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.

scholarly journals Follicle Detection in Ultrasound Images using Adaptive Clustering Algorithms and Empirical Mode Decomposition

2019 ◽  
Vol 9 (2) ◽  
pp. 2137-2141
Keyword(s):  
Human Body ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Polycystic Ovary ◽  
Clustering Algorithms ◽  
Ultrasound Image ◽  
Human Reproduction ◽  
Ultrasound Images ◽  
Number Of Clusters ◽  
K Value

Ultrasound Imaging is one of the techniques 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.Monitoring of follicle is important in human reproduction. This paper presents a method for follicle detection in ultrasound image of ovaries using Adaptive data clustering algorithms. The main requirements for any clustering algorithm are to initialize the value of K, i.e. the number of clusters. Estimating this K value is difficult task for given data. This paper presents adaptive data clustering algorithm which generates accurate segmentation results with simple operation and avoids the interactive input K (number of clusters) value for segmentation. The results represent adaptive data clustering algorithms are better than normal algorithms for clustering in ultrasound image segmentation. After segmentation, using the region properties of the image, the follicles in the ovary image are identified. The proposed algorithm is tested on sample ultrasound images of ovaries for identification of follicles and with the region properties, the ovaries are classified into categories, normal, cystic and polycystic ovary with its geometric properties.

scholarly journals NOISE REDUCTION METHOD FOR OCT IMAGES BASED ON EMPIRICAL MODE DECOMPOSITION

2013 ◽  
Vol 06 (02) ◽  
pp. 1350009 ◽  
Author(s):  
OLEG O. MYAKININ ◽  
DMITRY V. KORNILIN ◽  
IVAN A. BRATCHENKO ◽  
VALERIY P. ZAKHAROV ◽  
ALEXANDER G. KHRAMOV
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Noise Suppression ◽  
Reduction Method ◽  
Tissue Structure ◽  
Diagnostic Process ◽  
Important Process ◽  
Post Processing ◽  
Mode Decomposition ◽  
Mesh Implant

In this paper, the new method for OCT images denoizing based on empirical mode decomposition (EMD) is proposed. The noise reduction is a very important process for following operations to analyze and recognition of tissue structure. Our method does not require any additional operations and hardware modifications. The basics of proposed method is described. Quality improvement of noise suppression on example of edge-detection procedure using the classical Canny's algorithm without any additional pre- and post-processing operations is demonstrated. Improvement of raw-segmentation in the automatic diagnostic process between a tissue and a mesh implant is shown.

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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