臺灣博碩士論文加值系統

牙齒比色在臨床上一直是很重要且被廣泛使用的專業技術，但要讓比色結果
臻至完美，卻是件不容易的事，傳統比色是以牙科專用比色板置於病患牙齒處，
比對相鄰的牙齒，然後利用肉眼讀出結果。近年內有研究利用色度計或分光光度
計等電腦儀器所得的數位色差分析結果來比色，才提供了另一個牙齒比色的新選
擇。但是，由於數位色差所呈現的顏色特徵過於貼近其他齒色，甚至產生重疊，
因而產生分類困難的情況。也因此以電腦方法所得的比色準確率僅達50%，而傳統
肉眼的準確率也有48%，因此雖然報告顯示高科技裝置在重複比色時有好的穩定
性，但與傳統視覺的比色結果，卻無顯著差異，這樣的結果也就不至於令人感到
太驚訝了。
高畫數數位相機在現今社會已非常普通，對牙醫專業來說，這些相機有優點
也有缺點。優點是如果不涉及複雜的比色校正功能，數位相機可簡便且有效的幫
助牙醫師取得色彩鮮豔及影像清晰的臨床照片，缺點是在不同的照明條件下，一
般數位相機很難維持穩定的顏色特徵•其次，雖然數位相機能使牙齒呈現精確的
形狀及色彩分布，但不一定能精確的呈現色彩本身。因此，讓一般數位相機能符
合牙科臨床使用，這對牙科比色來說有著莫大的幫助。本研究的目的，就是要創
造一個可能的機制，經由演算法的運用，進而使得不同光源下所擷取的照片，也
能呈現一致且穩定的色差特徵。
本研究的主題是在不同的照明環境下，使用一般數位相機可以成功的比對出
正確的色彩區塊。利用分析和比較不同色彩空間的齒色差異找出穩定及有代表性
的顏色特徵，進而建立對相近齒色區塊的測量標準。對於不同取樣方法也進行分
析。本齒色測量法是結合有效的測量標準特徵及取樣方法而成。結果顯示，本實
驗的測量法其前一名的準確率大於50%，已明顯優於其他學者以電腦分析之準確
度，而本測量法的前五名，準確率更高達90%以上，這對臨床比色應用來說，非常
有意義。測量法也說明齒色層次〈自齒頸部切端之變化〉對比色結果有重大影響，
而且樣本平均值無法有效用於牙齒比色，說明了樣本間有相當的空間關悉存在，
因此樣本無法求其平均作運算。在本實驗中△E 直無法有效發揮應有之表現，相對
的，我們發現HSV 色彩空間中的S 特徵及Lab 色彩空間中的a.b 特徵在一般照相
機及臨床條件下能得到最好的比色效果。

Dental shade matching is an important but a difficult task in clinical practice.Traditionally, a shade guide is manually placed adjacent to the matching teeth and thenreadout the results by naked eyes. Only until recently, digital shade analysis by mean of colorimeter or spectrophotometer provides an alternative. However, the color features of the shades do not only closely tight to each other, but also exhibit overlapping, they are difficult to be classified. The successful matching rate of computerized methods only
reach about 50%, while that of naked eyes is 48%. Thus, it is not surprising that these high-technology devices, though reported to be stable in repeated matches, have been revealed no significant difference from traditional visual matching.
Consumers’ level mega-pixels digital cameras become popular. They share some
merits and dis-merits to dentists. They are convenient and helpful in roducing sharp and colorful images in clinical practices only if calibration for shade selection is not
involved. Besides, consumers’ digital cameras when exposed in different illumination
conditions can hardly produce photographs that retain color features. Furthermore,
dental photos often tell exactly the shape and the color distribution of the teeth, but not
the color itself. In order to modify a consumer digital camera so that it is helpful in
clinical shade selection, this research proposes a possible mechanism. An algorithm is
devised to normalize the illumination so that shade features are stable and retainable
when capturing under different light sources.
The objective of this research is to successfully match a content of a shade from a
shade guide that ignoring the illuminant condition using a commercialized digital
camera. The color spaces of teeth shades are being analyzed and compared. The stable
and representative color features will then be selected. New measurements have
V
established in this study to measure difference between contents of similar shades.
Different sampling methods are also discussed. The shade metrics are composed of
effective measurements, features and sampling methods. After comparing the results, it
shows that, with out proposed metrics, over 50% matching rate in top one match that is
a little bit higher than the previous research which reported about 50% by computerized
match. The top five matches have near or higher than 90% that is very helpful in clinical
application. Those measurements counting for color gradation along the contents, in
turn along the crown cervico-incisal axis, will have better matching results which means
that spatial relations within the color contents play an important role in shade matching
and cannot be neglected. It is also told that sample mean calculated from the whole
sample group is no longer useful in shade matching due to the spatial relationship of the
features that they cannot be averaged. E of Lab color space has △ s long been used for
measuring dental color difference, but is shown to be an impropriate measurement in
this study. In addition, S feature from HSV color space together with a* and b* features
from Lab color space are shown to be suitable features for shade matching in clinical
condition using digital camera.

