age at death evaluation by tooth cementum annulation

Figure  3.  Tooth  embedded  in  a  epoxide  resin  bloc  (Biodur  E12)  and  fixed  in  a  microtome  saw  (Leica  1600).  The  tooth  crown  of  the  sample  was  cut  off.  

Figure  4.  (A)  LocalizaFon  of  slices.  Tooth-­‐scheme  (lateral  view).  8-­‐10  sec<on  were  taken  (B)  Thin  secFons  on  a  slide.  

Age  at  death  evaluaFon  by  tooth  cementum  annulaFon  (TCA)    –  a  soLware  for  an  automated  line  counFng  (Auto-­‐TCA)  Andrea  Czermak1,  Adrian  Czermak2,  Gisela  Grupe3,  Hartmut  Ernst2  1  Ins<tut  für  Archäologische  WissenschaMen,  Frühgeschichtliche  Archäologie  und  Archäologie  des  MiPelalters,  Albert-­‐Ludwigs-­‐Universität  Freiburg,  Germany;  2  LMU  Biozentrum,  Ludwig  Maximilians  Universität  München,  Germany;  3  Faculty  of  Computer  Sciences,  University  of  Applied  Science  Rosenheim,  Germany;      

This  research  work  was  supported  by  the  Hanns-­‐Seidl-­‐S<Mung,  Munich,  Germany  (PhD  grant  to  Andrea  Czermak)  

Acknowledgement  

Conclusions  

Figure  13.  Example  for  Auto-­‐TCA  counFng  results  of  the  incremental  lines  of  individuals  in  different  age  groups  (morphologically  determined).  The  soMware  was  tested  on  samples  of  individuals  with  unknown  age-­‐at-­‐death  from  historical  excava<ons.  The  coun<ng  results  of  one  slice  show  a  Gaussian  distribu<on  curve  and  several  coun<ng  on  different  spots  at  one  slice  largely  coincide.  The  younger  an  individual,  the  slender  the  Gaussian  curve  and  the  definite  the  result  (A).  Proceeding  (morphologically  determined)  age  leads  to  a  flat  curve  and  more  scaPering  results  (B).  In  higher  age  groups  differences  between  slices  near  the  tooth  crown  and  slices  from  the  middle  of  the  root  become  clearly  visible  (C).  The  older  one  individual,  the  longer  can  the  distance  from  the  curves  of  different  slices  become  (D)  (Czermak  2006,  2012).        

A valid age at death estimation is required in historical and forensic anthropology. Tooth cementum annulation (TCA) provides a technique for age-at-death estimation of adult individuals. The approach uses light microscopic images acquired from tooth root cross sections. Age is estimated by counting the number of visible tooth cementum incremental lines and adding the result to the assumed age of tooth eruption. Manual line counting, however, is time consuming, potentially subjective and the number of individual counts is insufficient for quantitative evaluations. Here a custom-made software (AutoTCA) is presented that allows automated evaluation of TCA images. It involves Fourier filtering, ‘‘line-by-line’’ scanning and the counting of grey scale intensity peaks within a selected region of interest (ROI). Each scanning process of a particular ROI yields up to 400 counts, thus minimizing the potential error induced by manual line counting. This simple and time saving program can substitute manual counting and provides reproducibly consistent and user independent unbiased results. Reliability of the results, however, still depends largely on the state of preservation of the analysed material, the preparation, the choice of the thin section and image quality, underlining the need to standardize these factors.

Principle  of  tooth  cementum  annulaFon  (TCA)  

References  

Figure  11.  Power  spectrum  of  the  rotated  ROI.  (A)  Ver<cally  orientated  incremental  lines  are  grouped  near  the  central  horizontal  line  of  the  power  spectrum.  In  contrast,  non  linear  structures  or  linear  structures  orientated  in  different  angles  to  the  incremental  lines  in  the  original  image  (e.g.  saw  blade  marks)  are  represented  by  pixels  that  are  more  distant  from  the  horizontal  line.  (B)  To  completely  eliminate  interfering  ar<facts  a  point  symmetric  masked  was  applied  an  angular  filter  (20°)  was  used.  (C)  Masked  power  spectrum  of  rotated  ROI.    

