The use of anatomical multiatlas methods has proven to be one of the most competitive techniques for brain images segmentation. The majority of these methods are based on visual criteria of similarity between groups of an atlas to select a representative patient image to be segmented. However, this criterion is not necessarily linked to the performance of the segmentation. To overcome this dilemma, we present in this article, a new concept of preselection of an anatomical atlas group, which is based on machine learning and using an adapted descriptor that can give an efficient and more precise segmentation of the patient image. The new descriptor, local texture statistical properties for matching descriptor with only affine registration, is adapted from the local texture of matching (LTEMA) descriptor. The proposed method is tested on real MRI brain images (LONI database provided by USC Neurological Imaging Laboratory), and show the capability and the effectiveness of the proposed local descriptor, it has been compared to three local descriptors: scale-invariant feature transform, speed up robust feature, and LTEMA, as well as the comparison with the registration method. The obtained results show a significant improvement that makes this descriptor recommended for segmentation techniques.

中文翻译：

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使用局部纹理统计特性的解剖多图谱分割，用于匹配描述符与机器学习

解剖多图谱方法的使用已被证明是脑图像分割中最具竞争力的技术之一。这些方法中的大多数基于图谱组之间相似性的视觉标准来选择要分割的代表性患者图像。然而，这个标准不一定与分割的性能相关。为了克服这一困境，我们在本文中提出了一种新的解剖图谱组预选概念，该概念基于机器学习并使用自适应描述符，可以对患者图像进行有效和更精确的分割。新的描述符，用于仅具有仿射配准的匹配描述符的局部纹理统计特性，改编自局部纹理匹配（LTEMA）描述符。所提出的方法在真实的 MRI 脑图像（由 USC Neurological Imaging Laboratory 提供的 LONI 数据库）上进行了测试，并展示了所提出的局部描述符的能力和有效性，并与三个局部描述符进行了比较：尺度不变特征变换、速度up 健壮特征，和 LTEMA，以及与配准方法的比较。获得的结果显示了显着的改进，使得该描述符推荐用于分割技术。