کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
443066 692526 2013 20 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر گرافیک کامپیوتری و طراحی به کمک کامپیوتر
پیش نمایش صفحه اول مقاله
Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features
چکیده انگلیسی

Phase-contrast microscopy is one of the most common and convenient imaging modalities to observe long-term multi-cellular processes, which generates images by the interference of lights passing through transparent specimens and background medium with different retarded phases. Despite many years of study, computer-aided phase contrast microscopy analysis on cell behavior is challenged by image qualities and artifacts caused by phase contrast optics. Addressing the unsolved challenges, the authors propose (1) a phase contrast microscopy image restoration method that produces phase retardation features, which are intrinsic features of phase contrast microscopy, and (2) a semi-supervised learning based algorithm for cell segmentation, which is a fundamental task for various cell behavior analysis. Specifically, the image formation process of phase contrast microscopy images is first computationally modeled with a dictionary of diffraction patterns; as a result, each pixel of a phase contrast microscopy image is represented by a linear combination of the bases, which we call phase retardation features. Images are then partitioned into phase-homogeneous atoms by clustering neighboring pixels with similar phase retardation features. Consequently, cell segmentation is performed via a semi-supervised classification technique over the phase-homogeneous atoms. Experiments demonstrate that the proposed approach produces quality segmentation of individual cells and outperforms previous approaches.

Figure optionsDownload high-quality image (267 K)Download as PowerPoint slideHighlights
• Construct a dictionary based on diffraction patterns of phase contrast images.
• Develop a sparse representation approach to restore phase retardation features.
• Partition phase contrast microscopy images into phase-homogeneous atoms.
• Segment cells via semi-supervised classification over the phase-homogeneous atoms.
• Provide useful information for cell classification and potential applications.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Medical Image Analysis - Volume 17, Issue 7, October 2013, Pages 746–765
نویسندگان
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