Figure  9.  ScaWerplot  aLer  11x11  Gauss  low-­‐pass  filter  and  DFFT.  The  ROI  was  segmented  into  equal  rectangles  (128  pixels  x  length  of  ROI).  Each  segment  was  rotated  to  a  horizontal  posi<on,  using  the  outer  dark  line  as  a  reference.  Applica<on  of  Gauss  low-­‐pass  filter  and  Fourier  transforma<on  (DFFT:  discrete  fast  Fourier  transforma<on).  (A)  The  scaPer  plot  shows  a  preferred  direc<on,  which  is  extracted  by  fidng  a  straight  line.  (B)  This  line  determines  the  ideal  rota<on  angle  

Single counts Applied data

1 tooth ≈ 8000 each tooth mean value of all images

≈ 5 images ≈ 1600 each image mean value of all ROIs

≈ 4 ROI each image ≈ 400 each ROI mean value of the mode of the single counts of a ROI

≈ 400 Single-counts each ROI Mode of each ROI

Figure  6.  Slice-­‐secFon  in  a  bright  field  microscope.  Cementum-­‐layer  with  incremental  lines  (right  side),  den<ne-­‐layer  (leM  )  (magnifica<on  of  20x  diameters).  Picture  taken  with  the  incremental  lines  in  ver<cal  direc<on  for  a  bePer  program-­‐run.    

Sample  preparaFon,  microscopy  and  imaging  

Figure  5.  SecFon  cut  and  microscopic  image.  Sec<on  cuts  taken  in  an  90°-­‐angle  to  the  root  orienta<on  show  coincide  ring  structures.  In  the  bright  field  microscope  image  there  is  a  higher  contrast  between  bright  and  dark  lines  (medium  box).  Sec<on  cuts  along  the  axis  of  the  tooth  root  are  not  ver<cal  to  the  growing  line  of  the  cementum  and  they  show  rings  which  are  shiMed  and  not  completely  overlapping  (lower  box)  (Maat  2006).  Sec<ons  in  the  upper  and  medium  part  of  the  root  show  the  most  dis<nctly  and  visibly  lines.  (Figure  modified  aMer  Maat    2006)  

Figure  1.  Tooth  scheme  (longitudinal  cut).  The  root  area  is  surrounded  by  the  tooth-­‐cementum.  The  cementum  is  added  in  layers  on  the  bone-­‐side  of  the  tooth,  comparable  with  tree-­‐rings.  The  cementum  is  “nerved”  by  collagen  fibers  (“sharpey  fibers”),  which  are  fixing  the  tooth  in  the  alveolar  bone.  (Figure  modified  aMer  Schroeder  2001).  

Figure  2.  CorrelaFon  of  bands,  fiber  orientaFon,  crystal  structure  and  season.  Dark  and  bright  lines,  visible  in  transmiPed  light  microscope  are  supposable  correla<ng  with  variable  orienta<on  and  different  mineraliza<on  of  the  collagen  fibers.  Bright  bands  seem  to  be  developed  in  winter,  dark  lines  in  the  summer  season  (Liebermann  1994;  Stutz  2002).  The  changeover  of  the  bands  happens  in  March/April  and  September/October  (Wedel  2007).  (A)  The  varying  orienta<on  of  collagen  fibers  (Liebermann  1993,  1994;  assumed  orienta<on  of  the  sharpey  fibers  in  the  course  of  one  year  aMer  Wedel  2007)  and  (B)  the  (presumably)  consequen<al  orienta<on  and/or  size  of  the  crystals  (Cool  2002)  seem  to  create  the  phenotype  of  the  rings.  (Figures:  Czermak).    

Figure  10.  ROI  aLer  rotaFon.  Incremental  lines  are  now  accurately  orientated  in  verFcal  direcFon.  The  lines  are  oMen  disrupted  by  refrac<on  or  diffrac<on  ar<facts  (Fig.  6),  by  par<al  decomposi<on  of  the  tooth  or  by  linear  kerf  marks  caused  by  saw  blade  (arrows).  These  kerf  marks  are  similar  to  incremental  lines  and  in  case  of  a  parallel  course  they  can  influence  the  coun<ng  result  

Program  run   QuanFtaFve  evaluaFon  using  Auto-­‐TCA-­‐soLware  

Figure  8.  Region  of  interest  (ROI).  Several  points  have  to  be  marked  to  span  a  polygon  around  the  region  to  be  evaluated.  The  boundary  should  follow  the  bright  “erup<on  line”  on  one  side  and  the  dark  border  to  the  embedding  resin  on  the  other  side.  

