Intelligent Medical Imagingdl2.cs.ui.ac.id/files/Keynote_Speech_SNATI_2008.pdf · 2011. 12. 21. ·...
Transcript of Intelligent Medical Imagingdl2.cs.ui.ac.id/files/Keynote_Speech_SNATI_2008.pdf · 2011. 12. 21. ·...
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Intelligent Medical Imaging
Prof. Dr. Ir. Ahmad Fadzil M. H.
© Ahmad Fadzil
Intelligent Signal & Imaging Research ClusterDepartment of Electrical & Electronics EngineeringUniversiti Teknologi PETRONASThursday, 20 June 2008
© 2008 INSTITUTE OF TECHNOLOGY PETRONAS SDN BHDAll rights reserved. No part of this document may be reproduced, stored ina retrieval system or transmitted in any form or by any means (electronic,mechanical, photocopying, recording or otherwise) without the permission ofthe copyright owner.
Universiti Teknologi PETRONAS
© Ahmad Fadzil
www.utp.edu.my
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UTP
© Ahmad Fadzil
Outline
Intelligent Signal and Image Processing Research Cluster1
Intelligent Medical Imaging Research
Intelligent Medical Imaging Research in Vitiligo
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3
© Ahmad Fadzil
Intelligent Medical Imaging Research in Psoriasis4
Intelligent Medical Imaging Research in Diabetic Retinopathy5
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The Intelligent Signal and Imaging Research Group
The Intelligent Signal and Imaging Research Group is one of the leading intelligent signal and imaging research cluster in Malaysia.
© Ahmad Fadzil
Digital elevation model analysis
(Remote sensing)
Research
Seismic data analysis(Geoscience)
Medical imaging l i
Medical signal anal sis areas analysis
(Skin and eye-related diseases)
analysis(Heart
diseases,VEP)
Approaches
Colour space
analysis
Independent Component
Analysis
Principal Component
Analysis
Morphological filter
Fractal analysis
Computer Vision, Signal and Image ProcessingComputer Vision, Signal and Image Processing
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Outline
Intelligent Medical Imaging Research 2
1
Intelligent Medical Imaging Research in Vitiligo3
Intelligent Signal and Image Processing Research Cluster
© Ahmad Fadzil
Intelligent Medical Imaging Research in Psoriasis4
Intelligent Medical Imaging Research in Diabetic Retinopathy5
Medical Imaging - Overview
Medical imaging refers to the techniques and processes used to
Currently medical imaging is limited to the acquisition of imagesof the human organs/ body
create images of the human body for clinical purposes (medicalprocedures seeking to reveal, diagnose or examine disease).
Medical imaging can be seen as the solution of mathematicalinverse problems. This means that cause (the properties of livingtissue) is inferred from effect (the observed signal)
© Ahmad Fadzil
Analysis of the images obtained is performed clinically by experts
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Medical Imaging - Technology
Gamma ray : positron emission tomography (PET) a short-lived isotope, such as 18F, is incorporated into a substance used by the body such as glucose
X ray : computed tomography (CT)
which is absorbed by the tumor of interest
Expose to x-ray radiation, repeated scans must be limited to avoid health effects
© Ahmad Fadzil
Magnetic Resonance Imaging (MRI)
Medical Imaging - Technology
uses powerful magnets to polarise and excite hydrogen nuclei (single proton) in y g ( g p )water molecules in human tissue, producing a detectable signal which is spatially encoded resulting in images of the body
excellent soft-tissue contrast
no known long term effects of exposure to strong static fields
© Ahmad Fadzil
health risks associated with tissue heating from exposure to the RF field and the presence of implanted devices in the body, such as pace makers
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Medical Imaging - Technology
Ultrasound : ultrasonography H-F sound, 2-10MHz, safe, 2D moving images
Visible light : camera
© Ahmad Fadzil
Issues, Challenge and Approach
Issues
• Harmful (radiation, contrast agent)Challenges
T d l i t lli t di l• Specialized device – difficult to use - highly
trained operator needed
• Expensive (Initial cost, Maintenance)
• Image Acquisition only, little or no analysis for diagnostic purposes, subjective
To develop intelligent medical imaging system which is objective in analysis that is safe to the patients.
