子宮頸抹片細胞之電腦輔助診斷系統

Computer-aided Diagnosis System for Pap Smear Cells

黃俊魁(Chun-Kuei Huang)

指導教授 ： 蘇振隆

中原大學/工學院/醫學工程研究所/碩士(2006年)

https://doi.org/10.6840/cycu200600183

摘要

摘 要
子宮頸癌為台灣地區婦癌的致命疾病之一，其發生率高居五大婦癌的榜首。
傳統上以子宮頸抹片檢查為預防子宮頸癌最好的方式。抹片檢查中，以往主要是
透過人工觀察來辨別正、異常細胞與異常細胞型態如LSIL與HSIL。本研究主要目
的為使用資訊科技發展一套系統，以分析子宮頸癌發生之可能相關細胞型態及特
徵參數，並提供臨床醫師在子宮頸癌前期之診斷輔助。
本研究所運用之技術包含影像處理技術、倒傳遞類神經網路訓練、資料庫建
立、適用性評估及分析與介面之開發等。先將彩色轉為灰階後，進行雜訊去除，
並利用形態學與鏈碼技術來圈選與記錄輪廓。將此輪廓套於灰階與彩色影像進行
區分背景、細胞質及細胞核。針對各區分別進行影像之特徵選取，如：RGB、HIS、
Entropy、Contrast、N/C Ratio，及統計分析結果，作為辨識之參數及類神經網路之
發展。研究過程中，利用120張影像(60張正常細胞影像，60異常細胞影像)進行類
神經網路模型之訓練。並且以另外32張影像(11張正常細胞影像，21異常細胞影像)
進行測試，配合臨床的結果來評估系統的正確性。另外，參考臨床醫師的意見與
介面的發展來提升系統的實用性。
結果顯示，統計上P值的分析與類神經網路訓練結果的相關性是息息相關的，
而系統在對訓練組之抹片影像訓練辨識正常或異常與異常型態辨識時，其
Accuracy、Sensitivity與Specificity值均為1。對測試影像組測試結果，可以發現
Accuracy、Sensitivity與Specificity分別為0.97、1、0.91及Kappa值0.93。單以N/C ratio
分析結果，其正常細胞的核值比為小於0.1，LSIL為0.1～0.2，HSIL為0.2～1之間。
雖核值比在判斷的權重占絕大部分，但是色彩中核濃染程度也很重要。本系統根
據色彩與灰階和核值比分佈狀態進行判斷，能夠準確的判斷正常細胞，與異常細
胞中LSIL與HSIL狀態。在測試影像誤判僅有一例為正常的發炎細胞，其因背景與
黏液等複雜因素，而影響影像系統診斷上的結果。
從整個系統而言，本研究修改了過去僅能對單一顆細胞進行分割的缺點並適
度修改判讀參數，故能一次分析多顆正異常的細胞，而提升了判讀的效率及系統
的準確性。希望本輔助診斷系統能夠協助醫師在臨床上細胞的參數量化取得與資
料庫連結等介面能夠更有效率的解決任何臨床上有關子宮頸抹片上的相關問題。

關鍵字

形態學 ； 影像處理 ； 倒傳遞類神經網路 ； 子宮頸抹片 ； 資料庫 ； 鏈碼技術

並列摘要

Abstract
Cervical cancer is one of deadly diseases of cancers with highest occurrence rate
for women in Taiwan. Pap smear is the best inspection examination to prevent cervical
cancer. In viewing the pap smear, normal cells and abnormal cells which including Low
grade Squamous intraepithelial lesion (LSIL) and High grade Squamous intraepithelial
lesion (HSIL) were distinguished by physician. The purpose of this study is applied the
information technology to develop a system that can analyze the cell types and
characteristic parameters of cervical cancer, and assist physicians to diagnosis cervix
cancer in earlier stage.
Some techniques such as: image processing, Back Propagation Networks (BPN)
training, database storage, suitability assessment and analyzing, and interface
development were used in this study. First of all, color cell images were transformed
into gray level images. Through noises removed, morphology, and chain code
techniques, the contour of image was circled and recorded. Then the gray and color cell
images were segmented into three areas which are background, cytoplasm, and nuclear.
Feathers that include RGB, HIS, Entropy, Contrast, and Nuclear/Cytoplasm (N/C) ratio
were acquired from images, and the result from statistical analysis were served as
distinguish parameters for the development of BPN. In order to train the BPN model,
120 image cases including 60 normal and 60 abnormal cases were used in this study.
Then, 32 testing images including 11 normal and 21 abnormal cases and clinical
diagnosis results were used to evaluate the accuracy of system. Moreover, according to
the opinions from doctors, a friendly interface was developed for increasing the
practicality of this system.
Results show that P values from statistical analysis and the training result from
BPN is highly correlated. For training images, the system perform well in distinguish
cell type with accuracy, sensitivity, specificity were 1. For the testing images, the
accuracy, sensitivity, specificity and kappa value were 0.97, 1, 0.91 and 0.93,
respectively. Moreover, the N/C ratio for normal cells is less 0.1, for LSIL is between 0.1 and 0.2 , and for HSIL is between 0.2~1. Although the weighting value for N/C ratio
is highest in BPN model, the color and other gray parameters are also play important
roles to diagnose. Color, gray level, and the distribution of N/C ratio were used to
distinguish normal and abnormal cells which include LSIL and HSIL. During this study,
only one normal inflammatory cell case in test image was misjudged, because of some
effects like noisy background, mucus and other complexities effects that can affect the
results of diagnosis.
In conclusion, compared with previous study this system is improved in efficient
and accuracy. This system could not only segment and analyze multiple cells at one
time, but also provide better result with new parameters selection. In the near future, we
hope this system can provide more useful diagnosis information from cells for
physicians, and can solve any relational problems about pap smear more efficiently thru
database communication.

並列關鍵字

Back Propagation Networks ； Morphology ； Pap smear ； Database ； Chain code technique ； Image Processing

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