Biomedical Engineering in Diagnostic

and Treatment:

Experience Sharing in Sarawak

Dr Kuryati Kipli, Senior Lecturer, Department of Electrical & Electronic ,

Faculty of Engineering, UNIMAS.

Brief Intro on BME & BMET

Imaging for Biomedical Applications

Research on Biomedical Image Processing

& Analysis

Challenges

Content

Biomedical Engineering (BME) and Biomedical Engineering Technology (BMET) field in Malaysia is at its early stage.

Research and development in those areas offer a great potential for improvement of health care and health services.

The range of focus areas includes biomechanics, medical devices, medical imaging, biomaterials and physiological signal processing.

BME/BMET in Malaysia

Bachelor of BMET (hons)

BEng(Hons) Biomedical Engineering

Bachelor of Biomedical Engineering

Bachelor of Biomedical Engineering (Prosthetics and Orthotics)

Bachelor of Engineering (Bio-Medical)

Bachelor of Engineering (Hons.) Electronics Majoring in Bio-Instrumentation

Bachelor of Electronic Engineering with Honours (BEJ) (Biomedical)

BME Degree offered in Msia

Bachelor of Electronic Engineering with Honours (BEJ) (Biomedical)

Investigation of complex medical problems and development of

engineering methods to solve them

Combine engineering principles with medical & biological sciences to design and create equipment, devices, computer system and software used in healthcare.

Engineering framework including biomaterials, biomechanics, and

bioelectricity

Career at manufacturing facilities, company, universities, hospitals,

research facilities and government.

Biomedical Engineering (BME)

BME & BMET area

Manage and support the manufacture and use of existing medical devices and

technology in patient care

Services : installation and maintenance of biomedical equipment.

Medical device systems, electrical engineering technology, patient safety

and regulations, organizational leadership and supervision, clinical

communication

Career at biomedical instrumentation industries, hospitals, medical equipment

manufacturers.

Biomedical Engineering

Technologist (BMET)

Imaging for Biomedical Applications

Techniques and processes used to create images/data of the interior of the human body for modelling and quantification of physiological and anatomical features and for clinical purposes (medical procedures seeking to reveal, diagnose, examine or treat disease).

Source: CISIR,UTP

Imaging for Biomedical Applications On an average, 2 out of every 5 visits to hospital involve an imaging procedure in US (SG-2) More than 1B imaging procedures/year worldwide (Medtech Insight) Growth driven by new applications, increasing accuracy and increasing speed

Medtech Insight, CRIS

Source: Biomedical Engineering, IFE, 2013

Imaging Technology

Medical Imaging – Biomedical Engineering

radiography

(J. Hall-Edwards, 1896)

angiography

(E. Moniz, 1927) ultrasonography

(I. Edler, C. Hertz, 1953)

CT tomography

(A. Cormack, G. Hounsfield, 1972)

PET tomography

(M. Ter-Pogossian et.al., 1973)

Endoscopic capsule

(Given Imaging, 2001)

MRI tomography

(P. Lauterbur, P. Mansfield, 1973

since 80ties)

endoscopy

(B. Hirschowitz, since 70ties)

The History of Medical Imaging

Termography

(since 60thies, XX c.)

19

Introduction to Medical Imaging

CT+MRI, PET+MRI

(Gen. Electric, 2010)

Source: Biomedical Engineering, IFE, 2013

Source: Biomedical Engineering, IFE, 2013

• Each has it pro & con; cost, capability to reveal certain ROI, execution time

Medical Imaging – Biomedical Engineering

x y

f(x,y)

!

x, y( ) ² f x, y( )

y

Monochrome image as a 2D function

7

Introduction to Medical Imaging Source: Biomedical Engineering, IFE, 2013

Medical Imaging – Biomedical Engineering

0 50 100 150 200 250 30060

80

100

120

140

160

180

200

220

Distance along profile

Image brightness profile

8

Introduction to Medical Imaging Source: Biomedical Engineering, IFE, 2013

Medical Imaging – Biomedical Engineering

X

Y

(0,0)

. . . . . . . . . . . .

. . 15 17 18 . .

. . 20 31 14 . .

. . . . . . . . . .

f(x,y)

Digital image as pixel array

12

Introduction to Medical Imaging Source: Biomedical Engineering, IFE, 2013

Medical Imaging – Biomedical Engineering

R

B

RGB color image

9

Introduction to Medical Imaging

Medical Imaging – Biomedical Engineering

( ) ( )BGR fffyx ,,, =f

If each of the color component is

8 bit coded then 224 different

colors can be obtained

Color digital RGB image

14

Introduction to Medical Imaging

Medical Imaging – Biomedical Engineering

Digital image f(x,y):

2D array (M,N),

ie. of M rows and N columns,

of nonnegative elements assuming

a limited number of levels

Color digital image?

