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. 2016 Mar-Apr;13(2):326-40.
doi: 10.1109/TCBB.2015.2459685.

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

Madhu S Sigdel, Madhav Sigdel, Semih Dinç, Imren Dinç, Marc L Pusey, Ramazan S Aygün

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

Madhu S Sigdel et al. IEEE/ACM Trans Comput Biol Bioinform. 2016 Mar-Apr.

Abstract

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.

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Figures

Fig. 1
Fig. 1
Images of a protein crystallization sample captured with different depths of focus
Fig. 2
Fig. 2
Selecting lowest intensity pixels
Fig. 4
Fig. 4
Inclusion of pixels as threshold is reduced for a checker-box image (a)-(c) Original Harris, (d)-(f) Trace, and (g)-(i) FocusALL
Fig. 3
Fig. 3
Variation of contours with eigenvalues
Fig. 5
Fig. 5
Applying basic FocusALL
Fig. 6
Fig. 6
Applying FocusALL to high resolution image
Fig. 7
Fig. 7
Applying FocusALL-HR on a high resolution image. (a) Focused image at base resolution, (b) Depth color image at base resolution, (c) Enlarged depth color image, and (d) Focused image at high resolution
Fig. 8
Fig. 8
Applying basic FocusALL on PC3
Fig. 9
Fig. 9
Generating focused image for varying illumination
Fig. 10
Fig. 10
Identify objects, holes and background in PHI
Fig. 11
Fig. 11
Results of FocusALL-VI
Fig. 12
Fig. 12
Simulation of different focal depth on a texture image
Fig. 13
Fig. 13
Experimental dataset (images captured with different depths of field a)Protein images 1 (PC1), b) Protein images 2 (PC2), c) Protein images 3 (PC3), d) Retinal pigment epithelial (RPE) images, and (e) Simulated texture images
Fig. 15
Fig. 15
Comparison of region R1 in focused images on PC1 (a) EDF-RW, (b)EDF-CWT, and (c) FocusALL
Fig. 14
Fig. 14
Focusing results using different techniques (a) Protein crystallization images 1 (PC1), (b) Protein crystallization images 2 (PC2), (c) Retinal pigment epithelial (RPE) images, and (d) Simulated texture images
Fig. 16
Fig. 16
Comparison of focusing results on high resolution (a)-(d) Results on region R1 of PC1 dataset, and (e)-(f) Results on region R1 of PC2 dataset
Fig. 17
Fig. 17
Varying illumination results on PC3 (Fig. 13c), (a)-(g) Results on low resolution (320×240), and (h)-(k) Region R2 in high resolution (1280×960)
See this image and copyright information in PMC

References

    1. Sigdel M, Pusey ML, Aygün RS. Real-time protein crystallization image acquisition and classification system. Crystal growth & design. 2013;13(7):2728–2736. - PMC - PubMed
    1. Sigdel MS, Sigdel M, Dinç S, Dinç I, Pusey ML, Aygün RS. IEEE southEastConf. IEEE; 2014. Autofocusing of microscopic images using harris corner response measure. pp. 1–6. - PMC - PubMed
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