Background: The knowledge of subcellular location of proteins is essential to the comprehension
of numerous protein functions.
Objective: Accurate as well as computationally efficient and reliable automated analysis of protein
localization imagery greatly depend on the calculation of features from these images.
Methods: In the current work, a novel method termed as MD-LBP is proposed for feature extraction
from fluorescence microscopy protein images. For a given neighborhood, the value of central
pixel is computed as the difference of global and local means of the input image that is further
used as threshold to generate a binary pattern for that neighborhood.
Results: The performance of our method is assessed for 2D HeLa dataset using 5-fold crossvalidation
protocol. The performance of MD-LBP method with RBF-SVM as base classifier, is
superior to that of standard LBP algorithm, Threshold Adjacency Statistics, and Haralick texture
Conclusion: Development of specialized systems for different kinds of medical imagery will certainly
pave the path for effective drug discovery in pharmaceutical industry. Furthermore, biological
and bioinformatics based procedures can be simplified to facilitate pharmaceutical industry for