1. Introduction ...............................................................................................................1
1.1 Dental shade matching in dentistry today ................................................ 1
1.2 Importance of shade selection .................................................................. 1
1.3 Factors affecting shade selection.............................................................. 2
1.4 Difficulty of shade selection..................................................................... 3
1.5 Recent technique of shade matching ........................................................ 3
1.6 Modern technologies which can help in shade selection.......................... 5
1.7 The role of image processing and analysis in esthetic dentistry............... 5
1.7.1 Color spaces of dental shade ............................................................ 6
1.7.2 Metrics useful in shade measurement............................................... 6
1.8 Possible solution to shade matching using technique of content-based
image 7
2. Historical review .......................................................................................................8
2.1 Problem of shade selection ....................................................................... 8
2.2 Color science of dentistry ........................................................................11
2.3 Similarity measurement..........................................................................12
2.4 Content-based regional measurement.....................................................13
3. Backgrounds and motivation...................................................................................15
4. Materials and methods.............................................................................................16
4.1 Materials.................................................................................................16
4.2 Methods..................................................................................................16
4.2.1 To select suitable color spaces for dental shades............................ 16
4.2.2 To propose suitable metric measurement of dental Shade ............. 16
VII
4.2.3 To reduce illuminant effect on shade selection............................... 18
4.2.4 Data Sampling ................................................................................19
4.3 Procedure................................................................................................21
4.3.1 System flow ....................................................................................21
4.3.2 Images collection............................................................................21
4.3.3 Preprocessing..................................................................................21
4.3.3.1 Block content segmentation by manual cropping on the images
21
4.3.3.2 Light reflection removal by manual threshold binarization ... 23
4.3.3.3 Blocks segmentation............................................................... 23
4.3.4 Features extraction..........................................................................24
4.3.4.1 Color features extracted from different color spaces.............. 24
4.3.5 Similarity measurement..................................................................24
4.3.5.1 Metric measurements.............................................................. 24
5. Results .....................................................................................................................25
6. Discussions..............................................................................................................71
6.1 Impact of illumination ...................................................................................... 71
6.2 Color spaces and features ................................................................................. 71
6.3 Shade measuring methods ................................................................................ 72
6.4 Proposed metrics .............................................................................................. 72
7. Conclusion................................................................................................................74
References .......................................................................................................................75

1. Our proposed M4 metric behaves better than the colorimeter reviewed in the past
journal. The result of top five matches will greatly increase the matching rate and
help in clinical application.
2. The concept of multi-expert system that may lead to better result if we can select
the proper shade through more than one metrics or classifiers. The priorities of
these metrics or classifiers have to be learnt through professional knowledge.
These kinds of methods may need further investigation based on our final
conclusions.
3. Customers’ level digital camera can be use in professional dental shade selection.
The outcome matched by mega-pixels camera will not inferior to those performed
by high costed colorimetric or spectrophotometric devices if correct metric is
employed.
4. △E value has long been use in dental color similarity measurement. It seems that
we have another better metric to take the job, but still need further investigation.
5. The sample of shade guides captured at different time intervals cannot be averaged
for statistic mean. It seems that there exist certain degree of spatial relationships
between shade samples that they should be counted individually.
75
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