, TCS Award, IEI Award, Expert Engineer Award, Young Engineer Award, Young Scientist Award
Email: drgrsinha@ieee.org, ganeshsinha@acm.com, gr_sinha@miit.edu.mm

2
 Image Processing and CAD
 Breast Cancer- A Case Study
 Health Informatics
Lecture Outline
Medical Image Processing G R Sinha

Image Processing and CAD (Computer-aided Diagnosis)
3Medical Image Processing G R Sinha

4
Digital Image

5
 Pre-processing
 Image Enhancement
 Image Transformation
 Image Restoration
 Color Image Processing
 Image Compression
 Image Segmentation, Representation and Description
 Content-based Image Retrieval
 Pattern Recognition
Digital Image Processing

6
Analog signal
X(t)
Sampling
Quantization
Sampled signal
X(k)
Discrete signal
Digital signal
Encoding
Image processing is a set of tools which involves converting an
analogue or continuous image into digital form.
 Then performing some operations upon it so that the image
quality may be enhanced or some useful information could be
extracted.
Digital Signal

Conventional Medical Diagnosis System
 Human expertise is a scarce resource
 Human gets tired and forget
 Humans are inconsistent in their day to day decisions for the same set of data
 Human can lie, die, and hide
 Screening Challenges: Complex image interpretation; high volume and small viewing
time
7

Radiologist CAD Radiologist + CAD
Detected Marked Detected
Missed
Missed
Oversight
Missed
CAD + Radiologist
8

CAD (Computer-aided Diagnosis)
 CAD Combines creation, recognition, representation, collection, organization,
transformation, communication, evaluation and control of information.
 Art, science, engineering and human dimensions
 Diagnose; monitor; analyze; interpret; plan; design; instruct; clarify and Learn Efficiently

Imaging Process
Raw data
Reconstruction
123……………
2346…………..
65789…………
6578…………..Quantitative output
Processing
Analysis
Filtering
“Raw data”
Signal
acquisition
10

 Body Region, Organ, Tissue, Cell
 Energy sources and Detectors
 Image formation and Display
 User Interface
 Connection to other Systems
11
Components of Medical Imaging

Converting an image into data
 Qualitative and quantitative features
Examination
Level: Feature 1
Feature 2
Feature 3
.
.
Finding: Feature 1
Feature 2
.
.
12

CAD helps in
 Visualization: Enhancement for visual analysis.
 Detection: Detect the presence of disease manifestation/abnormality.
 Localization and Segmentation: Localize or segment the spatial regions.
 Detection of the abnormality and Classification of likelihood that the abnormality
represents a malignancy’
13

CAD for Breast Cancer
 A mammogram is an X-ray of breast tissue used for detection of lumps, changes in breast
tissue or calcifications.
 Abnormal tissues are generally dense white.

X-ray physics
 X-rays are form of electromagnetic energy which travel at the speed of light; and can pass all the way
through the body; be deflected or scattered or be absorbed
 Depends on the energy of the x-ray and the atomic number of the tissue.
 Higher energy x-rays: More likely to pass through
 Higher atomic number: More likely to absorb the x-ray
15

Radiographic Densities
1. Air
2. Fat
3. Soft tissue/fluid
4. Mineral
5. Metal
1.
2.
3.
4.
5.
16

Detection
Diagnosis: “Yes”I think my dog
swallowed something
17

Ultrasound
 Non-invasive
 Abdominal problems, measurement of blood flow and detection of constrictions in arteries and
veins.
 Also used in non-destructive testing in industry: e.g., cracks in structures.
18

 High-frequency sound (ultrasonic) waves to produce images of structures within the human body
 Piezoelectric crystal creates sound waves
 Still images or a moving picture
 Commonly used to examine fetuses to ascertain size, position, or abnormalities; also for heart, liver,
kidneys, gallbladder, breast, eye, and major blood vessels
contd..
19

MRI and CT
 Computed tomography (CT) also known as computed axial tomography (CAT) that 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.
20

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.