1...,,1,0

1...,,1,0

-=

-=

My

Nx( ) 1...,,1,0, -= Lyxf

(e.g. L=256)

Digital image as pixel array

13

Introduction to Medical Imaging

Source: Biomedical Engineering, IFE, 2013

Focus of research : Develop techniques/algorithms for automated

extraction of meaningful features (quantitative) from the acquired images (input from expert on Region Of Interest)

Develop techniques/algorithms for automated classification/detection of the disease

useful for early and accurate diagnosis based on quantitative diagnosis-converting the medical findings into a functionally useful product

Research in Biomedical Image Processing and Analysis

General Structure Medical Imaging – Biomedical Engineering

Introduction to Medical Imaging

18

Computer vision system

Image

Processing Feature

Estimation Segmentation

Data

Analysis

(recognition, classification,

interpretaction)

Computer +

software +

knowlegde database

Ima

ge

Acq

uis

itio

n

„healthy tissue”

features: geometrical,

topological,

texture …

Image Acquisition

Image Pre-processing

Feature Selection

Feature Extraction

Classification

Towards Automated Detection of Depression from sMRI

• Architecture of brain sMRI

based depression detection system

• Brain sMRI volumetric features

• DoC feature selection algorithm

• Classification model

Journal Publications

[1] Kipli, Kuryati and Kouzani, Abbas Z. (2014), “Degree of Contribution (DoC) Feature Selection Algorithm for Structural Brain MRI Volumetric Features in Depression Detection”, International Journal of Computer Assisted Radiology and Surgery (IJCARS),Springer, In Press. DOI: 10.1007/s11548-014-1130-9.

[2] Kipli, Kuryati, Kouzani, Abbas Z. and Williams, Lana J. (2013), “Towards automated detection of depression from brain structural magnetic resonance images”, Neuroradiology, pp. 1-18, Springer, Berlin, Germany.

[3] Kipli, Kuryati, Kouzani, Abbas Z. and A. Hamid, Isredza R. (2013), "Investigating Machine Learning Techniques for Detection of Depression Using Structural MRI Volumetric Features," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 3, no. 5, pp. 444-448.

Conference Publications

[1] K. Kipli, A. Z. Kouzani, and Y. Xiang, (2013), "An Empirical Comparison of Classification Algorithms for Diagnosis of Depression from Brain SMRI Scans," in International Conference on Advanced Computer Science Applications and Technologies (ACSAT), Kuching, Malaysia pp. 333-336.

[2] Kipli, Kuryati, Kouzani, Abbas Z., and A. Hamid, Isredza R. (2013), "Investigating Machine Learning Techniques for Detection of Depression Using Structural MRI Volumetric Features," in 2nd International Conference on Bioinformatics and Biomedical Science (ICBBS), Kuala Lumpur, Malaysia.

(Selected for publication in the International Journal of Bioscience, Biochemistry and Bioinformatics (IJBBB, ISSN: 2010-3638))

[3] Kipli, Kuryati, Kouzani, Abbas Z. and Joordens, Matthew (2013), "Evaluation of Feature Selection Algorithms for Detection of Depression from Brain sMRI Scans," in IEEE International Conference on Complex Medical Engineering (CME 2013), Beijing, China.

[4] Kipli, Kuryati and Kouzani, Abbas Z. (2013), "An Algorithm for Determination of Rank and Degree of Contribution of sMRI Volumetric Features in Depression Detection," in the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’13), Osaka, Japan.

[5] Kipli, Kuryati, Kouzani, Abbas Z. and Joordens, Matthew (2012) Computer-aided detection of depression from magnetic resonance images, in CME 2012 : Proceedings of the 2012 IEEE/ICME International Conference on Complex Medical Engineering, pp. 500-505, IEEE Computer Society, Los Alamitos, Calif.

[6] K. Kipli, (2012), “Performing Brain Structural MRI Volumetric Attributes Feature Evaluations and Classifications for Detection of Depression” in Institute for Frontier Materials (IFM), Annual Research Conference 2012, Deakin University on 5th & 6th Nov 2012, The Pier, 10 Western Beach Foreshore Road, Waterfront Geelong.

[7] Kipli, Kuryati, Kouzani, Abbas Z., Xiang, Yong and Joordens, Matthew (2011) Evaluation of segmentation algorithms for extraction of RNFL in OCT images, in ICIS 2011 : Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems, pp. 447-451, IEEE, Piscataway, N.J.

