Automatic panorama with auto-focusing based on image fusion for microscopic imaging system


SIGNAL IMAGE AND VIDEO PROCESSING, vol.8, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 8
  • Publication Date: 2014
  • Doi Number: 10.1007/s11760-014-0717-5
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: Auto-focus function, Auto-focusing, Microscope auto-screening, Microscope motorized system, Multi-focus color image fusion, Image stitching
  • Karadeniz Technical University Affiliated: Yes


It is usually desirable from a microscope imaging system to have an efficient auto-focusing and to maintain imaging quality throughout microscopy screening restricted automatically by the specimen borders. This paper presents a novel image fusion-based auto-focusing method and an automatic panorama confined with surroundings of the specimen so as to minimize the auto-scanning time for microscope imaging system. Multi-focus color image fusion is proposed to achieve the auto-focusing task for microscopic imaging. An image sequence is captured by using a microscope eyepiece camera with moving the microscope stage along Z-axis. Several images around a reference image are used to achieve in-focus image, instead of selecting a single image from the sequence. The reference image is an image given highest focus measurement value within the image sequence. Moreover, various evaluation criteria are utilized to analyze the performance of the proposed auto-focus approach on different color models for microscopic imaging. Microscope stage position along the Z-axis is automatically adjusted by image processing-based feedback system to maintain focus during scanning process. In this screening, the in-focus images with overlapped areas on the X-Y axes are stitched together to produce a mosaic image without any seams. In this process, the screening area is automatically constrained with the specimen regions which occupy 20-40% of the glass surface. An artificial neural network-based learning algorithm is implemented to decide whether the specimen regions are within microscope objective field of view or not. The experimental studies of the proposed method were achieved on an image data set collected from the bright-field microscopy screening for Mycobacterium tuberculosis in specimen of Ziehl-Neelsen-stained sputum smears.