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A Light-weight Solution for Real-Time DengueDetection using Mobile Phones

Jerrid MatthewsRajan Kulkarni

George WhitesidesMajid Sarrafzadeh

Mario GerlaTammara Massey

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What is Dengue?Definition• Dengue [Den-ghee]: is a flu-like viral disease spread by

infected Aedes aegypti mosquitoes. • Dengue hemorrhagic fever: is a severe, often fatal,

manifestation of dengue if left untreated.

Case Study• Approximately 100 million cases of dengue or dengue fever

occur each year– Dengue occurs in most tropical areas– Dengue is common in India, Asia, Australia, and Africa– Most U.S. cases occur in travelers returning from abroad or along the Texas-Mexico border

• Alarmingly, dengue outbreak occurrences are increasing in Texas and Florida

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How Dengue SpreadsMosquito bites a Dengue infected person

Virus replicates inside mosquito

Mosquito bites healthy person

Dengue manifests

• There is no specific treatment for dengue however early diagnosis can:– Improve chances for recovery– Prevent Dengue Fever

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Motivation

Dengue is now 30 times more common than it was half a century ago– Brazil in 2008, over 160,000 cases and 100 deaths

Due To:• High cost of Dengue detection kits

– $200 (Dengue Fever Rapid Dipstick Test)

– $700 (Dengue Fever IgG/IgM Card Test Kit)

• Lack of available treatment and regulation facilities in 3rd world countries– No real-time outbreak monitoring– Restricted purchasing ability for Dengue Detection Kits in certain regions

Need for an economical solution to detect dengue:– Can be cheaply mass produced– Enables real-time monitoring and detection in rural areas

Goal is to enable onsite patient diagnosis

Patient Treatment Flowchart

Traditional Process

New Process

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InnovationOur Innovation

– Algorithm that uses a $0.20USD medical patch and a cellular phone camera that displays the results to medical personnel

– Ability to upload test results for real-time monitoring

Purpose– To monitor Dengue outbreak cases in real-time– To leverage image processing capabilities of mobile

devices for Dengue detection– Improve quality of life in developing countries by reducing

time to diagnose infected individuals

• HTC-6800 Windows Mobile Phone with integrated 2.0 MP CMOS Camera

• Windows Mobile 6.1 OS• Qualcomm 400 MHz MSM750 ARM Processor• 64MB of RAM and 512 MB of flash memory

Windows Mobile Phone

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Experiment• Experimental Setup

– Initial tests performed with Matlab then implemented on Cell phone– To maintain cell phone comparability, we did not use

optimized Matlab image processing functions– Used 320 x 240 and 1 Megapixel sized image for experiments

• Assumptions– Better edge approximation with 0 °, 90 °, 180 ° or 270° patch

orientation– Camera is parallel to patch to avoid skewed sides– Low ambient light interference

Developed in conjunction with researchers at Harvard School of Medicine and the Dengue Relief FoundationAdvanced medical bioassay patch

Patch and Architecture

Windows Mobile Phone

(Diagnose Patch)

Apply Dengue Patch

Web Service

(Optional)Connection to database

Apply Patch to finger

Prick Finger

Take Picture of patch

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Level of Trust

2 3

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Image Processing AlgorithmStep 1: Isolate patch from background noise

• Convert the color image to binary– As long as patch is in the foreground it’s white

color can be isolated

Step 2: Localize patch using a greedy scan• Scan horizontally and vertically across image to

identify the areas with highest average pixel value for each scan direction

– Red lines show row/column with highest value

• Identified locations form a starting point for edge detection

A

B

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Image Processing Algorithm (Cont)

Step 3: Perform outward stencil scan to find patch edges

Strong gradient point forming line segment cutting through patch

Stencil scan direction to approximate edge of patch

2 1

1 2

[( ( , ) 2* ( , ))

(2* ( , ) ( , ))] / 4

x i j i j

i j i j

S L x y L x y

L x y L x y

2 1

1 2

[( ( , ) 2* ( , ))

