A local approach based on a Local Binary Patterns variant texture descriptor for classifying pain states
This paper focuses on the use of image-based techniques for classifying pain states, in particular we compare several texture descriptors based on Local Binary Patterns (LBP), and we proposed some novel solutions based on the combination of new texture descriptors: the Elongated Ternary Pattern (ELTP) and the Elongated Binary Pattern (ELBP). ELTP is the best performing descriptor in our experiments. The ELBP descriptor combines characteristics of the Local Ternary Pattern (LTP) and ELTP. These two variants of the standard LBP are obtained by considering different shapes for the neighborhood calculation and different encodings for the evaluation of the local gray-scale difference. The resulting extracted features are used to train a support vector machine classifier. Extensive experiments are conducted using the Infant COPE database of neonatal facial images. Our results show that a local approach based on the ELTP feature extractor produces a reliable system for classifying pain states.
Information Technology and Cybersecurity
image medical analysis, texture descriptors, local binary patterns, neonatal pain detection, support vector machine
Nanni, Loris, Sheryl Brahnam, and Alessandra Lumini. "A local approach based on a local binary patterns variant texture descriptor for classifying pain states." Expert Systems with Applications 37, no. 12 (2010): 7888-7894.
Expert Systems with Applications