Abstract
In this work, segmentation of cellular structures in the high resolutional histopathological images and possibility of the discrimination within normal and mitotic cells has been investigated. Mitosis detection is very exhaustive and time consuming process. In the first step, features of cells which have been found by the clustering algorithm have been extracted by oriented gradient histograms (HOG) method which is known as a robust texture descriptor. A mitotic cell has some textural changes that makes it recognizable among other normal cells. Hence, the classification accuracy of the unsupervised learning methods is increased after making use of proposed textural descriptor.
Keywords
Histogram
Artificial intelligence
Cluster analysis
Pattern recognition (psychology)
Mitosis
Segmentation
Computer science
Texture (cosmology)
Image segmentation
Computer vision
Process (computing)
Histogram of oriented gradients
Image (mathematics)
Biology
Cell biology
Subject Areas
AI in cancer detection
·Artificial Intelligence ·Physical Sciences
Digital Imaging for Blood Diseases
·Computer Vision and Pattern Recognition ·Physical Sciences
Image Retrieval and Classification Techniques
·Computer Vision and Pattern Recognition ·Physical Sciences
Citations by Year
2014: 1
2018: 1
2019: 1
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