May 14, 2008 الله أكبر و لله الحمد 1 Automated Identification Systems Hany Ammar Lane Dept. of Computer Science & Electrical Engineering The 2 nd International

May 14, 2008 الحمد لله و أكبر الله 1

Automated Identification Systems Hany Ammar

Lane Dept. of Computer Science & Electrical Engineering

The 2nd International Conference on Computer andCommunication Engineering (ICCCE08), KL, Malaysia

A key-Note Presentation on

الرحيم الرحمن الله بسمالله رسول على السالم و الصالة و لله الحمد

April 9, 2008 الحمد لله و أكبر الله 2

OutlineOutlineAutomated Identification Systems

The Center for Identification Technology Research (CITeR)

Examples of Automated Identification Systems Projects

Automated Dental Identification Systems (ADIS) Research Team Funding Agencies Overview of ADIS and the ADIS Architecture Record Pre-processing Dental Image Retrieval Matching

Summary


Automated Identification Systems

Automated identification of a person based on his/her physiological or behavioral characteristics Termed as “Biometrics”Identification

Fingerprint Hand Geometry Signature

Dental Features Iris Voice



APPLICATIONS INCLUDE HIGH SECURITY APPLICATIONS: financial

services, health care, law enforcement, Government applications, travel and immigration, and E-commerce

FORENSIC IDENTIFICATION: help solve legal cases and public issues which include bank robberies, homicides, kidnapping cases, and identifying victims of mass disasters (Post Mortem identification)



Forensic Post-Mortem (PM) Identification Methods include:

- Visual- Fingerprints- DNA- Dental

Dental features- Used to identify 75% of Tsunami victims in Thailand,

and 20% of 9/11 victims identified in the 1st year compared to only 0.5% identified using DNA

- Resist early decay of body tissues.- Withstand severe conditions in mass disasters.- Unique (Identification can sometimes be made from

one tooth).



Example systems Automated Dental Identification System

ADIS

ADIS

Digital Image Rep

Mrs. X PM Record: - NCIC codes- Dental Radiographs

Short Match List

Forensic Scientist



Example systems Automated Ear Identification System

AEISVideo

Sequence

Ear Segmentation and Localization

Image Enhancement

2-D and 3-D Feature Extraction

IdentificationEnrollment

Decision

Data-base

Currently being developedWVU-UM



Biometrics Lab at WVU – Face Video data acquisition system Collected a

Database of 500Subjects


NSF Center at WVUCITeR

The US National Science Foundation Center for Identification Technology Research (CITeR)Industry/University Cooperative Research Center (I/UCRC)West Virginia University is the lead institution http://www.citer.wvu.edu/



The Center for Identification Technology Research (CITeR)

Examples of Automated Identification Systems Projects

Automated Dental Identification Systems (ADIS) Research Team Funding Agencies Overview and the ADIS Architecture Record Pre-processing Dental Image Retrieval Matching

Summary


ADIS Project Research Team

Prof. Hany Ammar, Dr. Gamal Fahmy, Dr. Robert Howell, Dentist,

Ph.D. Students: Ayman Abaza, Diaa Nassar, Eyad Haj-Said,

MS Students: Mubasher, Zainab Millwallah, Usman Qureishi, Faisal Chaudhry, Mythili, and Satya Checkuri, Ali Bahoo

Prof. Anil Jain,Ph.D. Student: Hong Chen

Prof. Mohammad AbdelMottaleb,Ph.D. Students: Omaima Nomair, Mohammad Mahoor, Jindan,


Support

$1.5M over 5 years- This research is supported in part by the U.S. National Science Foundation (Digital Government Program) under Award number EIA-0131079 to West Virginia University, - The research is also supported under Award number 2001-RC-CX-K013 from the Office of Justice Programs,National Institute of Justice, U.S. Department of Justice. Points of view in this document are those of the authorsand do not necessarily represent position of theU.S. Department of Justice.- The research is conducted in Collaboration with The Criminal Justice Information Services Division (CJIS) of the US Federal Bureau of Investigation


Forensic Odontologist Compares PM Records with AM records based on:

- Dental Work (e.g. Fillings, Restorations ...)

- Inherent Dental Characteristics (Crown Morphology, Root Morphology, Spacing …)

- Very Time Consuming Process

OverviewOverviewDental Identification

Identification of the victims of 9/11

- 20% of the 973 identified in the first year

- Identification of 2,749 took around 40 months.


Source: The Bureau of Legal Dentistry (BOLD) - http://www.boldlab.org [2000]

OverviewOverviewDental Identification is a challenging problem

AM

PM


ArchitectureOverviewOverview


ADIS OutlineADIS OutlineOverview

Record Pre-processing

Dental Image Retrieval

Matching

Conclusion & Future Work

Comments & Questions


Record Pre-processingRecord Pre-processing1- Record Cropping:

global segmentation of dental films from their corresponding records.

The objective:

to automate the process of cropping a composite digitized dental record into its constituent films

Reference Record - 16

Subject Record


Dental Record

Pre-Processing

Cropping based on

Arch-Detection

Round Right

Cropping based on

Factor Analysis

Corner-type

Classification

Background

Extraction

Post-Processing

Dental Films

Record CroppingRecord CroppingApproach

April 9, 2008 الحمد لله و أكبر الله 19Under-segmented

Record CroppingRecord CroppingExperimental Results


By calculating “”,

“” found to range between 0.49 - 0.91,

“” was used to identify the Under Cropped Segments.

Record cropping time ranges 15-40 sec.

