心臟超音波影像分割之研究

Abstract

我們提出兩種以區塊為主的影像分割方法及兩種以邊緣為主的影像分割方法，其中所提出的第一個以區塊為主的方法是乏析賀菲爾德(Hopfield)類神經網路，在這個方法中使用了全體的和區域性的影像亮度資訊來建構這個類神經網路，當網路趨於穩定狀態時，就可以獲得影像分割的結果；另一個提出的以區塊為主的方法是α形狀技術的分割方法，選擇了適當的α大小之後，每個區塊會對應到其中的一個分群中，再藉由和AQ的比對，就可以知道哪個分群是我們所要的心臟了。而所提出的第一個以邊緣為主的方法是使用圖形理論中尋找最短路徑的方法，我們先將影像以圓形的方向展開，並對應成一個有方向性的圖形，為避免落入小區域中的最佳值，我們使用了動態程式的方式找出最佳路徑；另一個以邊緣為主的方法是適合於物體形狀的邊緣不像圓形的影像分割，我們使用了α周線來建構尋找的範圍，並將其對應至有方向性的圖形中，同樣的用動態程式的方法尋找最佳路徑。 另外我們也提出了從心臟超音波影像中擷取心臟瓣膜線的方法，在這個方法中需要一個專業醫生提供左心室內的一個點，結果的平均誤差是3%，這在臨床上是可以接受的，這可以大大的節省醫生使用機器的時間，轉而將時間花在病人身上。

In this dissertation, we propose two region-based segmentation algorithms and two edge-based segmentation algorithms for echocardiographic images. The first proposed algorithm of region-based segmentation scheme is fuzzy Hopfield neural network with fixed weight approach. This approach incorporated the global gray-level information and local gray-level information to construct a fuzzy Hopfield neural network. When the network converges to a stable state, the segmentation result will be obtained. A new approach using alpha-shape points is another proposed algorithm of region-based category. The region of interest corresponds to one of the clusters under a properly selected alpha. We identify the heart chamber in the ultrasound image by comparing the similarity between the alpha-connected components against the heart chamber obtained from the AQ image. The first proposed algorithm of edge-based segmentation scheme is finding the shortest path in directed graph. We circularly spread the image first and then map it to a directed graph. To avoid the local minimum trapping, dynamic programming approach is used for finding the shortest path. The other proposed approach for edge-based segmentation algorithm is suitable for non-circular like boundary. We incorporated an alpha-contour approach based on alpha-shape technique to construct the search space and then map it to a directed graph. The dynamic programming technique also used for finding the shortest path. In additional, we also propose a new approach for extracting mitral annular lines for echocardiographic images. A nearly automatic method for calculating the mitral annular lines from a 2D+1D precordial echocardiogram four-chamber view was presented. The proposed method needs only a physician to provide a point in the left ventricular chamber. The average error was 3% which is clinically acceptable. The proposed method saves much clinician time, allowing a shift from machine to patient care.