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[email protected]@ugm.ac.id

Intelligent Medical Imaging for Breast Cancer Detection and

Diabetic Retinopathy

Hanung Adi Nugroho

Inovasi E-Health and Biomedika untuk Indonesia


Research and Development of Intelligent Medical Imaging

Profile

Dr. Ir. Hanung Adi Nugroho

Department of Electrical Engineering and Information TechnologyFaculty of Engineering, UNIVERSITAS GADJAH MADAJl. Grafika 2, Kampus UGM, Yogyakarta 55281, IndonesiaTelp./ fax. +62-274-552305Email: [email protected]; [email protected]

Research areas: Biomedical signal and image processing and analysis; computer vision; medical instrumentation; patternrecognition; data mining; statistical data analysis.

Bachelor of Engineering (S.T.) – Teknik Elektro, Universitas Gadjah Mada, Yogyakarta, Indonesia (2001)

Master of Engineering (M.E.) – School of Information Technology and Electrical Engineering, The University ofQueensland, St Lucia, Brisbane, Australia (2005)

Doctor of Philosophy (Ph.D.) – Electrical and Electronics Engineering Department, Universiti TeknologiPETRONAS, Seri Iskandar, Malaysia (2012)

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Intelligent Medical Imaging Research

Medical imaging - Overview

Currently medical imaging is limited to theacquisition of images of the human organs/ body

Medical imaging refers to the techniques andprocesses used to create images of the humanbody for clinical purposes (medical proceduresseeking to reveal, diagnose or examine disease).

Medical imaging can be seen as the solution ofmathematical inverse problems. This means thatcause (the properties of living tissue) is inferred fromeffect (the observed signal)

Analysis of the images obtained is performed clinically by experts



Medical imaging - TechnologyGamma ray : positron emission tomography (PET)

X ray : computed tomography (CT)

a short-lived isotope, such as 18F, is incorporated into a substance used by the body such as glucose which is absorbed by the tumour of interest

Expose to x-ray radiation, repeated scans must be limited to avoid health effects

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Magnetic resonance imaging (MRI)

uses powerful magnets to polarise and excite hydrogen nuclei (single proton) in 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

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



Medical imaging - TechnologyUltrasound : ultrasonography

Fundus cameraRetinal image

H-F sound, 2-10MHz, safe, 2D moving images

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Issues, challenge and approach

Issues

• Harmful (radiation, contrast agent)

• Specialized device – difficult to use -highly trained operator needed

• Expensive (Initial cost, Maintenance)

• Image Acquisition only, little or no analysis for diagnostic purposes, subjective

Issues

• Harmful (radiation, contrast agent)

• Specialized device – difficult to use -highly trained operator needed

• Expensive (Initial cost, Maintenance)

• Image Acquisition only, little or no analysis for diagnostic purposes, subjective

Approach

From medical imaging (imageacquisition with enhancement) tomedical image analysis (featureextraction, classification, patternrecognition, measurements) resultingin intelligent imaging (decisionsupport systems)

Approach

From medical imaging (imageacquisition with enhancement) tomedical image analysis (featureextraction, classification, patternrecognition, measurements) resultingin intelligent imaging (decisionsupport systems)

Challenge

To develop intelligent medical imaging system which is objective in analysis that is safe to the patients.

Challenge

To develop intelligent medical imaging system which is objective in analysis that is safe to the patients.



Current research in intelligent medical imaging system at DTETI-UGM

Ophthalmology (Diabetic retinopathy)Radiology (Breast cancer)

Diabetic retinopathy GlaucomaBreast cancer

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Intelligent Medical Imaging Research in Breast Cancer



Intelligent Medical Imaging Research in Breast CancerBreast Cancer

Breast cancer is a disease in which malignant(cancer) cells form in the tissues of the breast.

