Feature extraction from three-dimensional images in quantitative microscopy |
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| Affiliation: | 1. British Columbia Cancer Agency, 601 West 10th Avenue, Vancouver, B.C., Canada V5Z 1L3;2. Department of Electrical Engineering, University of British Columbia, 2075 Wesbrook Mall, Vancouver, B.C., Canada V6T 1W5;1. Department of Information Technology, College of Engineering, Trikaripur, Kerala, India;2. Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India;3. Department of Computer Applications, Cochin University of Science and Technology, Kochi, India;1. School of Veterinary and Life Sciences, Vector- and Water-Borne Pathogen Research Group, Murdoch University, Murdoch, Western Australia 6150, Australia;2. Department of Medical Laboratory Sciences, Faculty of Allied Health Sciences, The Hashemite University PO Box 150459, Zarqa 13115, Jordan;3. College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China;1. State Key Laboratory of Plateau Ecology and Agriculture, Center for Biomedicine and Infectious Diseases, Academy of Animal Science and Veterinary Medicine, Qinghai University, Xining, Qinghai 810016, PR China;2. National Research Center for Protozoan Diseases, Obihiro University of Agriculture and Veterinary Medicine, Obihiro, Hokkaido 080-8555, Japan;3. Civil, Mining, & Environmental Engineering, University of Wollongong, Wollongong, NSW 2522, Australia;1. Data Mining Laboratory, Department of Engineering, College of Farabi, University of Tehran, Tehran, Iran;2. Department of Quantitative Health Sciences, Cleveland Cancer Foundation, Cleveland, OH, United States |
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| Abstract: | Different methods are investigated in selecting and generating the appropriate microscope images for analysis of three-dimensional objects in quantitative microscopy. Traditionally, the ‘best’ focused image from a set is used for quantitative analysis. Such an objectively determined image is optimal for the extraction of some features, but may not be the best image for the extraction of all features. Various methods using multiple images are here developed to obtain a tighter distribution for all features.Three different approaches for analysis of images of stained cervical cells were analyzed. In the first approach, features are extracted from each image in the set. The feature values are then averaged to give the final result. In the second approach, a set of varying focused images are reconstructed to obtain a set of in-focus images. Features are then extracted from this set and averaged. In the third approach, a set of images in the three-dimensional scene is compressed into a single two-dimensional image. Four different compression methods are used. Features are then extracted from the resulting two-dimensional image. The third approach is employed on both the raw and transformed images.Each approach has its advantages and disadvantages. The first approach is fast and produces reasonable results. The second approach is more computationally expensive but produces the best results. The last approach overcomes the memory storage problem of the first two approaches since the set of images is compressed into one. The method of compression using the highest gradient pixel produces better results overall than other data reduction techniques and produces results comparable to the first approach. |
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