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Automatic segmentation on cell image fusing gray and gradient information.

Boqiang Liu1, Cong Yin, Zhongguo Liu

  • 1School of Control Science and Engineering, Shandong University, Jinan 250061, China.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 16, 2007
PubMed
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An automated system accurately segments hematopoietic cells in blood and bone marrow smears using an advanced algorithm. This method enhances cell classification and diagnosis by improving nucleus and cytoplasm segmentation.

Area of Science:

  • Hematology
  • Medical Image Analysis
  • Computational Biology

Background:

  • Accurate classification of hematopoietic cells is crucial for diagnosing blood disorders.
  • Manual smear analysis is time-consuming and subjective.
  • Automated systems require precise cell segmentation for reliable results.

Purpose of the Study:

  • To develop an automated system for classifying and diagnosing hematopoietic cells.
  • To create an algorithm for accurate segmentation of nucleated hematopoietic cells (nucleus and cytoplasm) in stained smears.

Main Methods:

  • An automated segmentation algorithm fusing gray level, color information, and mathematical morphological gradient was developed.
  • Improved iterative threshold segmentation was used for nucleus segmentation.

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  • Color spaces and prior knowledge were utilized for cytoplasm segmentation.
  • Morphological gradient information was employed to prevent over-segmentation and mark cell components.

    • Main Results:

    • The algorithm achieved a 95.5% success rate for nucleus segmentation.
    • The cytoplasm segmentation success rate was 92.6%.
    • Edge detection using morphological gradient proved effective for contour detection.

    Conclusions:

    • The proposed method is valid and efficient for segmenting hematopoietic cells in color images.
    • This automated approach can aid in the classification and diagnosis of blood and bone marrow disorders.
    • The system demonstrates potential for improving the accuracy and efficiency of hematological analysis.