FocusALL: Focal Stacking of Microscopic Images Using Modified Harris Corner Response Measure

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 13; no. 2; pp. 326 - 340
• Main Authors: Sigdel, Madhu S., Sigdel, Madhav, Dinc, Semih, Dinc, Imren, Pusey, Marc L., Aygun, Ramazan S.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; auto-focusing; Cameras; Cellular; Cluster Analysis; Corners; Crystallization; Depth of field; Focal stacking; Harris corner response measure; High resolution; Humans; Image Processing, Computer-Assisted - methods; Linear programming; Microscopy; Microscopy - methods; Models, Biological; protein crystallization; Proteins; Proteins - chemistry; Retinal Pigment Epithelium - diagnostic imaging; Stacking; auto-focusing; Harris corner response measure; Focal stacking; protein crystallization
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2015.2459685

Automated image analysis of microscopic images such as protein crystallization images and cellular images is one of the important research areas. If objects in a scene appear at different depths with respect to the camera's focal point, objects outside the depth of field usually appear blurred. Therefore, scientists capture a collection of images with different depths of field. Focal stacking is a technique of creating a single focused image from a stack of images collected with different depths of field. In this paper, we introduce a novel focal stacking technique, FocusALL, which is based on our modified Harris Corner Response Measure. We also propose enhanced FocusALL for application on images collected under high resolution and varying illumination. FocusALL resolves problems related to the assumption that focus regions have high contrast and high intensity. Especially, FocusALL generates sharper boundaries around protein crystal regions and good in focus images for high resolution images in reasonable time. FocusALL outperforms other methods on protein crystallization images and performs comparably well on other datasets such as retinal epithelial images and simulated datasets.