Quantitative Comparison of Two Particle Tracking Methods in Fluorescence Microscopy Images

被引:0
|
作者
Mabaso, Matsilele [1 ]
Twala, Bhekisipho [2 ]
Withey, Daniel [1 ]
机构
[1] CSIR, MDS MIAS, ZA-0001 Pretoria, South Africa
[2] Univ Johannesburg, Dept Elect Engn, Johannesburg, South Africa
关键词
D O I
10.1109/BRICS-CCI-CBIC.2013.106
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Tracking of multiple bright particles (spots) in fluorescence microscopy image sequences is seen as a crucial step in understanding complex information in the cell. However, fluorescence microscopy generates high a volume of noisy image data that cannot be analysed efficiently by means of manual analysis. In this study we compare the performance of two computer-based tracking methods for tracking of bright particles in fluorescence microscopy image sequences. The methods under comparison are, Interacting Multiple Model filter and Feature Point Tracking. The performance of the methods is validated using synthetic but realistic image sequences and real images. The results from experiments show that the Interacting Multiple Model filter performed best, under the test conditions.
引用
收藏
页码:604 / 608
页数:5
相关论文
共 50 条
  • [1] A Motion Transformer for Single Particle Tracking in Fluorescence Microscopy Images
    Zhang, Yudong
    Yang, Ge
    MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2023, PT VIII, 2023, 14227 : 503 - 513
  • [2] DEEP LEARNING FOR PARTICLE DETECTION AND TRACKING IN FLUORESCENCE MICROSCOPY IMAGES
    Ritter, C.
    Spilger, R.
    Lee, J-Y
    Bartenschlager, R.
    Rohr, K.
    2021 IEEE 18TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), 2021, : 873 - 876
  • [3] DEEP LEARNING PARTICLE DETECTION FOR PROBABILISTIC TRACKING IN FLUORESCENCE MICROSCOPY IMAGES
    Ritter, C.
    Wollmann, T.
    Lee, J-Y
    Bartenschlager, R.
    Rohr, K.
    2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020), 2020, : 977 - 980
  • [4] Quantitative Comparison of Spot Detection Methods in Fluorescence Microscopy
    Smal, Ihor
    Loog, Marco
    Niessen, Wiro
    Meijering, Erik
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 2010, 29 (02) : 282 - 301
  • [5] Quantitative comparison of multiframe data association techniques for particle tracking in time-lapse fluorescence microscopy
    Smal, Ihor
    Meijering, Erik
    MEDICAL IMAGE ANALYSIS, 2015, 24 (01) : 163 - 189
  • [6] DEEP LEARNING METHOD FOR PROBABILISTIC PARTICLE DETECTION AND TRACKING IN FLUORESCENCE MICROSCOPY IMAGES
    Spilger, Roman
    Lee, Ji Young
    Minh Tu Pham
    Bartenschlager, Ralf
    Rohr, Karl
    2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI, 2023,
  • [7] Advanced particle filtering for multiple object tracking in dynamic fluorescence microscopy images
    Smal, Ihor
    Niessen, Wiro
    Meijering, Erik
    2007 4TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING : MACRO TO NANO, VOLS 1-3, 2007, : 1048 - 1051
  • [8] COLOCALIZATION ANALYSIS AND PARTICLE TRACKING IN MULTI-CHANNEL FLUORESCENCE MICROSCOPY IMAGES
    Qiang, Yu
    Lee, Ji Young
    Bartenschlager, Ralf
    Rohr, Karl
    2017 IEEE 14TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2017), 2017, : 646 - 649
  • [9] Multiple dense particle tracking in fluorescence microscopy images based on multidimensional assignment
    Feng, Linqing
    Xu, Yingke
    Yang, Yi
    Zheng, Xiaoxiang
    JOURNAL OF STRUCTURAL BIOLOGY, 2011, 173 (02) : 219 - 228
  • [10] Minimal Path based Particle Tracking in Low SNR Fluorescence Microscopy Images
    Lu, Sheng
    Chen, Tong
    Yang, Fan
    Peng, Chenglei
    Du, Sidan
    Li, Yang
    PROCEEDINGS OF 2019 4TH INTERNATIONAL CONFERENCE ON BIOMEDICAL SIGNAL AND IMAGE PROCESSING (ICBIP 2019), 2019, : 93 - 97