• 沒有找到結果。

5.1 結論

本論文主要發展簡單快速且精確的方法來協助醫生們的診斷,而由於現實生活有許 多事物的性質是難以用二值邏輯來加以描述的,多有著模糊層面,且加上模糊邏輯容易 運用,常常可適用於非線性或是系統不完全的地方上,並且有著顯著的效果存在,所以 使用此套推論法則來概略區分人體電腦斷層造影圖像,並於其後由邊界跟隨演算法為主 來萃取出只含有肺部的成分並把其他無關的物體影像資訊去除,隨後將只剩下肺組織的 影像以 Polynomial fitting 根據前後的關係模擬標示出肺裂隙來,期望能達到輔助醫生於 醫學上的各種病灶診斷以及醫療處理。

5.2 貢獻

本論文主要的貢獻為當在讀入所有病人的全身電腦斷層掃描影像資料後,可以自動 的分類出頭、胸以及腹部,如此不需要再以人工方式做處理,畢竟每個人的醫學影像是 很多張數的,手動分類將會耗費相當大量的時間,因此在自動分類後就可以針對待診斷 和處理的部位做集中性的研究,省下許多的時間。而目前我們研究部位位於肺部,所以 輔以自動分離出肺組織便於之後的各種肺病灶判讀,更加使診斷精確與操作容易。

5.3 未來研究

最後期望在未來的時間內能坐以下三項改善:

1. 自動找尋出使肺裂隙

為了使系統完全自動化的處理,而不再需要在某些執行點上由人工方式進行,使其

2. 更精確的標示出肺裂隙

目前我們使用的方法仍然不能很精確的把肺裂隙給標示出來,如果能更準確的將裂 隙指出,那麼在對於病灶位置上的判定將會更佳的明確清楚,治療上也會將有著良 好的功效。

3. 其他方向的身體器官的影像處理或辨識

期望也能朝向其他的身體內臟或組織的影像處理研究,發展出相關系統輔助醫療上 的診斷和判讀。

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