title: Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding.
abstract: The paper proposes a robust approach to automatic segmentation of leukocyte's nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert hematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises, respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm.
authors.affiliation: Department of Electronics &Telecommunication Engineering, Jadavpur University, Kolkata, India.
authors.forename: Arindam
authors.initials: A
authors.lastname: Jati
authors.affiliation: Department of Electronics &Telecommunication Engineering, Jadavpur University, Kolkata, India.
authors.forename: Garima
authors.initials: G
authors.lastname: Singh
authors.affiliation: School of Medical Science & Technology, IIT Kharagpur, West Bengal, India.
authors.forename: Rashmi
authors.initials: R
authors.lastname: Mukherjee
authors.affiliation: School of Medical Science & Technology, IIT Kharagpur, West Bengal, India.
authors.forename: Madhumala
authors.initials: M
authors.lastname: Ghosh
authors.affiliation: Department of Electronics &Telecommunication Engineering, Jadavpur University, Kolkata, India.
authors.forename: Amit
authors.initials: A
authors.lastname: Konar
authors.affiliation: School of Medical Science & Technology, IIT Kharagpur, West Bengal, India. Electronic address: chandan_chakraborty@yahoo.com.
authors.forename: Chandan
authors.initials: C
authors.lastname: Chakraborty
authors.affiliation: Department of Math and Computer Science, Liverpool Hope University, Liverpool, UK.
authors.forename: Atulya K
authors.initials: AK
authors.lastname: Nagar
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D057205:Automation, Laboratory / Q000379:methods*; D002467:Cell Nucleus / Q000145:classification*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007962:Leukocytes / Q000145:classification* / Q000166:cytology*; D008853:Microscopy / Q000379:methods*; D055815:Young Adult
keywords: Intuitionistic fuzzy divergence (IFD); Intuitionistic fuzzy generator (IFG); Intuitionistic fuzzy set (IFS); Leukocyte nucleus segmentation; Membership function; Non-membership function