Detection of Malignant Masses
malignant benign
22

Difficult Case
 Heterogeneously dense breast
 Fibro-glandular tissue (white areas) may hide the tumor
 Breasts of younger women contain more glands and ligaments
resulting in dense breast tissue
23

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
24

CAD Characterization of a CT

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

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
27

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): 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.
28

Breast Cancer Detection-A Case Study

Breast Anatomy and Micro-calcifications
30
 Micro-calcifications: Tiny deposits of calcium.
 Most women have one or more areas of micro-calcifications of various sizes.
 Majority of calcium deposits are harmless and a small percentage may be precancerous or cancerous
(benign).
 Some of the cells begin growing abnormally and may spread through the breast, to the lymph or to
other parts of the body (malignant).
 Common type of breast cancer begins in the milk-production ducts, but cancer may also occur in the
lobules or in other breast tissue.

31
Author Method Findings Limitations
Naseera et al.
(2016)
Adaptive histogram equalization
technique followed by Water
shed Segmentation
Better classification is realized
for benign and malignant
tissues.
Size and area of masses are not calculated
Singh et al.
(2015)
Max-Mean and Least-Variance
technique
Segmenting the Cancer Region Manual selection of threshold parameter and
size of averaging filter.
Hu et al. (2014) Adaptive global & local
threshold
segmentation.
Suspicious Lesions are detected
and analyzed.
 Other combinations of lesion feature are not
considered.
 Higher False negative rate is found.
Massich et al.
(2014)
Gaussian and
Seed region growing method.
Lesion are identified and
segmented.
Does not address the problem of segmentation
other non-lesion structures.
Quintana et al.
(2013)
Histogram techniques and
Canny algorithm.
Automatic micro calcification is
recognized in mammographic
images.
Does not perform satisfactorily due to non
uniform background.
 Higher FP rate is achieved.
Few Important Contributions

32
Author Method Findings Limitations
Minavathi et al.
(2012)
Gaussian and
Anisotropic diffusion
filter.
Mass in breast are identified and
Classified .
Blurred images are obtained.
Incorrect identification of boundary obtained.
Lei et al.
(2011)
Watershed transform Masses are segmented. Over-segmentation.
Jumaat et al.
(2011)
Median filter and
Balloon snake
Cancers or non-cancerous masses are
classified and their boundaries are
identified.
Lower degree of accuracy.
 Higher false negative rate.
Maitra et al.
(2011)
Divide and Conquer,
seeded Region growing
Algorithms
Abnormal Masses are detected. No mathematical background involved to measure the
mass, shape, size, position and density of masses.
Mohideen et
al. (2011)
Multi wavelet method Microcalcifications and suspicious
structures are detected.
Improvement of local details required in low contrast
regions.
Luo et al.
(2010)
Support vector machine. Breast Masses are segmented and
diagnosed.
Density of mass is not properly achieved.
contd..

Author Segmentation
Feature Extraction
(TP,FP,TN, TP)
Size and shape
define
Cancer Stage
Identification
Accuracy
(Sensitivity
Specificity)
Kai Hu et al.(2014) Yes Yes No No Yes
Kowal et al. (2011) Yes No Yes No No
Maitra et al.(2011) Yes No No No No
Nithya, et al. (2011) No Yes No No Yes
Shekhar et al. (2011) No Yes yes Yes No
Quintana et al. (2011) Yes Yes No No Yes
Liu et al. (2010) Yes Yes No No Yes
Bick et al. (2009) Yes No No No No
Bator et al. (2009) Yes Yes No No No
Yuji Ikedo et al. (2007) No Yes No No Yes
Kom et al (2007) Yes No No No No
Madabhushi et al. (2003) Yes Yes No No No
Lei et al. (2001) Yes Yes No No Yes
33
Comparison

34
 Breast positioning.
 Underexposure of images.
 Blurring and speckle noise
 Fatty breasts.
 Smaller malignancies.
 Boundary identification.
Problems

Input Image
De-noise the image
Perform Segmentation
Refine the Segmentation
Obtained Resulting
Mammographic Image
Size and shape identification
Feature Extraction
Feature Classification
Pre-processing Step
Segmentation Step
Post-processing Step
(CAD Evaluation)
35
Work-Flow

