本文選題：超聲圖像分割　切入點：Gabor特征　出處：《東南大學學報(自然科學版)》2017年05期

【摘要】：為了提高前列腺超聲圖像的分割精度,提出了一種基于改進主動形狀模型的前列腺超聲圖像分割算法.首先,提取前列腺超聲圖像的特征集合,該特征集合由Gabor紋理特征和局部二值模式(LBP)特征組成.然后,通過利用k均值算法對提取的特征集合進行聚類分析,得到超聲圖像的聚類表示圖.最后,在聚類表示圖上應(yīng)用ASM獲取超聲圖像中前列腺的形狀信息.結(jié)果表明,該算法可以準確地定位前列腺邊界信息,與醫(yī)生手動標記的前列腺輪廓相比,平均絕對距離僅為1.559 6 mm,戴斯相似度系數(shù)最高可達93.88%.利用超聲圖像的聚類表示圖可以獲得更加精確的前列腺輪廓信息,可用于海扶高聚焦超聲(HIFU)手術(shù)中的精準導(dǎo)航.
[Abstract]:In order to improve the accuracy of prostate ultrasound image segmentation, an algorithm based on improved active shape model is proposed. Firstly, the feature set of prostate ultrasound image is extracted. The feature set is composed of Gabor texture feature and local binary pattern feature. Then, by using k-means algorithm to cluster the extracted feature set, the clustering representation diagram of ultrasonic image is obtained. The shape information of prostate in ultrasonic image is obtained by using ASM on the cluster representation map. The results show that the algorithm can accurately locate the boundary information of prostate, compared with the prostatic contour marked manually by doctors. The average absolute distance is only 1.559 mm, and the highest similarity coefficient of Deiss can reach 93.88. By using the clustering representation of ultrasonic images, more accurate information of prostate contour can be obtained, which can be used for accurate navigation of HIFU in HIFU.
【作者單位】： 東南大學計算機科學與工程學院;東南大學計算機網(wǎng)絡(luò)和信息集成教育部重點實驗室;東南大學中法生物醫(yī)學信息研究中心;
【基金】：國家自然科學基金資助項目(31571001,61201344,61271312,61401085,81530060) 江蘇省自然科學基金資助項目(BK2012329,BK2012743,BK20150647,DZXX-031,BY2014127-11)
【分類號】：R737.25;TP391.41
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本文編號：1673316