Abstract

Aiming to solve the problem of low efficiency in manually recognizing the red and white cells in stool microscopic images, we propose an automatic segmentation method based on iterative corrosion with marker-controlled watershed segmentation and an automatic recognition method based on support vector machine (SVM) classification. The method first obtains saliency map of the images in HSI and Lab color spaces through saliency detection algorithm, then fuses the salient images to complete the initial segmentation. Next, we segment the red and white cells completely based on the initial segmentation images using marker-controlled watershed algorithm and other complementary methods. According to the differences in geometrical and texture features of red and white cells such as area, perimeter, circularity, energy, entropy, correlation and contrast, we extract them as feature vectors to train SVM and finally complete the classification and recognition of red and white cells. The experimental results indicate that our proposed marker-controlled watershed method can help increase the segmentation and recognition accuracy. Moreover, since it is also less susceptible to the heteromorphic red and white cells, our method is effective and robust.

Details

Title
Automatic segmentation and recognition of red and white cells in stool microscopic images of human
Author
Kong, Guanghui; Wang, Zhiyong; Wan, Xiuchao; Xue, Fengjun
Section
Medical Biology and Medical Signal Processing
Publication year
2020
Publication date
2020
Publisher
EDP Sciences
ISSN
25550403
e-ISSN
22671242
Source type
Conference Paper
Language of publication
English
ProQuest document ID
3179184712
Copyright
© 2020. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License.