© Ahmad Fadzil
g p p , j
Approach
From medical imaging (image acquisition with enhancement) to medical imageanalysis (feature extraction, classification, pattern recognition, measurements)resulting in intelligent imaging (decision support systems)
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Ophthalmology (Eye)
Current Research in Intelligent Medical Imaging at UTP
Dermatology (Skin)
Vitiligo
Diabetic R i h
© Ahmad Fadzil
Psoriasis
Retinopathy
Outline
Intelligent Medical Imaging Research in Vitiligo3
1
Intelligent Medical Imaging Research2
Intelligent Signal and Image Processing Research Cluster
© Ahmad Fadzil
Intelligent Medical Imaging Research in Psoriasis4
Intelligent Medical Imaging Research in Diabetic Retinopathy5
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Vitiligo - Introduction
Vitiligo is a skin disease where irregular white spots appear in the skin invarious size and location. The spots occur when pigment cells (melanocytes)are damaged and the pigment melanin can no longer be produced
Melanin pigments can no longer be produced
Melanocytes damaged
are damaged and the pigment melanin can no longer be produced.
© Ahmad Fadzil
The effect of vitiligo can be very considerable on the patients’psychological condition and patients with vitiligo have an increasedrisk of developing autoimmune diseases.
Vitiligo – Issues and Challenge
Issues• Efficacy assessment largely dependent on the human eye and judgment to
produce the scorings. • Dermatologists find it visually hard to determine the areas of skin
repigmentation due to the slow progress and as a result the observations are made over a longer time frame
Repigmentation3 months
treatment (ointment,
© Ahmad Fadzil
phototherapy -UV & laser)
Patient 3
Challenge:How to we develop an image analysis scheme that determines the non-melaninskin areas (corresponding to vitiligo skin areas) and repigmentation areasobjectively over a shorter time frame?
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Vitiligo - Approach
We use mathematical techniques to extract information of the skin histology from the digitalimages
95% of the incident light (250–700nm)penetrates into skin and follows a complexpath until it exits back out of the skin orgets attenuated by skin choromophores.
© Ahmad Fadzil
Using principal component analysisfollowed by independent componentanalysis, we can decompose RGB skinimage into melanin and haemoglobinspatially.
Principal Component Analysis(PCA)
RGB Digital Images
Vitiligo - Approach
Using the specific skin histology obtained from PCA/ICA, wecan determine vitiligo areas objectively.
(PCA)
Independent Component Analysis (ICA)
Thresholding based on Euclidean distance Extracting skin
histology
Original RGB image
© Ahmad Fadzil
Area Measurement
Repigmentation Measurement
Therapeutic Response Due to Treatment
Melanin component
Haemoglobin component
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Vitiligo – PCA/ICA
RGB PCA ICA
To transform observed image data (R,G,B) into (maximally independent) components of skin (melanin, haemoglobin)
xR,yG,zB
‐log (b)
‐log (g)
RGB PCA ICA
xC1,yC2
‐log (r)
‐log (b)
‐log (g)
xM,yH
skin colour distribution
haemoglobin
‐log (b)
‐log (g)
melanin
© Ahmad Fadzil
‐log (r)
* *
log (r)‐log (r)
Vitiligo - Benefits
The system is currently being used in Hospital Kuala Lumpur for clinical trialin efficacy assessment of therapeutic treatment .
• The digital image analysis system has been able to determine thetherapeutic response of the vitiligo treatment (repigmentation areas)objectively in a shorter time, hence the efficacy assessment of thetreatment . This will enable dermatologists to accordingly performaccurate procedures in a shorter time period.
© Ahmad Fadzil
Demo
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1. Hermawan Nugroho, Ahmad Fadzil M.H., “Skin Colour Segmentation using Principal Component Anaylsis”, Proceeding 3rd International Colloquium on Signal Processing and its Application, March 9 – 11, 2007, Melaka, Malaysia
Vitiligo - Reference
2. Hermawan Nugroho, Ahmad Fadzil M.H., S. Norashikin, H.H. Suraiya, “Computer Aided Vitiligo Monitoring using Independent Component Analysis” Proceeding International Conference on Biotechnology Engineering (ICBioE 2007), May 8 – 10, 2007, Kuala Lumpur, Malaysia
3. Hermawan Nugroho, Ahmad Fadzil M.H., S. Norashikin, H.H. Suraiya, “Determination of Skin Repigmentation Progression”, Proceeding 29th Annual International Conference of the IEEE (Institute of Electrical and Electronics Engineering) Engineering in Medicine and
© Ahmad Fadzil
the IEEE (Institute of Electrical and Electronics Engineering), Engineering in Medicine and Biology Society (EMBS) 2007, August 23-26, 2007, Lyon, France
4. M. H. Ahmad Fadzil, Hermawan Nugroho, S. Norashikin, H. H. Suraiya, “Assessment of Therapeutic Response in Skin Pigment Disorder Treatment”, 3rd International Symposium on Information Technology 2008 (ITSiM08), Kuala Lumpur.
