2D texture based classification, segmentation and 3D orientation estimation of tissues using DT-CWT feature extraction methods

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• 作者：Dogu Baran Aydogan ; Markus Hannula ; Tuukka Arola ; Prasun Dastidar ; Jari Hyttinen
• 关键词：Machine learning ; Image DB ; Texture analysis ; 3D orientation estimation
• 刊名：Data and Knowledge Engineering
• 出版年：2009
• 出版时间：December 2009
• 年：2009
• 卷：68
• 期：12
• 页码：1383-1397
• 全文大小：2708 K

文摘

In this study, four different 2D dual-tree complex wavelet (DT-CWT) based texture feature extraction methods are developed and their applications are demonstrated in segmenting and classifying tissues. Two of the methods use rotation variant texture features and the other two use rotation invariant features. This paper also proposes a novel approach to estimate 3D orientations of tissues based on rotation variant DT-CWT features. The method updates the strongest structural anisotropy direction with an iterative approach and converges to a volume orientation in few steps. Although classification and segmentation results show that there is no significant difference in the performance between rotation variant and invariant features; the latter are more robust to changes in texture rotation, which is essential for classification and segmentation of objects from 3D datasets such as medical tomography images.