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Chromosome image contrast enhancement using adaptive, iterative histogram matching

Ehsani, S. P ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. DOI: 10.1109/IranianMVIP.2011.6121581
  3. Publisher: 2011
  4. Abstract:
  5. Vivid banding patterns in medical images of chromosomes are a vital feature for karyotyping and chromosome classification. The chromosome image quality may be degraded by many phenomenon such as staining, sample defectness and imaging conditions. Thus, an image enhancement processing algorithm is needed before classification of chromosomes. In this paper, we propose an adaptive and iterative histogram matching (AIHM) algorithm for chromosome contrast enhancement especially in banding patterns. The reference histogram, with which the initial image needs to be matched, is created based on some processes on the initial image histogram. Usage of raw information in the histogram of initial image will result in more dependency to the input image and acquiring better contrast improvement. Moreover, the iteration procedure leads to a gradual contrast enhancement and getting the best result. The iteration steps may vary depending on the image characteristics and histogram. In order to assess the performance of the proposed algorithm in comparison with existing image enhancement techniques, Constant Gain Transform (CGT) and Local Standard Deviation Adaptive Contrast Enhancement (LSD-ACE), a quantitative measurement, the contrast improvement ratio (CIR), is utilized. The experimental results indicate that the proposed method shows the best results in terms of the CIR measure and, as well as in visual perception
  6. Keywords:
  7. Chromosome karyotyping ; Contrast improvement ratio ; Adaptive contrast enhancement ; Banding patterns ; Chromosome classification ; Chromosome image ; Constant gain ; Contrast Enhancement ; Enhancement techniques ; Histogram matching ; Image characteristics ; Image histograms ; Imaging conditions ; Improvement ratio ; Input image ; Iteration procedure ; Iteration step ; Local standard deviation ; Medical images ; Processing algorithms ; Quantitative measurement ; Visual perception ; Algorithms ; Chromosomes ; Computer vision ; Graphic methods ; Image enhancement ; Image quality ; Image segmentation ; Medical imaging ; Image matching
  8. Source: 2011 7th Iranian Conference on Machine Vision and Image Processing, MVIP 2011 - Proceedings, 16 November 2011 through 17 November 2011 ; 2011 ; 9781457715358 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6121581