Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms


Albayrak A. , Bilgin G.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, cilt.57, ss.653-665, 2019 (SCI İndekslerine Giren Dergi)

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  • Cilt numarası: 57
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1007/s11517-018-1906-0
  • Dergi Adı: MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
  • Sayfa Sayısı: ss.653-665

Özet

The analysis of cell characteristics from high-resolution digital histopathological images is the standard clinical practice for the diagnosis and prognosis of cancer. Yet, it is a rather exhausting process for pathologists to examine the cellular structures manually in this way. Automating this tedious and time-consuming process is an emerging topic of the histopathological image-processing studies in the literature. This paper presents a two-stage segmentation method to obtain cellular structures in high-dimensional histopathological images of renal cell carcinoma. First, the image is segmented to superpixels with simple linear iterative clustering (SLIC) method. Then, the obtained superpixels are clustered by the state-of-the-art clustering-based segmentation algorithms to find similar superpixels that compose the cell nuclei. Furthermore, the comparison of the global clustering-based segmentation methods and local region-based superpixel segmentation algorithms are also compared. The results show that the use of the superpixel segmentation algorithm as a pre-segmentation method improves the performance of the cell segmentation as compared to the simple single clustering-based segmentation algorithm. The true positive ratio (TPR), true negative ratio (TNR), F-measure, precision, and overlap ratio (OR) measures are utilized as segmentation performance evaluation. The computation times of the algorithms are also evaluated and presented in the study.