(2* ( , ) ( , ))] / 4

y i j i j

i j i j

S L x y L x y

L x y L x y

( , )iG x y

stencil equation  used for scans across the X axisxS

( , ) Starting points for stencil scan along line segment i j i jL x y GG

stencil equation  used for scans across the Y axisyS

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Image Processing Algorithm (Cont)

Step 4: Processing the wells• Perform luminosity analysis on wells by clustering

similar color shades and taking the maximum valid cluster color– Outlier colors that occur due to chemical reaction

side effects (eg: blue shades above) are omitted

Algorithm Discussion• Majority of overhead lies in patch localization• Once patch is localized runtime is fast

• Image Analysis / Average Runtime:– 240x320 image: 13 seconds to process– (Image resized to 1 Megapixel): 30 seconds to process– Error exists when estimating corner points of patch,

however is minimized when patch is at or near 0 °, 90 °, 180 ° or 270°

A B

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Future WorkResearching a more robust algorithm for

patch localizationExample: a hybrid algorithm similar to

active contouring starting at a predefined bounded area to localize patch

Data Security: Integrating with UCLA’s Gateway device for secure storage of data when access to 3G network is not available

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References• P. Dussart, L. Petit, B. Labeau, L. Bremand, A. Leduc, D. Moua, S. Matheus, L. Baril, “Evaluation of Two New

Commercial Tests for the Diagnosis of Acute Dengue Virus Infection Using NS1 Antigen Detection in Human Serum”, PLoS Neglected Tropical Diseases, vol. 2, no. 8, pp. e280, 2008.

• "Erba Den-Go to Detect Dengue", http://www.transasia.co.in/Erbadengo.html• D.J. Gubler, “Dengue/dengue hemorrhagic fever: history and current status”, Novartis Foundation Sympsoium, vol.

277, pp. 3-16, 2006.• M. Guszman and G. Kouri. Dengue and dengue hemorrhagic fever in the Americas: lessons and challenges.”

Journal of Clinical Virology 27 (2003) 1-13.• R. S. Lancotti, C. H Calisher, D. Gubler G. Chang, A. V. Vorndam, "Rapid Detection and Typing of Dengue Viruses

from Clinical Samples by Using Reverse Transcriptase-Polymerase Chain Reaction, Vol. 30, No 3, 1992.• A.W. Martinez, M.J. Butte, G.M. Whitesides, “Patterned Paper as a Platform for Inexpensive, Low-Volume, Portable

Bioassays.” Angewandte Chemie International Edition, vol. 46, no. 8, pp. 1318-1320.• Organizacion Panamerica de la Salud. Nueva Generatcion de Programas de Prevencion y Control del Dengue en las

Americas. OPS/HCP/HCT/206/02.• F. Pinheiro. Dengue in the Americas 1980-1987. Epidemiol Bull PAHO 1989; 10:1-8.• T. Su, S. Seo, A. Erlinger, A. Ozcan "Multi-color LUCAS: Lensfree on-chip cytometry using tunable monochromatic

illumination and digital noise reduction",” Cellular and Molecular Bioengineering. 1:2, 146-156., 2008.• Hilde M., Rust M.,Chen C, Heidi E, Wilschut1 J, Zhuang X., Smit1 J. "Dissecting the Cell Entry Pathway of Dengue

Virus by Single-Particle Tracking in Living Cells"• Sa-ngasang-A, Wibulwattanakij S., Chanama S, O-rapinpatipat A., A-nuegoonpipat A., Anantapreecha S.,

Sawanpanyalert P, Kurane we. "Evaluation of RT-PCR as a Tool for Diagnosis of Secondary Dengue Virus Infection"• Lanciotti R., Calisher C., Gubler D., Jen Chang G., Vorndam V. "Rapid Detection and Typing of Dengue Viruses from

Clinical Samples by Using Reverse Transcriptase-Polymerase Chain Reaction"• "Dengue Fever Rapid Test Kits", http://www.ivdpretest.com/Dengue-Rapid-Tests.html• "Dengue Relief Foundation", http://www.denguerelief.org/aboutus/index.html• Anthony R., Charles R, Illah N., "A Low Cost Embedded Color Vision System", IROS 2002

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Image Processing Algorithm (Cont)

Step 4: Processing the wells• Compare the