Details

Autor(en) / Beteiligte

• Koyuncu, Can Fahrettin
• Cetin‐Atalay, Rengul
• Gunduz‐Demir, Cigdem

Titel

Object‐Oriented Segmentation of Cell Nuclei in Fluorescence Microscopy Images

Ist Teil von

• Cytometry. Part A, 2018-10, Vol.93 (10), p.1019-1028

Ort / Verlag

Hoboken, USA: John Wiley & Sons, Inc

Erscheinungsjahr

2018

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Cell nucleus segmentation remains an open and challenging problem especially to segment nuclei in cell clumps. Splitting a cell clump would be straightforward if the gradients of boundary pixels in‐between the nuclei were always higher than the others. However, imperfections may exist: inhomogeneities of pixel intensities in a nucleus may cause to define spurious boundaries whereas insufficient pixel intensity differences at the border of overlapping nuclei may cause to miss some true boundary pixels. In contrast, these imperfections are typically observed at the pixel‐level, causing local changes in pixel values without changing the semantics on a large scale. In response to these issues, this article introduces a new nucleus segmentation method that relies on using gradient information not at the pixel level but at the object level. To this end, it proposes to decompose an image into smaller homogeneous subregions, define edge‐objects at four different orientations to encode the gradient information at the object level, and devise a merging algorithm, in which the edge‐objects vote for subregion pairs along their orientations and the pairs are iteratively merged if they get sufficient votes from multiple orientations. Our experiments on fluorescence microscopy images reveal that this high‐level representation and the design of a merging algorithm using edge‐objects (gradients at the object level) improve the segmentation results. This study introduces a new cell segmentation method that relies on using gradient information not at the pixel‐level but at the object‐level. To this end, it proposes to decompose an image into smaller homogeneous subregions, define edge‐objects at four different orientations to encode the gradient information at the object‐level, and devise an effective algorithm that segments nuclei by merging the smaller subregions using the edge‐objects. In this merging algorithm, the edge‐objects vote for subregion pairs along the direction specified by their edge types and the subregion pairs are iteratively merged provided that they get sufficient votes from multiple directions.