Figure  7.  DiffracFon  arFfacts.  Ar<fact  lines  (arrows)  are  oMen  visible  on  the  interface  of  prepara<on  and  embedding  material  (magnifica<on  20x).  They  could  be  mistaken  for  incremental  lines,  mainly  on  images  with  lower  magnifica<on  than  40x  diameters  (Czermak  2006,  2012).    

(1)  Using  the  Auto-­‐TCA-­‐soMware  is  much  more  <me  saving  than  manual  coun<ng.  (2)  The  soMware  provides  user  independent,  consistent  and  reproducible  results.  (3)  Sta<s<cal  error  is  minimized  by  larger  number  of  single  counts  compared  to  manual  coun<ng.  

Abstract   Aims  of  this  project  (1)   CreaFon  of  an  unbiased  method  

to  subsFtute  manual  line  counFng  

(2)   OpFmize  sample  preparaFon  and  imaging  

(3)   QuanFtaFve  evaluaFon  using  Auto-­‐TCA-­‐soLware  

Digital  image  processing  DetecFon  of  the  ROI  VerFcally  orientaFon  of  the  “naturally  grown”  incremental  lines  

B  

A  A  

B  

C  

Figure  12.  (A)  ROI  aLer  image  analysis.  AMer  Fourier  back-­‐transforma<on  of  the  masked  spectrum  all  interfering  structures  are  eliminated  from  the  image.  (B)  Example  for  a  line  scan.  AMer  image  processing  the  applied  algorithm  creates  a  line-­‐by-­‐line  scan  of  pixel-­‐by-­‐pixel  gray  scale  values  of  the  processes  ROI.  Local  maxima  and  local  minima  and  local  minima.  Local  maxima  correspond  to  the  number  of  incremental  lines  in  this  row.  They  are  detected  and  counted  by  a  programmed  peak-­‐finder  algorithm.  

EliminaFon  of  interfering  structures  

VerFcally  orientaFon  of  the  “naturally  grown”  incremental  lines  

Table  1.  Number  of  single-­‐counts  each  counFng  level  and  applied  data  for  further  examinaFon  .  Each  coun<ng  process  generates  depending  on  ROI  size  300-­‐500  single  counts.  The  Auto-­‐TCA  soMware  shows  the  results  in  of  one  coun<ng-­‐process  in  a  window,  sorted  by  the  most  counted  line-­‐number,  in  downward  order.  The  “most  counted  value”,  the  mode,  of  a  ROI  was  taken  for  further  examina<on.  

grayscale  level  

width  of  ROI  

EvaluaFon  of  the  counFng  results  

[1]  Czermak  A,  Czermak  AM,  Ernst  H,  Grupe  G  (2006):  A  New  Method  for  the  Automated  Age-­‐at-­‐Death  Evalua<on  by  Tooth-­‐Cementum  Annula<on  (TCA).  Anthopologischer  Anzeiger  64  (1):  25-­‐40.  

[2]  Czermak  A  (2012):  Social  Stra<fica<on  in  the  Early  Middle  Ages  -­‐  Evidence  by  Demography,  Physical  Stress  and  Nutri<on.  (Soziale  Stra<fizierung  im  frühen  MiPelalter  –  Aussage  und  Nachweismöglichkeiten  anhand  von  biologischen  Indikatoren).  Disserta<on,  München.  

[3]  Cool  SM,  Forwood  MR,  Campbell  P,  Bennet  MB  (2002):  Comparison  between  bone  and  cementum  composi<ons  and  the  possible  basis  for  their  layered  appearances.  Bone  30  (2):  386-­‐392.  

[7]  Schroeder  H  (2001).  Orale  Strukturbiologie.  StuPgart,  Thieme.  [8]  Stutz  A  (2002).  Polarizing  Microscopy  Iden<fica<on  of  Chemical  Diagenesis  in  Archaeological  Cementum.  Journal  of  Archaeological  Science  

29:  1327-­‐1347.  [9]  Wedel  VL  (2007).  Determina<on  of  Season  at  Death  Using  Dental  Cementum  Increment  Analysis.  Journal  of  Forensic  Science  52  (6):  

1334-­‐1337.  