},min{ hw

wh

Record CroppingRecord CroppingExperimental Results

Perfectly

Cropped

74%

Under Cropped

24%

Error

2%

Perfectly Cropped

Under Cropped

Error

Randomly selected test sample of 100 dental records (images) from the CJIS ADIS database, the total film count in the test set is 722.


Record Pre-processingRecord Pre-processing3- Film Type Detection:

dental films classification into bitewing, periapical, or panoramic.

The objective:

to automate the process of dental film type detection.

bitewingperiapical

panoramic


Record Pre-Record Pre-processingprocessing4- Teeth Segmentation:

Teeth segmentation from dental radiographic films.

The objective: to automate the process of local segmentation of each tooth. teeth isolation into a rectangular box


Record Pre-processingRecord Pre-processing5-Tooth Contour Extraction:

another level of segmentation, to extract the contour of the tooth.

The objective:

to extract an accurate smooth representative tooth contour,

- Representative smooth contour.

- Time / tooth = fraction of the second).


Record Pre-Record Pre-processingprocessingExperimental Result

Records

Correct or partially correct contour extraction (%)

Errors(%)

Average time (s)

Perfect contour

(P)

Perfect crown (PC)

Partially correct

(C)

Errors(E)

10 AM 56.2 14.05 16.75 13.0 0.15

10 PM 60.0 11.61 14.83 13.56 0.17

ALL 58.10 12.83 15.79 13.28 0.16

The snake-based algorithm on the same platform takes about 5 sec compared to 0.16 sec.

For a test set of 20 records, involving ~340 teeth


Record Pre-processingRecord Pre-processing6-Teeth Labeling: automatic classification of teeth into incisors, canines, premolars and molars as part of creating a dental chart.

The objective: - to accurately classify and label teeth,

- to accommodate a missing segment.

RX7RX6 RX5

RX4

RD7RD6 RD5

RD4

7M 5

P


An adult has 32 permanent teeth

(8 Incisors, 4 Canines, 8 Premolars and 12 Molars).

Each tooth has a specific structure and position in the mouth.

Dental Atlas for the left half of the upper jaw.

Record Pre-processingRecord Pre-processingDental Atlas

American Medical Association, http://www:medem:com


- Teeth Classification: added the film type, designed a technique based on Linear DiscriminantAnalysis (FisherTeeth).

- Extended the validation stage for the presence of missing tooth.

Teeth Labeling Approach – Eigen Teeth Labeling Approach – Eigen Teeth Teeth

Record Pre-Record Pre-processingprocessing


Experimental Results of teeth labeling

Based on the dataset used in the literature,

(50 bitewing films involving about 400 teeth).

Method Molars Average

Premolars Average

Labeling Time

Complex Signature 89.6% 90.95% 21.3 msec

Centroid Distance 90.55% 87.85% 21.3 msec

Eigen Teeth 91.67% 92.86% 11.5 sec

Record Pre-processingRecord Pre-processing





Matching




Dental Image Retrieval4- Potential Matches Search: searching the dental database in a fast way to find a candidate list.

The objective: - to accomplish a relatively short candidate list, with a high probability of having the correct match reference.

This objective directly targets the scalability of ADIS system.

Candidate List

Digital Image Repositories


Potential Match Potential Match SearchSearchChallenges

Multiple RepresentationOf the same tooth (RX6)

Reference Record

Subject Record


Potential Match Potential Match SearchSearchProposed Approaches

Archiving

Retrieval /Matching

Preprocessing Stage

LabeledTeeth

Segments

View Normalization

Appearance-based

features

CandidateList

Subject Preprocessing

Reference Preprocessing

Teeth Contour Extraction

Shape-based

features

Digital Image Repositories

1- Appearance-based, namely Eigen images.

low computational-cost features;

Limitation: need geometric and gray-scale normalization.

2- shape –based namely moment invariant and edge orientation histogram Limitation: need accurate teeth contour.


Potential Match Potential Match SearchSearchExperimental Result (Comparison between appearance and shape

based)

Minimum fusion, better for shape-based.

The appearance-based, better for short candidate list.

The edge direction histogram achieves the same performance for slightly longer candidate list. 0 5 10 15 20 25 30 35 40 45

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Candidate List Size

Hit

Rat

e

Best Min Fusion CMC curves

OneClassFourClassSmoothHist





Matching




Image Comparison Image Comparison ComponentComponent


Image Comparison Image Comparison ComponentComponent

Teeth Alignment: is to align each corresponding pair, in other word to find the transformation matrix that best align the reference and subject segments.

The objective: is to achieve an accurate aligned segments in few seconds, so as to allow for a faster Image Comparison Component.

Teeth Alignment


A Hierarchical fusion scheme: Tooth-level fusion Case-level fusionA Ranking Scheme to Sort the Match List

Micro and Macro Decision-Making (The Strategy)

Image Comparison ComponentImage Comparison Component


Results

Image Comparison ComponentImage Comparison Component



Example Research Projects

Automated Dental Identification Systems (ADIS)

Research Team Funding Agencies The ADIS Architecture Record Pre-processing Dental Image Retrieval Matching

Summary


SummarySummary

• Automated Identification Systems are needed in many applications in the yearsto come

• They Pose many challenging problems


SummarySummary

Timeliness Performance Teeth labeling and alignment are

time consuming processesQuality of radiographs are very critical for ADIS

Poor quality can affect the segmentation accuracy significantly

Matching efficiency can also be affected by poor quality radiograph

ADIS challenges

Documents

May 14, 2008 الله أكبر و لله الحمد 1 Automated Identification Systems Hany Ammar Lane Dept. of Computer Science & Electrical Engineering The 2 nd International