Follicle Detection in Digital Ultrasound Images using Bi-Dimensional Empirical Mode Decomposition and Mathematical Morphology

  • M.Jayanthi Rao
  • Dr. R.Kiran Kumar
Keywords: Ovarian Classification, Image Processing, Histogram Equalization, Bi-dimensional Empirical Mode Decomposition


Ultrasound Imaging is one of the technique used to study inside human body with images generated using high frequency sounds waves. The applications of ultrasound images include examination of human body parts such as Kidney, Liver, Heart and Ovaries.  This paper mainly concentrates on ultrasound images of ovaries. The detection of follicles in ultrasound images of ovaries is concerned with the follicle monitoring during the diagnostic process of infertility treatment of patients.Monitoring of follicle is important in human reproduction. This paper presents a method for follicle detection in ultrasound images using Bi-dimensional Empirical Mode Decomposition and Mathematical morphology. The proposed algorithm is tested on sample ultrasound images of ovaries for identification of follicles and classifies the ovary into three categories, normal ovary, cystic ovary and polycystic ovary. The experiment results are compared qualitatively with inferences drawn by medical expert manually and this data can be used to classify the ovary images.


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How to Cite
M.Jayanthi Rao, & Dr. R.Kiran Kumar. (2018). Follicle Detection in Digital Ultrasound Images using Bi-Dimensional Empirical Mode Decomposition and Mathematical Morphology. International Journal of Engineering and Management Research, 8(4), 163-167.