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Intelligent Medical Imaging Research in Breast CancerBreast Cancer Detection

No Radiation

More Detail

Expensive

Limited availability

Low cost

Short acquisition time

No radiations

High availability

Convenient

more sensitive

Depend on operator

Radiologist s experience

Inconsistency of interpretation

Breast compression

Low-dose X-ray

Just for particular patient

Limited availabilityBreast Self Exam

Mammograms

USGMRI

• Elaborate the radiology knowledge into image processing and analysistechnology

• Assist radiologists to diagnose nodule

CAD

CAD : Computer Aided Diagnosis


Intelligent Medical Imaging Research in Breast CancerResearch Objective

To develop a computer aided diagnosis (CAD)system for classifying breast nodule in ultrasound(US) images to distinguish benign and malignantnodules.

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Diagnosis of Breast Cancer using Ultrasound

A breast ultrasound is a scan that uses penetrating sound waves thatdo not affect or damage the tissue and cannot be heard by humans.

Normal Abnormal



Methodology

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Image Acquisitions

Image Processing Analysis

Image Display

Radiologists

Computer Aided System

Diagnosis

USG Image

Scheme of CAD System



USG Images

RoI (1)

Noise and Marker

Reduction(3)

Segmentation (4)

Feature Selection(6)

Malignant / Benign

Preprocessing

GrayScale Conversion (2)

Feature Extraction (5)

• Moment based features

• Geometry Feature• Texture Feature

Birads based Classification (7) Diagnosis (8)

System Diagram Block

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Nodule

Background

Segmented Area

Posterior Characteristic

Margin Characteristic

Echo Pattern Characteristic

Texture Features

Texture Analysis


Intelligent Medical Imaging Research in Breast CancerGeometry Features

Geometric feature is constructed by a set of geometrical elements such as points, lines, curves or surfaces

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Geometry and Moment Based Features

Nodule

Background

Shape characteristics

Margin characteristics

Moment Based Analysis

Geometry Analysis


Intelligent Medical Imaging Research in Breast CancerResearch Roadmap

2014

2015

Shape and Boundary

Echo Pattern

Prototype 1

2016Margin and Posterior Features

Prototype 2

Clinical TrialIntegrated

Modules

2018

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Results



Unmarked Hypoechoic or Hypoechoic

• Round - Oval

• Irregular

• Circumscribed

• Circumscribed

• Not Circumscribed


Benign

Malignant

Malignant

Malignant

Diagnosis Rules for i-Brids V.1

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ImageCapturing

ROI and FilteringSegmentation and Feature Extraction Diagnosis

i-Brids Prototype.1



0.00%10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%

100.00%

Accuracy Sensitivity Specificity PPV NPVShape 96.20% 94.70% 97.90% 94.73% 97.91%

Margin 80.90% 79.50% 82.50% 78.50% 82.50%

Echo 91.23% 95.83% 87.88% 85.19% 96.67%

Performance Analysis of CAD System

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No Statistical AnalysisDiagnosis

Malignant Benign1 Number of Features Agreement 19 132 Number of Features due to Chance 12.5 6.53 Total Number of Subjects 384 Total Number of Agreement 32

5 Number of Agreement due to chance 19

6 Kappa 0.68

Statistical Analysis

Kappa statistics are commonly used to indicate the degree of agreement of nominalassessments made by multiple appraisers.A Kappa 0.68 is in the “substantial” agreement range between radiologists and CAD system.



