陰道鏡對子宮頸癌的自動化輔助診斷

Automatic Diagnosis of Cervical Cancer in Colposcopy

李新澤(Shin-tse Lee)

指導教授 ： 戴紹國

朝陽科技大學/資訊學院/資訊管理系/碩士(2012年)

摘要

陰道鏡是子宮頸癌篩檢中非常重要的工具，醫師可以透過它觀察子宮頸上皮組織的實際情況判斷病變等級。由於病變情況沒有客觀的量化標準，所以必須完全依靠醫師的主觀判斷。若是醫師經驗不足，則準確度會降低。我們可以運用電腦影像技術去輔助醫師確認病灶位置，並且提供病灶癌化程度的量化數據與分級結果，藉此讓醫師進行更精準的診斷。本研究可以自動化切割出病灶位置，並且將癌化特徵抽取出來進行分級。病灶的切割過程使用Fuzzy k-mean clustering、自我組織圖網路和分群品質測量進行影像切割。接著抽取病灶的特徵向量，我們使用HSV color model取得病徵的特徵向量。針對特徵向量使用SVM、KNN分類器進行分級。我們使用白化特徵進行分級，正確分級成功率達87.2%。我們相信自動化的輔助診斷系統，能夠幫助醫師進行診斷及預後工作。

關鍵字

分類器 ； 自我組織圖網路 ； 子宮頸癌 ； 陰道鏡 ； 影像切割

並列摘要

The morbidity and mortality of cervical cancer can be reduced by the screening of the precancerous lesions. Pap smears, colposcopy and biopsy are the most common screening tools. Pap smear is the first-line tool because of its high specificity and low cost. But its false-positive rate is too high and must be confirmed by other tools. Biopsy is a deterministic examination for cervical neoplasia. However, it is not suitable for the high probability of false-positive. Digital colposcopy is a promising technology for the detection of cervical intraepithelial neoplasia. However, there are no quantitative criteria for the differential of precancerous lesions and it is subjected to the variation of inter-observer and intra-observer. Therefore, automated image analysis of colposcopic images is thus necessary for the improvement of diagnosis of colposcopy. The segmenetation of the lession from digital colposcopyic image is a key issue of this analysis. Our goal is to develop a segmentation policy that can separate images into regions which contain the lesion areas. These areas can be provided to the analysis system and help doctor to make the diagnosis.

並列關鍵字

classifier ； cervical cancer ； colposcopy ； sementation ； self-oranizing map

參考文獻

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