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頁籤選單縮合
題 名 | Auto-segmentation of Ultrasonic Images by the Genetic Algorithm=利用基因演算法自動分割超音波影像 |
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作 者 | 許益彰; 江青芬; 汪啟茂; | 書刊名 | Journal of Medical and Biological Engineering |
卷 期 | 21:2 2001.06[民90.06] |
頁 次 | 頁121-126 |
分類號 | 416.13 |
關鍵詞 | 基因演算法; 超音波影像; 影像分割; 紋路特徵; Genetic algorithm; Utrasonic images; Sgmentation; Txtural features; |
語 文 | 英文(English) |
中文摘要 | 超音波影像常利用紋路特徵來作為影像分割的依據,而紋路特徵的篩選,是影像分割是否成功的首要關鍵。目前,這方面的研究在紋路特徵的篩選,大都靠人為判斷,這不但降低了整體分割方式的適應性,且往往會造成分割結果的錯誤。因此,本研究針對超音波影像,發展了一套利用基因演算法自動篩選紋路特徵的分割方法,以解決人為篩選的缺點。本方法分三個步驟:首先,建立一含有32組特徵的特徵資料庫;接著,再利用基因演算法對這32組特徵做篩選,並根據其在最後汰選結果的出現率給予其權值。而汰選所根據的適應條件是由邊緣相鄰區域的相似性、邊緣長度、邊緣完整性、邊緣厚度、以及邊緣曲率所評估而得的。最後,根據所選出的特徵及其比重以k-means方式做聚集分類。比較本研究方式所呈現的分割結果與人為篩選的結果顯示,前者更接近人眼辨識的分類。此外,本研究首次提出以特徵權值克服了傳統k-means聚集演算方式中,所有特徵的比重相等而造成較大特徵值會主導分類結果的缺點。 |
英文摘要 | The textural-feature-based segmentation methods were widely applied to the segmentation problems of ultrasonic images. However the manual selection of textural features in the previous approaches not only makes these segmentation methods inadaptable but could lead to the results with bias. Herein we propose an auto-feature-selection algorithm to solve the problems. This algorithm includes three steps: The feature library composed of 32 textural features was established at first. The genetic algorithm was then used to auto-select the features and give each of them different weight according to their importance. The fitness of each gene was evaluated by five factors including region dissimilarity, number of edge points, edge fragmentation, edge thickness, and curvature. Finally, K-means process classified the image into 3 different tissues using the selected features with different weights. The segmentation outcomes of various ultrasonic images by this auto-feature selection algorithm have shown better correspondence with human comprehension in comparison with the results of previous works. In addition, it provides a more adaptive way to adjust the weight of the features used for clustering process and therefore to avoid takeover by the big-value features. This problem has been paid little attention in the traditional K-means process in which all the features have the same weight. |
本系統中英文摘要資訊取自各篇刊載內容。