Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction

2014 ◽  
Vol 41 (7) ◽  
pp. 072903 ◽  
Author(s):  
Stavros Tsantis ◽  
Stavros Spiliopoulos ◽  
Aikaterini Skouroliakou ◽  
Dimitrios Karnabatidis ◽  
John D. Hazle ◽  
...  
Keyword(s):  
Edge Detection ◽  
Ultrasound Image ◽  
Speckle Reduction ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Multiresolution Edge Detection

scholarly journals Edge detection in MRI brain tumor images based on fuzzy C-means clustering

2018 ◽  
Vol 126 ◽  
pp. 1261-1270 ◽  
Author(s):  
Alexander Zotin ◽  
Konstantin Simonov ◽  
Mikhail Kurako ◽  
Yousif Hamad ◽  
Svetlana Kirillova
Keyword(s):  
Brain Tumor ◽  
Edge Detection ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Fuzzy C Means Clustering

scholarly journals Penentuan Kurva Kelengkungan Tulang Belakang pada Citra X-ray Skoliosis Menggunakan Metode Fuzzy C-Means

2019 ◽  
Vol 3 (1) ◽  
pp. 9
Author(s):  
Bagus Adhi Kusuma
Keyword(s):  
Early Detection ◽  
Edge Detection ◽  
Cobb Angle ◽  
Clustering Algorithm ◽  
X Ray ◽  
Fuzzy C Means ◽  
Spinal Curve ◽  
Fuzzy C Means Clustering ◽  
Four Levels ◽  
Curve Fitting Method

Scoliosis is a disorder that requires X-ray detection. Early detection is needed by patients with scoliosis. Based on information obtained through early detection, it will enable doctors to take the initial steps of treatment quickly. Determination of the spinal curve is the first step that serves to measure how severe the angle of the scoliosis curve. The angular severity of the scoliosis disorder can be calculated using the Cobb angle. Therefore, by estimating the spinal curve, we can also estimate the Cobb's angle. Based on the method from the previous study, the interobserver value can touch 11.8o with an error in the intraobserver measurement of 6o. Thus, during the Cobb Angle calculation process, subjective aspects are common and can still be tolerated today. But this aspect can also be the most frequent problem when using manual measurement methods by doctors. This study proposes an algorithm to measure the curve of the spine with computer-aided of X-ray images quickly with a error level error that is still within tolerance value. The data pre-processing process is carried out using Canny edge detection. The Fuzzy C-Means clustering algorithm (FCM) can detect the center point of the vertebral segment after segmentation of edge detection pre-processing. Formation of the spinal curve is done by the polynomial curve fitting method with the results of accuracy of 2.45o. Based on information on the spinal curve, the severity of the form of the scoliosis curve can be classified into four types, normal, mild, moderate and severe. Of the four levels, this system can be used to detect whether a person has scoliosis or not.

Despeckling of Ultrasound Images in Contourlet Domain

2013 ◽  
Vol 647 ◽  
pp. 283-287
Author(s):  
Yu Shu Liu ◽  
Ming Yan Jiang
Keyword(s):  
Edge Detection ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Image Feature ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Edge Preservation ◽  
Directional Information ◽  
Relative Variance ◽  
Disease Diagnostics

Ultrasound images are the important foundation for disease diagnostics. Unfortunately, speckle noise is an inherent property of ultrasound images. So speckle reduction is an important pre-processing step in the ultrasound image feature extraction and analysis. This paper proposes a novel noise reduction algorithm for ultrasound images, which is based on edge detection of the images using the directional information of contourlet transform. The relative variance of the contourlet coefficients is used as a measure of edge detection. The adaptive threshold can be calculated using the probability density function of relative variance. It is shown that the proposed method outperforms several existing techniques in terms of the universal index, edge preservation and visual quality, and in addition, is able to maintain the significant details of ultrasound images.

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