 Low Pass Filter(LPF)
 High Pass Filter(HPF)
 Histogram Equalization(HE)
 Adaptive Histogram Equalization(AHE)
 Global Histogram Equalization(GHE)
 Local Histogram Equalization(LHE)
 Contrast Limited Adaptive Histogram Equalization(CLAHE)
 Gray Level Grouping(GLG)
36
Pre-processing

37
Methods Property Drawback
LPF Keeps low intensity Value Blurring and Ringing effect
HPF Keeps High Intensity Value
It may not give cutoff frequency for the application you
need.
HE Operates in global contrast of the image
Global methods have both over-enhancement and under-
enhancement problems.
AHE
Adjusts image intensity in small regions in the
image
Wash out effect , introduces artifacts and losing out the
image details.
GHE Histogram information of the entire input image
It fails to adapt with the local brightness features of the
input image
LHE
Uses a small window that slides through every
pixel of the image sequentially
Produce an undesirable checkerboard effects on
enhanced images
CLAHE
Limiting the local contrast-gain by restricting the
height of local histogram
Over enhancement which results in the loss of some local
information
GLG
Group the histogram components of a low-
contrast image into a proper number of groups
The degree of enhancement is not that much significant.
Comparison

(a) Original Image, (b) LPF, (c ) HPF, (d) HE, (e) LHE ,(f) GHE, (g)CLAHE,(h) GLG
(a) (b) (c) (d)
(f) (g) (h)
38
(e)
Results

 Mass segmentation
 Pixel based segmentation methods
 K-means clustering
 Threshold based
 Adaptive thresholding
 Region based segmentation
 Split and merge
 Region growing
39
Segmentation

(a)Original image, (b) K-Means, (c) Thresholding ,(d) Adaptive thresholding ,(e) Split and Merge, (f) Region Growing .
(a) (c)(b)
(d) (e) (f)
40
Results

41
(a) (b) (c)
(a) circular, (b) lobular and (c) speculated.
Masses

42
TX: The mass cannot be assessed.
T0: Evidence of tumor is absent.
Tis: The cancer may be.
T1: The mass is 2 cm or smaller in diameter.
T2: The mass is 2-5 cm in diameter.
T3: The mass is more than 5 cm in diameter.
Cancer Stage

43
Left Group
Gray Level 0 1 2 3 4 5 6 7
Amplitude
× 6 × 1 3 5 × 9
× 6 × 4 5 × 9
× 6 × 9 × 9
× 15 × × 9
× 24 × ×
Right Group
0 1 2 3 4 5 6 7
× 6 × 1 3 5 × 9
× 6 × 4 5 × 9
× 6 × 9 × 9
× × 15 × 9
× × × 24
6
1
3
5
9
0
2
4
6
8
10
0 1 2 3 4 5 6 7
Ampltude
Gray Level
15
4 4
9
24
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7
Ampltude
Gray Level
Modified GLC-CE

Methods Statistical Parameter
Non Cancerous Images
GRSDB-1 GRSDB-8 GRSDB-14
AMF
PSNR 16.889 16.645 15.396
RMSE 07.527 08.402 06.701
CNR 23.244 23.476 24.735
NAE 00.113 00.061 0.0764
HBF
PSNR 18.645 17.477 17.888
RMSE 08.556 07.479 07.841
CNR 29.485 30.653 30.242
NAE 00.088 00.064 00.062
CLAHE
PSNR 23.136 23.334 22.516
RMSE 08.372 09.868 08.027
CNR 34.994 33.796 33.614
NAE 00.278 00.203 00.221
GLC
PSNR 26.243 27.524 27.432
RMSE 08.235 08.984 08.342
CNR 37.241 36.862 37.563
NAE 00.354 00.284 00.431
GLC-CE (α=0.6)
PSNR 25.776 25.117 25.202
RMSE 07.907 08.332 07.267
CNR 35.534 35.365 35.185
NAE 00.245 00.189 00.223
44
Comparison