Outline
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Intelligent Medical Imaging Research2
Intelligent Signal and Image Processing Research Cluster
Intelligent Medical Imaging Research in Vitiligo3
© Ahmad Fadzil
Intelligent Medical Imaging Research in Psoriasis4
Intelligent Medical Imaging Research in Diabetic Retinopathy5
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Psoriasis - Introduction
Psoriasis is a chronic inflammatory, non-contagious skin disorder which typically consists of red plaques covered by silvery-white
Although psoriasis is an incurable disease, there are many available treatments to control the symptoms of psoriasis
consists of red plaques covered by silvery white scales
Plaque psoriasis is the most common form of psoriasis (80%)
© Ahmad Fadzil
the symptoms of psoriasis
However, there is no single treatment that works for every case.
Dermatologist should monitor the extent of psoriasis continuously to assess the treatment efficacy
PASI (Psoriasis Area & Severity Image)
It assesses four body region : head trunk
PASI is the gold standard method to assess the extent of psoriasis and in treatment efficacy.
It assesses four body region : head, trunk, upper extremities, and lower extremities.
For each body region, the surface area involved, erythema(redness), thickness, and scaliness of the lesion are determined. 2
2
1 1 3
3 4 3
1
6( ) ( )PASI 0.1 0.2R T S A R T S Au u u uh h h h= + + + + +
© Ahmad Fadzil
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3
4 4 4
3 4 3
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6
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( ) ( )
( ) ( )0.3 0.4
u u u uh h h h
R T S A R T S At t t t l l l l+ + + + + +
A = area (0 – 6), R = redness (0 – 4), T = thickness (0 – 4), S = scaliness (0 – 4), h = head, u = upper extremities, t = trunk, l = lower extremities.
The treatment is considered effective when the initial PASI score is reduced by 75 %
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Psoriasis - Issues
The PASI score varies between dermatologists (Inter-observer variation) and during repeat visits by patients, inconsistent PASI scoring by the same dermatologist can occur (Intra-observer variation)
Calculating area of lesion
Low accuracy
Tedious
Measuring thickness & scaliness
Thickness & scaliness are determined by tactile inspection
Inconvenience and subjective
© Ahmad Fadzil
Determining degree of redness
Degree of redness is affected by patient’s skin colour
Objective Assessment of Psoriasis Area
The appearance of psoriasis lesion vary between each patient it is affectedvary between each patient, it is affected by their normal skin colour
However, human visual system is able to identify the lesion based on colour difference with the surrounding normal skin
Challenge
© Ahmad Fadzil
How to quantify and emulate the ability of human visual system in differentiating the colour of psoriasis lesion from normal skin?
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Objective Assessment of Psoriasis Area - Approach
We convert from RGB to CIELAB colour space which is linear to calculate colour difference between healthy skin and psoriasis lesion
© Ahmad Fadzil
Each colour is represented by L*, a*, and b*L* = degree of lightnessa* = degree of greenness to rednessb* = degree of blueness to yellowness
Objective Assessment of Psoriasis Area - Approach
Calculating colour difference (Hue difference (∆hab), chroma difference (∆Cab) and Lightness difference (∆L*)) between healthy skin and psoriasis lesion in the CIELAB colour space L* = 100L* = 100L 100
+a*
+b*
-a*
P1
P2
L*1
L*2
Cab1Cab2 hab1
hab2
L 100
+a*
+b*
-a*
P1
P2
L*1
L*2
Cab1Cab2 hab1
hab2
© Ahmad Fadzil
-b*
L* = 0
-b*
L* = 0
* * *1 2
1 2
* * *1 2
ab ab ab
ab ab ab
L L L
h h h
C C C
∆ = −
∆ = −
∆ = −
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Objective Assessment of Psoriasis Area - Approach
Change RGB image to CIELAB imageCIELAB image
Calculate colour difference Between healthy normal skin and lesion
Select samples of normal skin and lesion
Normal healthy skin
Lesion
© Ahmad Fadzil
Between healthy normal skin and lesion
Pixel classification into lesion and normal skin
Healed lesion
Objective Assessment of Psoriasis Erythema
Konica Minolta Chromameter CR-400 is an
For accurate measurement of skin and lesion colour, we use the chromameter
instrument to measure colour by modeling
characteristic of light source, spectral
reflectance of the object, and colour vision
of human eye.
