Automated identification of mitochondrial regions in complex intracellular space by texture analysis

Automated processing and quantification of biological images have been rapidly increasing the attention of researchers in image processing and pattern recognition because the roles of computerized image and pattern analyses are critical for new biological findings and drug discovery based on modern high-throughput and highcontent image screening. This paper presents a study of the automated detection of regions of mitochondria, which are a subcellular structure of eukaryotic cells, in microscopy images. The automated identification of mitochondria in intracellular space that is captured by the state-of-the-art combination of focused ion beam and scanning electron microscope imaging reported here is the first of its type. Existing methods and a proposed algorithm for texture analysis were tested with the real intracellular images. The high correction rate of detecting the locations of the mitochondria in a complex environment suggests the effectiveness of the proposed study.