Author/Authors :
Du, Xiaohui University of Electronic Science and Technology of China - MOEMIL Laboratory - School of Optoelectronic Information - North Jianshe Road - Chengdu, China , Liu, Lin University of Electronic Science and Technology of China - MOEMIL Laboratory - School of Optoelectronic Information - North Jianshe Road - Chengdu, China , Wang, Xiangzhou University of Electronic Science and Technology of China - MOEMIL Laboratory - School of Optoelectronic Information - North Jianshe Road - Chengdu, China , Zhang, Jing University of Electronic Science and Technology of China - MOEMIL Laboratory - School of Optoelectronic Information - North Jianshe Road - Chengdu, China , Ni, Guangming University of Electronic Science and Technology of China - MOEMIL Laboratory - School of Optoelectronic Information - North Jianshe Road - Chengdu, China , Hao, Ruqian University of Electronic Science and Technology of China - MOEMIL Laboratory - School of Optoelectronic Information - North Jianshe Road - Chengdu, China , Liu, Juanxiu University of Electronic Science and Technology of China - MOEMIL Laboratory - School of Optoelectronic Information - North Jianshe Road - Chengdu, China , Liu, Yong University of Electronic Science and Technology of China - MOEMIL Laboratory - School of Optoelectronic Information - North Jianshe Road - Chengdu, China
Abstract :
Trichomonas examination is one of the important items in the leucorrhea routine detection. and it cannot be recognized by still
images because of the unstable morphology and unfixed focal location caused by motion characteristic. We proposed an improved
VIBE algorithm. 6 videos (totally 1414 frames) are collected for testing. In order to compare the effects of the algorithms, we
segment each frame artificially as ground truth. Experiments show that percentage of correct classification (PCC) achieves 88%.
The proposed improved method can effectively suppress the false detection caused by the formed components such as epithelial
cells in the leucorrhea microscopic image and the missed detection caused by the background model update during the
movement. At the same time, improvements can effectively suppress smear and ghost areas. The algorithm proposed in this paper
can be integrated into the leucorrhea automatic detection system.
Keywords :
Trichomonas , VIBE , PCC , Detection
