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  • 學位論文

肺部腫瘤偵測之電腦輔助診斷系統

Computer-aided Diagnosis System for Lung Nodule Detection

指導教授 : 蘇振隆
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摘要


在放射醫學上,依據胸腔X光片影像上的表現,來做肺癌的初步診斷頗具困難度。當對X光片影像的結果懷疑有不正常的組織時,則進一步使用CT、MRI、支氣管或切片等方式來診斷。因此本研究在發展一演算法用於胸腔放射影像之肺部腫瘤,偵測可疑腫瘤的可能存在部位,提供醫師做為參考。 方法上以差值影像為基礎,對差值影像作閥值的選取並進而對選取閥值後的影像以似圓性來圈選可疑腫瘤區域,然後再以兩種演算法對圈選出來的可疑區域進行處理而減少FP數目。方法一為利用生理資訊(灰階度判斷是否鈣化,位置判斷是否為縱膈…等)來減少FP數目;若FP數降低不足再以方法二利用監督式的倒傳遞類神經網路來降低FP數目。本系統所使用的影像來源,包含假體影像及臨床之胸腔X光影像。藉由假體影像來判斷程式之正確性,再進而應用到真實的病人影像中,進行臨床可行性的評估。此外,也藉由臨床醫師之協助比較系統與傳統人工判讀之區別。 系統除了提供面積之計算外,在假體測試中均成功的測得目標物。在真實影像之測量中,在閥值為30%條件下,對一般腫瘤較少的病人其敏感度均能達到1,再加入生理資訊與類神經網路的方法,及FP數目可由24.92個/每張影像下降到3.06個/每張影像,符合醫學診斷之要求。對腫瘤較多之影像,在閥值為32%條件下,其敏感度可以達到0.96667,加入生理資訊與類神經網路的方法,及FP數目可由17.33個/每張影像下降到2個/每張影像。與其他系統比較中,本系統的FP數較少,且準確性較高。 由以上結果顯示,本研究在假體影像與真實影像上都可以偵測到腫瘤的存在,並且降低FP的數目。另外系統也提供較彈性的使用介面,頗富使用之便利性。未來對部分參數,如:面積與系統運算時間做更進一步的改善,將使系統更具有臨床意義,並且達到輔助醫生診斷上的目標。

並列摘要


In radiology, it's quite difficult to make a preliminary diagnosis of lung cancer by using a chest X-ray image. When an uncertainly abnormal tissue occurred in X-ray image, some advanced tests, such as CT, MRI, bronchotomy , or microtomy , will be made for more detailed diagnosis. The aim of this study was to develop an algorithm applying in detecting lung nodules on chest radiological image, and mark possible region of suspicious nodules for doctor's diagnosis. The detecting method was based on difference image. At first, we selected a threshold for all image and mark all suspicious nodules regions by circularity at difference image. Then, two algorithms were used on the suspicious regions for reducing the number of false positive(FP). A biological information which is determine whether calcification by gray scale and verify whether mediastinum by position was used to reduce the number of FP. If it didn't show great effect, artificial neural network (ANN) was applied to reduce FP number. The source of our image contained phantom and clinical chest X-ray image. By using of phantom image , the correctness of our algorithm could be evaluated. The algorithm was applied on real clinical patient image to evaluate its clinically practical. Besides, by the aids of clinical doctors, we found that the difference of performance between our system and traditional method could be distinguished. Our system provided the calculation of suspicious area and successfully detect the nodules in phantom. In real image, the sensitivity approaches to 100% for less nodules patients when applied the gray level of 30% in cumulative histogram as a threshold. With the aids of biological information and ANN, the FP number went down from 24.92/per image to 3.06/per image. It satisfied the demand of medical diagnosis. For more nodules image, the sensitivity approached to 0.96667 when applied 32% gray level as a threshold. With the aids of biological information and ANN, the FP number west down from 17.33/per image to 2.00/per image. Comparing with other systems, this system obtained less FP and higher accuracy. Result showed that we can detect nodules in either phantom or real image successfully with less FP number. Besides, our system provides flexible user's platform. In the future, some functions, such as area and calculation time ,will be improved so that the system can be more clinically practical and achieve the goal of assisting the diagnosis of doctor.

參考文獻


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被引用紀錄


鄭煥勲(2006)。肺臟腫瘤於動態顯影CT影像之特徵分析〔碩士論文,中原大學〕。華藝線上圖書館。https://doi.org/10.6840%2fcycu200600638
賴宣宇(2005)。電腦輔助診斷系統於乳房腫瘤X光與超音波影像之整合應用〔碩士論文,中原大學〕。華藝線上圖書館。https://doi.org/10.6840%2fcycu200500815
葉嘉芬(2003)。利用三維形態分析診斷肺臟腫瘤之系統〔碩士論文,中原大學〕。華藝線上圖書館。https://doi.org/10.6840%2fcycu200300137
范振添(2002)。醫療影像傳輸及資料探索之系統開發〔碩士論文,中原大學〕。華藝線上圖書館。https://doi.org/10.6840%2fcycu200200482

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