Quantitative Quality Assessment of Microscopic Image Mosaicing
The mosaicing technique has been employed in more and more application fields, from entertainment to scientific ones. In the latter case, often the final evaluation is still left to human beings, that assess visually the quality of the mosaic. Many times, a lack of objective measurements in microscopic mosaicing may prevent the mosaic from being used as a starting image for further analysis. In this work we analyze three different metrics and indexes, in the domain of signal analysis, image analysis and visual quality, to measure the quality of different aspects of the mosaicing procedure, such as registration errors and visual quality. As the case study we consider the mosaicing algorithm we developed. The experiments have been carried out by considering mosaics with very different features: histological samples, that are made of detailed and contrasted images, and live stem cells, that show a very low contrast and low detail levels.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328188Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1870
 C. Sun, R. Beare, V. Hilsenstein, and P. Jackway, "Mosaicing of microscope images with global geometric and radiometric corrections," Journal of Microscopy, vol. 224, no. 2, pp. 158-165, Nov. 2006.
 D. Gareau, Y. Patel, Y. Li, I. Aranda, A. Halpern, K. Nehal, and M. Rajadhyaksha, "Confocal mosaicing microscopy in skin excisions: a demonstration of rapid surgical pathology," Journal of Microscopy, vol. 233, no. 1, pp. 149-159, Jan. 2009.
 E. Zagrouba, W. Barhoumi, and S. Amri, "An efficient image-mosaicing method based on multifeature matching," Machine Vision and Applications, vol. 20, pp. 139-162, 2009.
 D. Steckhan, T. Bergen, T. Wittenberg, and S. Rupp, "Efficient large scale image stitching for virtual microscopy," in 30th Annual International IEEE EMBS Conference Vancouver, British Columbia, Canada, August 20-24, Aug. 2008, pp. 4019-4023.
 A. Bevilacqua, A. Gherardi, and M. Ferri, "Predicting biological age from a skin surface capacitive analysis," Int. J. Mod. Phys. C, vol. 15, no. 9, pp. 1309-1320, 2004.
 B. D. Lucas and T. Kanade, "An iterative image registration technique with an application to stereo vision," in International Joint Conference on Artificial Intelligence, 1981, pp. 674-679.
 J. Y. Bouguet, "Pyramidal implementation of the Lukas Kanade feature tracker: Description of the algorithm," In Intel Research Laboratory, Technical Report, pp. 1-9, 1999.
 H. Foroosh, J. B. Zerubia, and M. Berthod, "Extension of phase correlation to subpixel registration," IEEE Transactions on Image Processing, vol. 14, no. 1, pp. 12-22, 2002.
 M. A. Fischler and R. C. Bolles, "Random sample and consensus: A paradigm for model fitting with application to image analysis and automated cartography," Comm. of the ACM, vol. 24, no. 6, pp. 381- 395, 1981.
 A. Bevilacqua, A. Gherardi, L. Carozza, and F. Piccinini, "Semiautomatic background detection in microscopic images," in International Conference on Biological Science and Engineering (ICBSE), Venice, Italy, November 24-26, 2010.
 P. Azzari and A. Bevilacqua, "Joint spatial and tonal mosaic alignment for motion detection with ptz camera," Lecture Notes in Computer Science, vol. 4142, pp. 764-775, 2006.
 Z. Wang and A. C. Bovik, "A universal image quality index," IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81-84, Mar. 2002.