scholarly journals Learnable Empirical Mode Decomposition based on Mathematical Morphology

2022 ◽  
Vol 15 (1) ◽  
pp. 23-44
Author(s):  
Santiago Velasco-Forero ◽  
R. Pagès ◽  
Jesus Angulo
Keyword(s):  
Empirical Mode Decomposition ◽  
Mathematical Morphology ◽  
Mode Decomposition

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

2018 ◽  
Vol 8 (4) ◽  
Author(s):  
M. Jayanthi Rao ◽  
Dr. R. Kiran Kumar
Keyword(s):  
Empirical Mode Decomposition ◽  
Mathematical Morphology ◽  
Human Body ◽  
Polycystic Ovary ◽  
Human Reproduction ◽  
Diagnostic Process ◽  
Ultrasound Images ◽  
Body Parts ◽  
Mode Decomposition ◽  
Bidimensional 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 Bidimensional 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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