Hue = dominant wavelength of a colour
© Ahmad Fadzil
Chroma = saturation of a colour
( )1 * *tan /abh b a−=
* *2 *2abC a b= +
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Objective Assessment of Psoriasis Erythema - Approach
f
Normal skin
Erythema (redness) is dependent on the patient’s normal skin tone
Patients are classified into three skin tones (dark, brown, fair) based on their L* values
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40
45
50
55
60
65
Skin group
L*
Fair skin
Brown skin
Dark skin
© Ahmad Fadzil
Fair Brown Dark
Objective Assessment of Psoriasis Erythema - Approach
The human visual system perceive colour differences based on the combination of differences in the hue, saturation (chroma) and lightness. We investigate the effects of these parameters .
Fair skin
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101214
*
score1
Erythema score linearly correlates with the hue difference (∆hab) for all skin tones
© Ahmad Fadzil
-4-20246
-5 0 5 10 15 20 25 30 35
∆hue
∆L*
score2score3
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Objective Assessment of Psoriasis Thickness & Scaliness
Measuring Method: Triangulation light block method
Data Acquisition :Konica Minolta VIVID 910 N t t 3D Di iti
In order to assess thickness and scaliness , we aquire 3D images of the lesion
method
Scan Range: 0.6 to 1.0 m (In Standard mode)0.5 to 2.5 m (In Extended mode)
Laser Scan Method: Galvanometer-driven rotating mirror
Accuracy : ±0.05 mm
Non-contact 3D Digitizer
© Ahmad Fadzil
y
Precision : 0.008 mm
Output Format 3D data: Konica Minolta format, & (STL,DXF, OBJ,
ASCII points, VRML)Color data: RGB 24-bit raster scan data
Objective Assessment of Psoriasis Thickness & Scaliness
Data Acquisition
© Ahmad Fadzil
3D imagePsoriasis lesion
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Objective Assessment of Psoriasis Thickness & Scaliness
Approach
Surfaces of the psoriasis lesions are digitized using 3D laser scannerSurfaces of the psoriasis lesions are digitized using 3D laser scanner .
Depth information of the 3D psoriasis lesion are converted into 2D grayscale image.
© Ahmad Fadzil
Achievements
The lesion segmentation method was applied on images of 8 patients with various skin colour. Our segmentation method achieves accuracies higher than 90%. The error occurs mainly at the border of the lesion due to colour gradation between normal skinoccurs mainly at the border of the lesion due to colour gradation between normal skin and psoriasis lesion.
The erythema score of a lesion can be accurately determined by the hue difference (∆hab ) within a particular skin type group.
The proposed method has the potential to assess erythema objectively and consistently without being influenced by other characteristic of the lesion such as area, pattern, and boundary.
© Ahmad Fadzil
The objective assessments of thickness and scaliness are currently being investigated
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Research Collaboration (UTP-Dermatology Dept, Hospital Kuala Lumpur
© Ahmad Fadzil
Psoriasis - Reference
1. M. H. Ahmad Fadzil, Dani Ihtatho, M. A. Azura, H. H. Suraiya, “Objective Assessment of Psoriasis Erythema for PASI Scoring”, 30th Annual International Conference of the IEEE EMBS 2008, Vancouver, Canada, 2008.
2. M. H. Ahmad Fadzil, Dani Ihtatho, “Modeling Psoriasis Lesion Colour for PASI Erythema Scoring”, 3rd International Symposium on Information Technology 2008 (ITSiM08), Kuala Lumpur, Malaysia.
3. Hermawan Nugroho, Naz-e-Batool, M. H. Ahmad Fadzil, P. A. Venkatachalam, “Surface Analysis of Psoriasis for PASI Scaliness Assessment”, Proceedings International on Intelligent and Advanced Systems (ICIAS2007), Kuala Lumpur, Malaysia.
© Ahmad Fadzil
4. Dani Ihtatho, M. H. Ahmad Fadzil, M. A. Azura, H. H. Suraiya, “Automatic PASI Area Scoring”, Proceedings International on Intelligent and Advanced Systems (ICIAS2007),Kuala Lumpur, Malaysia.