List of Publications

Develop automated screening tool for early disease detection

Ways to identify these diseases is by observing the abnormalities of vascular pattern

Many features in retinal-useful for diseases diagnosing

Automated Retinal Image Analysis

Different Types Of Retinal Features (some of the features)

Outputs image of the proposed method at each steps (a) input image/original image (b) green channel image (c) CLAHE (d) morphological reconstruction (e) morphological top-hat transform (f) Otsu’s thresholding (g) unwanted region removal

Segmentation accuracy 92-95% depending on type of disease images

Segmentation of Retinal Blood Vessel

(a) (b)

(c) (d)

(e) (f)

(g)

Bifurcation angle; angle between the daughter vessels.

Bifurcation Angle Extraction

Measuring angle between the daughter vessels.

The irregularity of the bifurcation angle can be used as an indicator for early detection of hypertension.

Based on studies, the angle for optimal blood flow in bifurcation angle is 72⁰.

Images : VICAVR Database-retinal image with marked artery and vein vessels by three different experts.

Checking 8 neighborhood of every positive pixel

When there are three vessels coming out from one point, they are marked as bifurcation.

Bifurcation Localization

Start

Image Acquisition

Bifurcation Localization

Measuring angle

End

Output: Algorithm for angle identification & measurement

shape of a feature, surface analysis, volumetric, size, area, angle, length, intensity, peak value, texture

Extracted features/pattern

Classification

Medical Imaging – Biomedical Engineering

Introduction to Medical Imaging

24

Analysis of wrist radiograms

Control (1) Osteopenia (2)

Osteoporosis (3)

Markov Random Field model

Classsifcation error: 9%

Liniear

Discriminant

Analysis

Classification error: 0%

Sample expected Output: System

FAZ

FA

Z a

rea (

pix

els

)

normal mild

moderate

severe

PDR

normal mild

moderate

severe

PDR

normal mild

moderate

severe

FAZ FAZ

FA

Z a

rea (

pix

els

)

Retinopathy Monitoring and Grading system

Other Related Research

Nearfield Electromagnetic Imaging Technique for Brain Tumour Detection

Microwave imaging is a well-established technique which is being employed to build low- cost and portable medical diagnostic tools

IMAGE RECONSTRUCTION: BRAIN IMAGING

Product Description

Novelty/Invention

Commercial/Potential

• This invention overcomes the disadvantages of the conventional caliper and string method for finger circumference measurement which may be inefficient, time consuming and impractical for a busy clinic practice.

The primary objective of this invention is to provide a circumferential finger measurement device for facilitating measurements at various points along fingers, particularly at the nail-fold (NF) and distal interphalangeal joint (DIP). This digital readout device would take a measurement to obtain data for the studies of finger clubbing evaluation.

The availability of this device in wards will: • increase efficiency in measuring circumferences of

NF and DIP • create a simpler and faster method for helping the

clinician in evaluating and identifying finger clubbing condition of the patients

• generate passion among the clinicians for investigation and clinical studies on the early stage of finger clubbing

Features of the Invention

Fig. 2 The main components of the present invention

Fig. 3 Overall structure of the device Fig. 4 Layout of Finger Clubbing Digital Index Evaluation System

Fig. 1 NF and DIP circumferences of a normal (left) and clubbed (right) finger

Fig. 5 Comparison of time taken

Product Description

Novelty/Invention

Commercial/Potential

• Simple circuitry • Small chip size

• Power amplifier devices • Suitable for medical applications

Pin (dBm)

-30 -25 -20 -15 -10 -5 0 5 10

PAE

(%)

0

5

10

15

20

25

Frequency (GHz)

1 2 3 4 5 6 7 8

S-pa

ramete

rs (dB

)

-25

-20

-15

-10

-5

0

5

10

15

Simulation result

Measurement result

S21

S11

S22

Vdd

C1L1

L3 C2

L2

R1

L4

C3

M1

M2

M3

RFin

RFout

Vbias1

Vbias2

R2

R3

• Gain= 10±1 dB,

• Good input and output matching

• Good group delay variation

• good linearity

Features

Advantages

UWB technology is used in many medical applications such as medical sensor networks, medical imaging systems, implanted biomedical devices and medical monitoring. The emergence of UWB technology requires the transceiver to be wide range frequency, high data rate, ultra low power and high circuit integration over short distance. However, the existing transceiver technology for UWB consumes high power from its integrated PA about 48% which is not suitable for medical application. Therefore, low power PA is necessary in order to be used in medical application.

• New topology CMOS Power amplifier • Low power consumption with good efficiency • Very wide operating frequency (3.1-6GHz)

PRICE COMPARISON POWER AMPLIFIER

Product Technology Price (USD)

Microchip (SST12L18E) GaAs 0.68

RFMD (RF6117) GaAs 1.63

MAXIM (MAX2240) 0.25

This product CMOS 0.03

Power breakdown of transmitter

PA Chip Micrograph (0.86 x 0.78mm) 3.1-6.0 GHz CMOS PA

Measured S-parameters Measured Group Delay

Automated, Accurate

Portable, simple

Access to data

Collaboration from both parties

Challenges