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Automated nuclei segmentation of malignant using level sets.

Ahmed Husham1, Mohammed Hazim Alkawaz2,3, Tanzila Saba4

  • 1Faculty of Computing, Universiti Teknologi, Johor Bahru, Malaysia.

Microscopy Research and Technique
|August 2, 2016
PubMed
Summary

This study introduces a novel method for accurate nuclei segmentation in breast cancer images. The approach enhances image quality and employs level set segmentation for precise detection of malignant regions.

Keywords:
histopathologylevel setsmalignant detectionnucleisegmentation

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Biomedical Image Analysis

Background:

  • Accurate segmentation of nuclei in noisy, complex images remains a significant challenge in digital pathology.
  • Identifying malignant nuclei is crucial for cancer diagnosis and treatment planning.

Purpose of the Study:

  • To propose a novel, high-performance method for detecting and segmenting nuclei in H&E stained breast cancer images.
  • To accurately determine if segmented nuclei are malignant.

Main Methods:

  • The proposed method involves three stages: preprocessing (noise removal, image enhancement), seed detection using centroid transform, and segmentation using the level set (LS) method.
  • Candidate detection is employed on the centroid transform to evaluate the centroid of each object.
  • The level set method is applied for precise nuclei segmentation.

Main Results:

  • The method was evaluated on 58 H&E breast cancer images from the UCSB Bio-Segmentation Benchmark dataset.
  • The proposed approach demonstrated high performance and accuracy compared to existing techniques.
  • Experimental results showed strong agreement with ground truth images.

Conclusions:

  • The developed method offers a robust and accurate solution for nuclei segmentation in breast cancer histopathology.
  • This technique has the potential to improve the accuracy of computer-aided diagnosis systems for breast cancer.