5. Dani Ihtatho, M. H. Ahmad Fadzil, M. A. Azura, H. H. Suraiya, “Area Assessment of Psoriasis Lesion for PASI Scoring”, Proceedings of the 29th Annual International Conference of the IEEE EMBS 2007, Lyon, France.
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Outline
1
Intelligent Medical Imaging Research2
Intelligent Signal and Image Processing Research Cluster
Intelligent Medical Imaging Research in Vitiligo3
© Ahmad Fadzil
Intelligent Medical Imaging Research in Diabetic Retinopathy5
Intelligent Medical Imaging Research in Psoriasis4
What is Diabetic Retinopathy?
Diabetic retinopathy is retinopathy (damage to the retina) caused by complications of diabetes mellitus, which could eventually lead to blindness.
Courtesy NIH National Eye Institute
Affects up to 80% of all diabetics who have had diabetes for 10 years or more.
After 20 years of diabetes, nearly all patients with type
At least 90% of new cases could be reduced if there was proper and vigilant treatment and monitoring
© Ahmad Fadzil Source: wikipedia
The same view with diabetic retinopathy
nearly all patients with type 1 diabetes (juvenile onset) and >60% of patients with type 2 diabetes (adult onset)have some degree of retinopathy.
of the eyes.
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DR Pathologies
hemorrhageshemorrhages
Small blood vessels in the eye are especially vulnerable to poor blood sugar control. An overaccumulation of
© Ahmad Fadzil
ModPDR (dataset2 - NPDR2)
An overaccumulation of glucose damages the tiny blood vessels in the retina. During the initial stage, called nonproliferative diabetic retinopathy (NPDR), most people do not notice any changes in their vision.
DR Pathologies
exudates
hemorrhages
Macula region
Some people develop a condition called macular edema. It occurs when the damaged bl d l l k fl id d
© Ahmad FadzilProliferative DR (dataset1 - PDR2)
blood vessels leak fluid and lipids onto the macula, the part of the retina that lets us see detail. The fluid makes the macula swell, which blurs vision.
Without timely treatment, these new blood vessels can bleed, cloud vision, and destroy the retina – PDR stage
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Diabetic Retinopathy
Analysing fundus image can show severity of DR
© Ahmad Fadzil
Disease of the retina as a complication of diabetes mellitus.
Characterized by the progressive microvascular complications.
Normal
What do the doctors do?
Eye examination Visual acuity testOphthalmoscopyOcular Coherence Tomography
(1) leaking blood vessels, (2) retinal swelling, such as macular edema(3) pale, fatty deposits on the retina (exudates) – signs of leaking blood vessels
g p y
The ophthalmologist will look at the retina for early signs of the disease
© Ahmad FadzilSource: www.retinalscreening.nhs.uk
of leaking blood vessels(4) damaged nerve tissue (neuropathy)(5) any changes in the blood vessels.
signs of the disease
To allow the doctor to find the leaking blood vessels a test called fluorescein angiography is performed. In this test, a special dye is injected into the arm.
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Issues, Challenges, Approaches
Issues
Diabetes mellitus affect ~10%
Challenges
1 C d l i &Diabetes mellitus affect ~10% population (DR is a real concern -epidemic stage?)
Needs access to ophthalmologist with fundus camera equipment
Grading severity of DR
1. Can we develop a screening & grading system to be made accessible to all diabetes patients?
2. Can we detect DR early even before patient have visual problems?
3. Can we make non-invasive
© Ahmad Fadzil
Low contrast Fundus images requiring Fluorescein angiography -an invasive procedure
procedure as effective?
Fundus camera technology+
Image Processing & Computer Vision
1Severity of DR related to retinal capillary
2Very low contrast of retinal blood vessels in fundus
3Fundus fluourescein angiography to Issues
Imaging Issues in Diabetic Retinopathy
occlusion image enhance the contrast
Challenge
© Ahmad Fadzil
Original image (source: DRIVE)Fundus FA (Source: Selayang hospital)
How to enhance the contrast of blood vessels using non-invasive technique?How to develop an intelligent system to support monitoring and grading of DR?
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Computerized Diabetic Retinopathy Monitoring and Grading Systems
An intelligent system for effective screening of DR and in assisting ophthalmologist make accurate diagnosis without resorting to invasive methods.
© Ahmad Fadzil
Grading of DR severity
Kowa nonmyd 7Kowa VX 10i
Capabilities of Computerized DR System
Challenges
1.Enable effective screening
Identification of those individuals who may be
Can we develop a screening & grading system to be made accessible to all diabetes patients?
