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Proceedings Paper

PCA-HOG symmetrical feature based diseased cell detection
Author(s): Min-jie Wan
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Paper Abstract

A histogram of oriented gradient (HOG) feature is applied to the field of diseased cell detection, which can detect diseased cells in high resolution tissue images rapidly, accurately and efficiently. Firstly, motivated by symmetrical cellular forms, a new HOG symmetrical feature based on the traditional HOG feature is proposed to meet the condition of cell detection. Secondly, considering the high feature dimension of traditional HOG feature leads to plenty of memory resources and long runtime in practical applications, a classical dimension reduction method called principal component analysis (PCA) is used to reduce the dimension of high-dimensional HOG descriptor. Because of that, computational speed is increased greatly, and the accuracy of detection can be controlled in a proper range at the same time. Thirdly, support vector machine (SVM) classifier is trained with PCA-HOG symmetrical features proposed above. At last, practical tissue images is detected and analyzed by SVM classifier. In order to verify the effectiveness of this new algorithm, it is practically applied to conduct diseased cell detection which takes 200 pieces of H&E (hematoxylin & eosin) high resolution staining histopathological images collected from 20 breast cancer patients as a sample. The experiment shows that the average processing rate can be 25 frames per second and the detection accuracy can be 92.1%.

Paper Details

Date Published: 6 April 2016
PDF: 14 pages
Proc. SPIE 9711, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues IX, 97110K (6 April 2016); doi: 10.1117/12.2209262
Show Author Affiliations
Min-jie Wan, Nanjing Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 9711:
Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues IX
Daniel L. Farkas; Dan V. Nicolau; Robert C. Leif, Editor(s)

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