急性顱內血腫在電腦斷層影像中的電腦輔助診斷

Abstract

外傷或中風引起的顱內血腫，會造成腦部的壓迫，是神經學的急症，也是公共衛生的重大議題。即時的診斷與迅速的治療，是改善顱內血腫病人預後的關鍵因素；然而對非專科醫師而言，在電腦斷層影像中評估這些病灶並不容易。本論文探討顱內血腫在電腦斷層影像中的電腦輔助診斷，並結合二值性等位集合法與多解析度處理，提出新的影像分割方法。 我們運用C4.5演算法發展出判斷顱內血腫類型的決策樹，並發現不同解析度的決策樹都具有良好的效能。利用多解析度二值性等位集合法，我們可以在電腦斷層影像中穩定的分割出顱內血腫的區域。在區分腦實質與顱內血腫之前，我們整合解剖學的知識將顱內區域、顱骨及顱外區域分割出來。本系統對顱內血腫的定性及定量診斷，與人類專家的結果相比，有相當高的一致性。本論文的後半段探討顱內血腫造成腦部壓迫的徵象的電腦輔助診斷。我們利用兩種方法來測量中線偏移：運用對稱的方法與運用解剖構造的方法。前者利用二次貝茲曲線與一維對稱的性質找出偏移的中線，並利用基因演算法來進行參數的最佳化；後者則在運用影像分割找出腦室的前角之後，再運用霍夫(Hough)轉換將其中的透明膈辨識出來。最後，我們描述了運用霍夫轉換找出顱底的腦池區域，並判斷此區域被壓迫的程度。

Intracranial hematomas, either traumatic or spontaneous, can produce fatal outcomes because they can produce local pressure on the brain. Accurate diagnosis and rapid decision making are the key factors to good patient outcome. This thesis introduces new methods capable of obtaining the features of the intracranial hematomas in brain CT images. In addition, a new approach of image segmentation integrating binary level set method and multi-resolution processing is proposed. We develop the decision rules to recognize the type of the intracranial hematoma on CT slices with large intracranial hematomas using C4.5 algorithm. These decision rules work well in different resolutions. To obtain robust segmentation of the intracranial hematoma regions, we introduce a multi-resolution binary level set method using image pyramids and apply it to hematoma segmentation. Prior to segmentation of the hematoma from the brain, anatomical knowledge is integrated with image processing techniques in the segmentation of intracranial regions. The results show excellent precision and recall as verified by human experts. In the second half of this thesis, we describe two methods for automatic measurement of the midline shift (MLS). The first one employs symmetry and curve fitting to measure the MLS of the CT slice at the level of Foramen of Monro. Genetic algorithm is used for parameter optimization. Landmark-based MLS recognition is carried out by first segmenting the frontal horn region followed by a knowledge-driven rule. Hough transform (HT) is then applied to locate the septum pellucidum. Finally, we describe automatic recognition of the basal cisterns using HT. This method is able to pick out the normal or compressed basal cistern region from the given CT data set.