Can we detect DR early even before patient have visual
yat risk of developing DR and in assisting ophthalmologist make accurate diagnosis (identification of DR conditions by means of its pathologies and symptoms) without resorting to invasive methods.
2. Grading the severity and progression of DR
© Ahmad Fadzil
before patient have visual problems?
Can we make non-invasive procedure as effective?
GradingNormal Mild Non Proliferative DR (NPDR)Moderate NPDRSevere NPDRPDR
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Segmentation of Retinal Blood Vessels - Approach
Fundus image Enhancement Extraction
Colour fundus image is processed and analyzed to monitor the severity of DR.
Fundus image Enhancement Extraction
© Ahmad Fadzil
Contrast limited adaptive histogram equalization (CLAHE) and mathematical morphology are used to enhance the contrast and extract retinal blood vessels.
Research & Development
To avoid invasive FA, we developed novel imaging solution to the low and varying contrast Fundus imagesy g g
a Fundus image (Green band) b H l bi l t d ta Fundus image (Green band) b H l bi l t d t
a. Fundus image after homomorphic filtering
b. First componenta. Fundus image after homomorphic filtering
b. First component
© Ahmad Fadzil
To detect DR early and monitor DR, we developed an alternative approach to the grading of DR
a. Fundus image (Green band) b. Haemoglobin-related componentFigure 5. Contrast enhancement of retinal blood vessels
a. Fundus image (Green band) b. Haemoglobin-related componentFigure 5. Contrast enhancement of retinal blood vessels
c. Second component d. Third componentc. Second component d. Third component
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Current research on contrast enhancement
Digital fundus images
Using independent component analysis, we decompose RGB fundus image into macular pigment, melanin and haemoglobin resulting in contrast enhancement of retinal blood vessels.
Digital fundus images
Red channel Green channel Blue channel
Independent component analysis (ICA)
Digital fundus image
Macular
© Ahmad Fadzil
Independent component analysis (ICA)
Macular pigment Haemoglobin Melanin
Retinal blood vessels
region
Contrast enhancement of retinal blood vessels
Achievements
Contrast enhancement of retinal blood vessels reduces the need of applying contrasting agent on patients.
System provides the map of retinal vasculature – able to detect free capillary zone and presence of new vessels growth.
Improve efficiency of DR screening and monitoring process.
© Ahmad Fadzil
The proposed method successfully enhances retinal vasculature with contrast enhancement factor of 3.15 and 3.2 using CLAHE and ICA, respectively, for digital retinal images.
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R&D Roadmap for Computerised DR Monitoring & Grading System
2007 2008
2008-2009 Clinical trialMarket surveys
2010 System available in market
© Ahmad Fadzil
2005Research started
2007 Patent SearchMOSTI Technofund grant RM1m
2007-2008 Prototype system (R&D Collaboration -UTP, VITROX, Hospital Selayang)Patent filing
Diabetic Retinopathy - References
1. Ahmad Fadzil M H, Lila Iznita I, P A Venkatachalam, T V N Karunakar: Extraction and reconstruction of retinal vasculature Journal of MedicalExtraction and reconstruction of retinal vasculature. Journal of Medical Engineering Technology 2007, 31(6):435-442.
2. Ahmad Fadzil M H, Hanung Adi Nugroho, P A Venkatachalam, Hermawan Nugroho, Lila Iznita I: Determination of Retinal Pigments from Fundus Images using Independent Component Analysis. Proceedings Biomed 2008 Conference 4th Kuala Lumpur International Conference on Biomedical Engineering. Kuala Lumpur: Springer; 2008.
3. Ahmad Fadzil M H, Hanung Adi Nugroho, P A Venkatachalam, Hermawan Nugroho, Lila Iznita I: Contrast Enhancement of Retinal Blood Vessels in
© Ahmad Fadzil
gDigital Fundus Image to be presented In: IASTED 8th Conference on VIIP 2008. Mallorca, Spain: IASTED; 2008.
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Researchers
© Ahmad Fadzil
Researchers
© Ahmad Fadzil
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TERIMA KASIH
© Ahmad Fadzil
THANK YOU© 2008 INSTITUTE OF TECHNOLOGY PETRONAS SDN BHDAll rights reserved. No part of this document may be reproduced, stored ina retrieval system or transmitted in any form or by any means (electronic,mechanical, photocopying, recording or otherwise) without the permission ofthe copyright owner.