45
Methods Statistical Parameter
Cancerous Images
GRSDB-19 GRSDB-23 GRSDB-31
AMF
PSNR 25.525 24.768 25.426
RMSE 06.764 07.657 08.514
CNR 26.605 25.568 26.364
NAE 00.048 00.065 00.086
HBF
PSNR 32.674 33.546 33.736
RMSE 06.756 06.486 07.253
CNR 16.763 17.342 16.341
NAE 00.047 00.075 00.094
CLAHE
PSNR 39.352 38.413 39.697
RMSE 08.257 08.873 07.364
CNR 40.596 39.654 40.974
NAE 00.278 00.263 00.254
GLC
PSNR 42.342 42.624 42.285
RMSE 08.534 08.874 07.543
CNR 43.126 43.075 44.453
NAE 00.325 00.267 00.287
GLC-CE (α=0.6)
PSNR 40.112 40.193 41.285
RMSE 07.883 08.115 07.142
CNR 41.262 40.052 42.184
NAE 00.217 00.213 00.231
contd..

46
Method Used /
Database
GRSDB-19 GRSDB-23 GRSDB- 31
Original
Image
K-Means
ATS
RGS
GLC-CE Subjected to Segmentation

Extracted Masses from GRSDB-14 (Non-cancerous Image)
47
Mass Segmentation (GLC-CE &ATS)

Extracted mass from GRSDB-14 (Cancerous Image)
48
Contd..

ATS with GLC-CE for GRSDB-14
S.N. Mass # Mean
Result obtained by CAD
Area (mm2) Perimeter (mm) Diameter (mm)
1. 15 252.9 0. 668 2.332 0.90
2. 23 253.0 0.352 2.353 0.64
3. 26 224.9 0.159 2.341 0.48
4. 61 253.0 0.344 2.469 0.53
5. 64 237.3 0.662 2.499 0.91
6. 81 236.6 0.412 2.610 0.77
7. 82 252.7 0.227 2.581 0.47
49
S.N. Mass # Mean
Result obtained by CAD
Area (mm2) Perimeter (mm) Diameter (mm)
1. 5 253.3 0.125 1.889 0.36
2. 7 252.7 0.102 1.929 0.34
3. 8 252.6 0.364 2.067 0.67
4. 10 253.0 0.366 2.256 0.62
5. 11 253.0 0.384 2.335 0.81
6. 14 252.5 0.131 2.344 0.38
7. 47 241.8 0.216 2.603 0.55
ATS with CLAHE for GRSDB-14
Features

S.
N.
Mass # Mean
Result obtained by CAD Result by Radiologist Area
Diff.
[A-B]
Area (mm2)
[A]
Perimeter
(mm)
Diameter
(mm)
Area (mm2)
[B]
Peri- meter
(mm)
Diameter
(mm)
1. 5 244.3 7.90 23.7 12.74 7.1 23.2 12.30 + 0.80
2. 8 248.5 11.3 33.9 18.23 11.5 34.5 18.76 - 0.20
3. 9 253.2 8.33 24.99 13.44 8.53 25.32 13.75 - 0.20
4. 32 252.0 5.23 15.69 8.44 5.2 15.46 8.28 + 0.03
5. 36 253.5 4.40 13.2 7.10 4.21 13.64 7.41 + 0.19
6. 56 210.2 2.43 7.29 3.92 2.33 7.68 4.32 + 0.10
7. 59 245.9 3.32 9.96 5.35 3.41 9.84 5.22 - 0.09
8. 64 252.5 3.50 10.5 5.65 3.46 10.72 5.81 + 0.04
9. 78 252.9 6.31 18.93 10.18 6.35 18.60 10.02 - 0.04
10. 83 225.0 2.67 8.01 4.31 2.6 8.27 4.57 + 0.07
50
ATS with GLC-CE for GRSDB-19
contd..

51
Image
Preprocessing
Methods
Segmentation
Methods
Range of area difference (mm2)
Positive Negative
GRSDB-17
CLAHE
ATS 0.23 - 1.89 0.14 - 0.23
RGS 0.54 - 3.69 0.17 - 2.32
GLC-CE
ATS 0.18 - 1.25 0.15 - 0.22
RGS 0.75 - 2.63 0.65 - 2.74
GRSDB-18
CLAHE
ATS 0.29 - 1.65 0.15 - 0.29
RGS 0.76 - 3.54 0.19 - 2.62
GLC-CE
ATS 0.17 - 1.45 0.13 - 0.21
RGS 0.83 - 2.42 0.86 - 2.69
GRSDB-29
CLAHE
ATS 0.23 - 1.43 0.14 - 0.32
RGS 0.61 - 2.69 0.11 - 2.17
GLC-CE
ATS 0.19 - 0.93 0.09 - 0.28
RGS 0.31 - 2.54 0.41 - 1.73
Comparison