Unmarked Hypoechoic or Hypoechoic

• Circumscribed


• No Posterior Feature

• Enhancement

Benign/Malignant

Benign

• Shadowing Malignant

• No Posterior Feature

• Enhancement

Malignant

Malignant

• Shadowing Malignant

Diagnosis Rules for i-Brids V.2

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ImageCapturing

ROI and FilteringSegmentation and Feature Extraction Diagnosis

i-Brids Prototype 2


Intelligent Medical Imaging Research in Breast CancerAccuracy of CAD System

76.00%78.00%80.00%82.00%84.00%86.00%88.00%90.00%92.00%94.00%96.00%98.00%

Margin Posterior DiagnosisRadiologist 1 89.47% 84.21% 97%

Radiologist 2 89.47% 86.84% 97%

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92%93%94%95%96%97%98%99%

100%

Sensitivity Specificity PPV NPVRadiologist 1 100% 94.74% 95% 100%

Radiologist 2 100% 94.74% 95% 100%

Performance Analysis of CAD System



No Statistical Analysis

Margin Posterior Diagnosis

Circumscribed Indistinct EnhancementNo

PosteriorShadow Malignant Benign

1 Number of Features Agreement 22 16 21 3 9 19 19

2 Number of Features due to Chance 12.74 6.74 11.6 0.79 3.47 9.5 9.5

3 Total Number of Subjects 38 38 38

4 Total Number of Agreement 38 33 38

5 Number of Agreement due to chance 19.47 19 15.87

6 Cohen's Kappa 1 0.774 1

Statistical Analysis

A Kappa 1 is in the “perfect” agreement range between two radiologistA Kappa 0.74 is in the “substantial” agreement range between two radiologist

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Intelligent Medical Imaging Research in Breast CancerVideo of i-Brids


Intelligent Medical Imaging Research in Breast CancerPotential Market

HealthLaboratories

Hospitals/Clinics Puskesmas

1380 1599

3451

Number of Health Care Fasilities in Indonesia

Total: 6430

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Intelligent Medical Imaging Research in Breast CancerRecognition

[1] H. A. Nugroho, N. Faisal, I. Soesanti, and L. Choridah, “Analysis of Computer Aided Diagnosis on Digital Mammogram Images,” in 2014 InternationalConference on Computer, Control, Informatics and Its Applications Analysis, 2014, pp. 25–29.

[2] A. Nugroho, H. A. Nugroho, and L. Choridah, “Active Contour Bilateral Filter for Breast Lesions Segmentation on Ultrasound Images,” in 2015International Conference on Science in Information Technology (ICSITech) Active, 2015, pp. 36–40.

[3] H. A. Nugroho, Y. Triyani, M. Rahmawaty, , I. Ardiyanto ,and L. Choridah, “Performance Analysis of Filtering Techniques for Speckle Reduction onBreast Ultrasound Images,” in 2016 International Electronics Symposium (IES), 2016, pp. 454–458.

[4] M. Rahmawaty, H. A. Nugroho, Y. Triyani, I. Ardiyanto, and I. Soesanti, “Classification of Breast Ultrasound Images based on Texture Analysis,” iniBioMed 2016, 2016, pp. 84–89.

[5] Y. Triyani, H. A. Nugroho, M. Rahmawaty, I. Ardiyanto, and L. Choridah, “Performance Analysis of Image Segmentation for Breast UltrasoundImages,” in ICITEE 2016, 2016, no. October, pp. 415–420.

[6] H. K. N. Yusufiyah, H. A. Nugroho, T. B. Adji, and A. Nugroho, “Feature Extraction for Classifying Lesion ’ s Shape of Breast Ultrasound Images,” 2ndInt. Conf. Inf. Technol. Comput. Electr. Eng., pp. 105–109, 2015.

[7] H. A. Nugroho, H. Khuzaimah, N. Yusufiyah, T. B. Adji, and A. Nugroho, “Zernike Moment Feature Extraction for Classifying Lesion ’ s Shape of BreastUltrasound Images,” in 7th International Conference on Information Technology and Electrical Engineering (ICITEE), 2015, pp. 458–463.

[8] H. A. Nugroho, N. Faisal, I. Soesanti, and L. Choridah, “Identification of Malignant Masses on Digital Mammogram Images based on Texture Featureand Correlation based Feature Selection Hanung,” in 6th International Conference on Information Technology and Electrical Engineering (ICITEE),2014.

