Statistical Texture Modeling for Medical Volume Using Linear Tensor Coding

Junping Deng,Xu Qiao,Yen-Wei Chen

doi:10.1155/2013/630902

Junping Deng, Xu Qiao + Show 1 more

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https://doi.org/10.1155/2013/630902

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Abstract

We introduced a compact representation method named Linear Tensor Coding (LTC) for medical volume. With LTC, medical volumes can be represented by a linear combination of bases which are mutually independent. Furthermore, it is possible to choose the distinctive basis for classification. Before classification, correlations between category labels and the coefficients of LTC basis are used to choose the basis. Then we use the selected basis for classification. The classification accuracy can be significantly improved by the use of selected distinctive basis.

Highlights

In the recent years, the research of digital atlases is a popular and important topic in the medical volume processing [1, 2]
Inspired by the work of active shape model (ASM), 3D ASM was proposed for construction of 3D statistical models for segmentation of the left ventricle of the heart [6]
In [8], 3D active appearance model is used for segmentation of cardiac MR and ultrasound images

Summary

Introduction

The research of digital atlases is a popular and important topic in the medical volume processing [1, 2]. Many problems in medical volumes interpretation involve the need of a modeling to understand the volumes with which it is presented, and well representation of medical volumes is very important part of computer-assisted diagnosis (CAD). The representation of medical volume can be mainly categorized as shape-based methods, in which a deformable model is represented or matched to, and appearance based methods, in which the model represents the volume region covered by the structures. The active appearance model (AAM) is proposed which can represent both shape and texture information. In [8], 3D active appearance model is used for segmentation of cardiac MR and ultrasound images. Radiologists are mainly depending on the intensity variations (texture information) in livers on medical images to identify modules or tumors and make a diagnostic decision. There has been little research on applications of digital atlas to CAD

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