Image
Result
by CAD
Result by
Radiologist
Image
Result by
CAD
Result by
Radiologist
GRSDB-1 Tx T0 GRSDB-22 T1 T1
GRSDB-2 T0 T0 GRSDB-23 T2 T2
GRSDB-3 T0 Tx GRSDB-24 T1 T1
GRSDB-4 Tx Tx GRSDB-25 T1 T1
52
Cancer Stage

53
Database TP FP FN TN Sensitivity% Specificity% Accuracy%
GRSDB-17 52 3 2 39 94.55 95.12 94.79
GRSDB-18 49 2 1 41 96.08 97.62 96.77
GRSDB-19 38 1 2 40 97.44 95.24 96.30
GRSDB-20 44 2 2 37 95.65 94.87 95.29
GRSDB-21 40 1 2 50 97.56 96.15 96.77
GRSDB-22 42 3 1 38 93.33 97.44 95.24
GRSDB-23 37 1 2 53 97.37 96.36 96.77
GRSDB-24 38 2 2 42 95.00 95.45 95.24
GRSDB-25 43 2 2 47 95.56 95.92 95.74
GRSDB-26 41 1 3 49 97.62 94.23 95.74
GRSDB-27 45 2 1 45 95.74 97.83 96.77
GRSDB-28 44 2 2 46 95.65 95.83 95.74
GRSDB-29 51 3 2 39 94.44 95.12 94.74
GRSDB-30 47 2 2 43 95.92 95.56 95.74
CAD Performance

Authors Methods Sensitivity % Specificity % Accuracy %
Lei et al. (2001) AI with DWT 97.3 96 95.4
Jumaat et
al.(2010) Balloon Snake Not Calculated Not Calculated 95.53
Rangayyan et al.
(2007)
Gabot filter and curvilinear
structures 92 95.4 86.7
Mencattini et
al. [(2010) Luminance-region based approach 92.6 96.6 95.12
Bator et al.
(2009) Linear Structure method 90-95 88-92 Not
Calculated
Kai Hu et al.
(2011)
Adaptive global and local
thresholding segmentation method 91.73 89.8 94.5
Jun Liu et al.
(2010)
Anisotropic diffusion filter and
watershed method 95 94 96
Minavathi et al
(2012)
Gaussian smoothing and Active
contour method 92.7 90.3 94
Madabhushi et
al . (2014)
Second-order butterworth filter and
region growing method 80 82 81.4
Proposed CAD
system GLC-CE-ATS 97.55 96.37 96.75
54
Validation

Health Informatics

 Health informatics, also known as medical informatics, deals with the methods or devices
that are used to acquire, store, retrieve, and use information in the health care sector.
 Technology enables a health care provider to keep electronic medical records for billing,
scheduling, and research.
Health Informatics
56

 The use of hand-held or portable devices to assist the healthcare provider with data
entry or medical decision.
 ‘mHealth’ is a developing technology in the health informatics field.
contd…
57

 Clinical informatics focuses on computer applications for all types of medical data and
knowledge that may be collected, organized, analyzed, stored , and used in a medical
clinic.
 Digitalized images are used in the practices of cardiology, dermatology, surgery,
obstetrics, gynecology and pathology.
58
contd..

Feature Extraction
Similarity Retrieval
Image FeaturesImage Database
Query Image
Query Results
Feedback
Algorithm
User Evaluation
59
CBMIR

60
Inspiring Equation
E= mc2
E = Excellence m= Motivation c=Commitment
Example: c= 0.5 (half hearted), E= ¼ & c= 2 (doubly committed), E= 4
Thank you, any queries please!

Medical image processing

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Medical image processing

Similar to Medical image processing (20)

More from Dr G R Sinha

More from Dr G R Sinha (16)

Recently uploaded

Recently uploaded (20)

Medical image processing