[9] H.R. Fajrin, H. A. Nugroho, and I. Soesanti“Ekstraksi Ciri Berbasis Wavelet Dan Glcm Untuk Deteksi Dini Kanker Payudara Pada Citra Mammogram,”in SNST, 2015, pp. 47–52.

[10] M. Sahar, H. A. Nugroho, Tanur, I. Ardiyanto, and L. Choridah “Automated Detection of Breast Cancer Lesions Using Adaptive Thresholding andMorphological Operation,” in International Conference on Information Technology Systems and Innovation (ICITSI), 2016.

[11] Tianur, H. A. Nugroho, M. Sahar, R. Indrastuti, and L. Choridah, “Classification of Breast Ultrasound Images based on Posterior Feature,” inInternational Conference on Information Technology Systems and Innovation (ICITSI), 2016.

Breast Cancer :



Team Members and CollaboratorsDepartment Electrical Engineering and Information TechnologyFaculty of EngineeringUniversitas Gadjah Mada

Department of Radiology Sardjito Hospital, Yogyakarta

• H A Nugroho • I Ardiyanto• M Rahmawaty • Y Triyani• M Sahar

• L Choridah• R. Indrastuti• A. Mardhiah

• Tianur• A Nugroho• D A Husna• H Khuzaimah• R L Buana

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Intelligent Medical Imaging Research in Diabetic Retinopathy



What is diabetic retinopathy

TYPE 1 DIABETES: when the pancreasdoesn’t produce insulin

TYPE 1 DIABETES: when the pancreasdoesn’t produce enough insulin (or theinsulin cannot be processed)

GESTATIONAL DIABETES: when theinsulin is less effective during

pregnancy

your body needs insulin to transform glucose into energy

Types of diabetes:

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What is diabetic retinopathy

Diabetic Nephropathy Diabetic Neuropathy

Diabetic Cardiomyopathy

Diabetic Retinopathy(DR)

DR is retinopathy (damage to the retina) caused by complications of diabetes mellitus, which could

eventually lead to blindness.

Fact : - Nearly all patients of type-1 diabetes and 60% of

patients of type-2 diabetes indicate retinopathy.- DR is the leading cause of the blindness in

developing countries among adults aged 20-74 years.

Normal vision DR vision



Diabetes : fact and figures

Reported by International Diabetes Federation (IDF), 2015

“Worldwide”2015: 415 million people with diabetes2040: 642 million people with diabetes

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The pathologies of DR

new blood vessels

Haemorrhages

exudates

Micro aneurysms

Moderate NPDR

Mild NPDR

No DR

Proliferative DR

Severe NPDR



Issues, challenges and approachesIssues

Diabetes mellitus affect ~10% population (DR is a real concern -epidemic stage?)

Needs access to ophthalmologist with fundus camera equipment

Low contrast Fundus images requiring Fluorescein angiography -an invasive procedure

Challenges

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 procedure as effective?

Fundus camera technology+

Image Processing & Computer Vision

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Haemorrhages detection

EnhancementEnhancement Haemorrhages candidatesHaemorrhages candidates Detected HaemorrhagesDetected Haemorrhages

Pre-processing

Green and V band extraction

Histogram matching

Opening operation

Contrast enhancement Haemorrhages

candidate detection Post-processing

Retinal vessels detection

Retinal vessels elimination

Double length filtering

Masking operation

Two-dimensional matched filtering

Fundus image



Hard exudates detection

Fundus image Filtered imageHard Exudates

Detected Hard Exudates

Green channel extraction

Complement operation

Matched filter

Optic disc (OD) detection[1]

Removal OD and Morphological

operation

Detected ODCandidate Exudates

Removal OD andCandidate Hard Exudates

[1] H. A. Nugroho, K. W. Oktoeberza, T. B. Adji, and M. B. Sasongko, "Segmentation of exudates based on high pass filtering in retinal fundus images," in 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE), 2015, pp. 436-441.