[4]  Liebermann  D  E  (1993):  Life  History  Variables  Preserved  in  Dental  Cementum  Microstructure.  Science  261:  1162-­‐1164.  [5]  Liebermann  DE  (1994):  The  Biological  Basis  for  Seasonal  Increments  in  Dental  Cementum  and  their  Applica<on  to  Archaeological  

Research.  Journal  of  Archaeological  Science  21:  525-­‐539.  [6]  Maat  G,  Gerretsen  R,  et  al.  (2006).  Improving  the  visibility  of  tooth  cementum  annula<ons  by  adjustment  of  the  cudng  angle  of  

microscopic  sec<ons.  Forensic  Science  Interna<onal  159(S):  95-­‐S99.  

Frühadult

0

5

10

15

20

25

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30Incremental lines

Häu

figke

it [%

]

Schnitt3/Zählung1 Schnitt3/Zählung2 Schnitt3/Zählung3Schnitt4/Zählung1 Schnitt4/Zählung2 Schnitt4/Zählung3Schnitt5/Zählung1 Schnitt5/Zählung2 Schnitt5/Zählung3

Mitteladult

0

5

10

15

20

25

15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45Incremental lines

Häu

figke

it [%

]

Schnitt3/Zählung1 Schnitt3/Zählung2 Schnitt3/Zählung3Schnitt5/Zählung1 Schnitt5/Zählung2 Schnitt5/Zählung3Schnitt6/Zählung1 Schnitt6/Zählung2 Schnitt6/Zählung3

Spätadult

0

5

10

15

20

25

5 10 15 20 25 30 35 40 45 50Incremental lines

Häu

figke

it [%

]

Schnitt3/Zählung1 Schnitt3/Zählung2 Schnitt3/Zählung3Schnitt4/Zählung1 Schnitt4/Zählung2 Schnitt4/Zählung3Schnitt7/Zählung1 Schnitt7/Zählung2 Schnitt7/Zählung3Schnitt8/Zählung1 Schnitt8/Zählung2 Schnitt8/Zählung3

Mittelmatur

0

5

10

15

20

25

15 20 25 30 35 40 45 50 55 60Incremental lines

Häu

figke

it [%

]

Schnitt2/Zählung1 Schnitt2/Zählung2 Schnitt2/Zählung3Schnitt3/Zählung1 Schnitt3/Zählung2 Schnitt3/Zählung3Schnitt8/Zählung1 Schnitt8/Zählung2 Schnitt8/Zählung3

Occurrence  

Occurrence  

Occurrence  

Occurrence  

slice3/count1  slice4/count1  slice5/count1  

slice3/count2  slice4/count2  slice5/count2  

slice3/count3  slice4/count3  slice5/count3  

slice3/count1  slice5/count1  slice6/count1  

slice3/count2  slice5/count2  slice6/count2  

slice3/count3  slice5/count3  slice6/count3  

slice3/count1  slice4/count1  slice7/count1  slice8/count1  

slice3/count2  slice4/count2  slice7/count2  slice8/count2  

slice3/count3  slice4/count3  slice7/count3  slice8/count3  

slice2/count1  slice3/count1  slice8/count1  

slice2/count2  slice3/count2  slice8/count2  

slice2/count3  slice3/count3  slice8/count3  

early  adult  (aged  20-­‐24)   middle  adult  (aged  25-­‐31)  

late  adult  (aged  32-­‐38)   middle  mature  (aged  46-­‐52)  

TCA:  aged  27  

TCA:  aged  41  

TCA  (1):  aged  37   TCA  (2):  

aged  40  

TCA  (1):  aged  45  

TCA  (2):  aged  52  

czermak_andrea@web.de  Corresponding  address  

Auto-­‐TCA-­‐soLware  

Image  quality  is  crucial  for  valid  line  counFng.  

The  soLware  provides  reliable  counFng  results,  but  does  not  validate  the  TCA-­‐method.  

B  A  

C   D  

TCA-­‐method  (1)  Using  the  Auto-­‐TCA-­‐soMware  is  much  more  <me  saving  than  manual  coun<ng.  (2)  The  soMware  provides  user  independent,  consistent  and  r