COMPLETED LBP BASED TEXTURE ANALYSIS IN MAMMOGRAM

Breast cancer is a frequent cancer diseases and it is the leading cause of cancer death among women in most of the occidental countries. Mammography is one among the key tool to identify the location and size of tumor in the breast. Texture analysis plays an important role in detecting the disease patterns in mammogram and to identify the masses as normal or abnormal. The local binary pattern descriptor provides an illumination invariant and rotation invariant approach for the texture analysis. However the LBP consider only the sign parameters. So it may lose some textural information. This can be overcome by considering the sign, magnitude and centre gray level values. Here a new approach for the Texture analysis in mammogram using completed LBP is presented. Although different methods have been proposed most of them suffer from large number of false positives. In contrast this method uses textural properties to reduce the number of false positives

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We have recently introduced a new tool for texture analysis called feature based interaction map (FBIM). The FBIM approach can be efficiently used to assess fundamental structural properties of textures such as anisotropy, symmetry, orientation and regularity [4]. It has been demonstrated [5] that the FBIM is suitable for rotation-invariant texture classification of patterns with regular, weak regular, or linear structure. In this paper, we show how the interaction map can be applied as a structural filter for segmentation, detection of textured objects and texture defects, analysis of oriented structures and shape-from-texture. The power of the FBIM filter is in its unique capability to grasp the structure of pixel interactions typical for a given texture pattern. To efficiently use this capability, we propose a fast running implementation of the FBIM algorithm and present pilot experimental results demonstrating the potential of the FBIM approach in diverse tasks and applications