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2015

2016

2017

2018

DR pathologies detection DR screening

system

DR monitoring and

grading system

Micro aneurysms detection

Optic disc detection

Macula detection

Analysis of DR pathologies

Haemorrhages and hard exudates detection

Clinical study

System evaluation

FAZ detection

Classification

Research roadmapGeneral : to develop a system to assist the ophthalmologists in monitoring and diagnosing

diabetic retinopathy disease.

First year: to develop algorithms in each module to detect structures and pathologies in DR retinal image.

Second year: to integrate the modules and develop an algorithm for screening DR system.

Third year: to test the system based on clinical study for monitoring and grading system.



Recognition International Conferences

[1] H. A. Nugroho, D. A. Dharmawan, I. Hidayah, and L. Listyalina, "Automated microaneurysms (MAs) detection in digital colour fundus images using matched filter," in Computer, Control, Informatics and its Applications (IC3INA), 2015 International Conference on, 2015, pp. 104-108.

[2] H. A. Nugroho, L. Listyalina, N. A. Setiawan, S. Wibirama, and D. A. Dharmawan, "Automated segmentation of optic disc area using mathematical morphology and active contour," in Computer, Control, Informatics and its Applications (IC3INA), 2015 International Conference on, 2015, pp. 18-22.

[3] H. A. Nugroho, D. Purnamasari, I. Soesanti, K. W. Oktoeberza, and D. A. Dharmawan, "Detection of foveal avascular zone in colour retinal fundus images," in 2015 International Conference on Science in Information Technology (ICSITech), 2015, pp. 225-230.

[4] H. A. Nugroho, K. W. Oktoeberza, T. B. Adji, and M. B. Sasongko, "Segmentation of exudates based on high pass filtering in retinal fundus images," in 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE), 2015, pp. 436-441.

[5] H.A. Nugroho, L. Listyalina, and D. A. Dharmawan, "A New Approach for Detection of Retinal Haemorrhages in ColourFundus Images," presented at the International Seminar on Sensors, Instrumentation, Measurement and Metrology, 2016.

[7] I. Ardiyanto, H.A. Nugroho, and R. L. B. Buana, "Maximum Entropy Principle for Exudates Segmentation in Retinal Fundus Images," presented at the International Seminar on Sensors, Instrumentation, Measurement and Metrology, 2016.

[8] H.A. Nugroho, W.KZ. Oktoeberza, I. Ardiyanto, R.L.B. Buana, and M. B. Sasongko, "Automated Segmentation of Hard Exudates Based on Matched Filtering," presented at the International Seminar on Sensors, Instrumentation, Measurement and Metrology, 2016.

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Recognition Journals

[1] H. A. Nugroho, K. W. Oktoeberza, T. B. Adji, and F. Najamuddin, "Detection of Exudates on Color Fundus Images using Texture Based Feature Extraction," International Journal of Technology, vol. 6, p. 04, 2015.

[2] H.A. Nugroho, D.A. Dharmawan, and L. Listyalina, "Automated Segmentation of Foveal Avascular Zone (FAZ) in Digital Colour Retinal Fundus Images," International journal of biomedical engineering and technology, 2016.



Team members and Collaborator

Rapid Assessment Diabetic Retinopathy and Intelligent System Research GroupsDepartment of Electrical Engineering and Information Technology, Faculty of Engineering

Universitas Gadjah Mada, Indonesia

(Hanung Adi Nugroho, Noor Akhmad Setiawan, Teguh Bharata Adji, Indriana Hidayah, Igi Ardiyanto, Ratna Lestari Budiani Buana, Dhimas Arief Dharmawan,

Latifah Listyalina, Dewi Purnamasari, Widhia Oktoeberza KZ)

Department of Ophthalmology, Sardjito Hospital, Yogyakarta, Indonesia

(dr. Muhammad Bayu Sasongko, dr. Kartika Dhani)

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THANK YOU

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