Edge detection using fast Bidimensional Empirical Mode Decomposition and mathematical morphology

2010 ◽  
Author(s):  
James Z. Zhang ◽  
Zijing Qin
Keyword(s):  
Edge Detection ◽  
Empirical Mode Decomposition ◽  
Mathematical Morphology ◽  
Mode Decomposition ◽  
Bidimensional Empirical Mode Decomposition

Application of Bidimensional Empirical Mode Decomposition to Medical Liquid Opacity Detection

2011 ◽  
Vol 128-129 ◽  
pp. 530-533
Author(s):  
Jian Wan ◽  
Yuan Peng Diao ◽  
Dong Mei Yan ◽  
Qiang Guo ◽  
Zhen Shen Qu
Keyword(s):  
Edge Detection ◽  
Empirical Mode Decomposition ◽  
External Environment ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Edge Detection Algorithm ◽  
Mode Decomposition ◽  
Simulation Results ◽  
Bidimensional Empirical Mode Decomposition

A Robert operator edge detection algorithm based on Bidimensional Empirical Mode Decomposition (BEMD) to detect medical liquid opacity is proposed. This method can effectively resolve the problem that traditional Robert operator edge detection can be easily effected by noise, and it also has certain effects on restraining external environment influence. The simulation results show that, compare with traditional medical liquid opacity detection methods, the proposed method could achieve higher detection accuracy, and has a certain theory and application value.

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.

A NOVEL APPROACH OF EDGE DETECTION VIA A FAST AND ADAPTIVE BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION METHOD

2010 ◽  
Vol 02 (02) ◽  
pp. 171-192 ◽  
Author(s):  
SHARIF M. A. BHUIYAN ◽  
JESMIN F. KHAN ◽  
REZA R. ADHAMI
Keyword(s):  
Edge Detection ◽  
Empirical Mode Decomposition ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Morphological Operations ◽  
Intrinsic Mode Functions ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Edge Detectors ◽  
Bidimensional Empirical Mode Decomposition

A novel approach of edge detection is proposed that utilizes a bidimensional empirical mode decomposition (BEMD) method as the primary tool. For this purpose, a recently developed fast and adaptive BEMD (FABEMD) is used to decompose the given image into several bidimensional intrinsic mode functions (BIMFs). In FABEMD, order statistics filters (OSFs) are employed to get the upper and lower envelopes in the decomposition process, instead of surface interpolation, which enables fast decomposition and well-characterized BIMFs. Binarization and morphological operations are applied to the first BIMF obtained from FABEMD to achieve the desired edges. The proposed approach is compared with several other edge detection methodologies, which include a combination of classical BEMD and morphological processing, the Canny and Sobel edge detectors, as well as combinations of BEMD/FABEMD and Canny/Sobel edge detectors. Simulation results with real images demonstrate the efficacy and potential of the proposed edge detection algorithm employing FABEMD.

scholarly journals Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

2014 ◽  
Vol 31 (9) ◽  
pp. 1982-1994 ◽  
Author(s):  
Xiaoying Chen ◽  
Aiguo Song ◽  
Jianqing Li ◽  
Yimin Zhu ◽  
Xuejin Sun ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Feature Extraction Method ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Bidimensional Empirical Mode Decomposition

Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

An improved bidimensional empirical mode decomposition: A mean approach for fast decomposition

2014 ◽  
Vol 98 ◽  
pp. 344-358 ◽  
Author(s):  
Chin-Yu Chen ◽  
Shu-Mei Guo ◽  
Wei-sheng Chang ◽  
Jason Sheng-Hong Tsai ◽  
Kuo-Sheng Cheng
Keyword(s):  
Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Bidimensional Empirical Mode Decomposition ◽  
Fast Decomposition
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