Classification of Subcellular Location Patterns in Fluorescence Microscope Images Based on Modified Threshold Adjacency Statistics

The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. As proteins are integral components of cell function, it is critical to understand their properties such as structure and localization. The study of protein subcellular localization (PSL) is important for elucidating protein functions involved in various cellular processes. The subcellular location of proteins is most often determined by visual interpretation of fluorescence microscope images, but in recent years, to perform highresolution, high-throughput analysis of ten thousands of expressed proteins for the many cell types and cellular conditions under which they may be found creates, automated methods that are needed. In this review, we use a novel method that determines an improved features set, that distinguish subcellular patterns with high accuracy and high speed. This method based on modified threshold adjacency statistics (MTAS), the essence which is to threshold the images. Previous work that uses threshold adjacency statistics (TAS), introduces a simple set of Subcellular Location Features (SLF) which are computed by counting the number of threshold pixels adjacent.

Keywords:

subcellular location features , pattern , threshold adjacency statistics , SVM