A Spatially Adaptive Filter Reducing Arc Stripe Noise for Sector Scan Medical Ultrasound Imaging Qianren Xu Mohamed Kamel Magdy M. A. Salama.

Slides:



Advertisements
Similar presentations
3-D Computer Vision CSc83020 / Ioannis Stamos  Revisit filtering (Gaussian and Median)  Introduction to edge detection 3-D Computater Vision CSc
Advertisements

Contrast-Aware Halftoning Hua Li and David Mould April 22,
Topic 6 - Image Filtering - I DIGITAL IMAGE PROCESSING Course 3624 Department of Physics and Astronomy Professor Bob Warwick.
EDGE DETECTION ARCHANA IYER AADHAR AUTHENTICATION.
Treatment Planning of HIFU: Rigid Registration of MRI to Ultrasound Kidney Images Tara Yates 1, Penny Probert Smith 1, J. Alison Noble 1, Tom Leslie 2,
Hongliang Li, Senior Member, IEEE, Linfeng Xu, Member, IEEE, and Guanghui Liu Face Hallucination via Similarity Constraints.
An Adaptive Image Enhancement Algorithm for Face Detection By Lizuo Jin, Shin’ichi Satoh, and Masao Sakauchi. ECE 738 In Young Chung.
1Ellen L. Walker Edges Humans easily understand “line drawings” as pictures.
Alternatives to Spherical Microphone arrays: Hybrid Geometries Aastha Gupta & Prof. Thushara Abhayapala Applied Signal Processing CECS To be presented.
Wavelet-Domain Video Denoising Based on Reliability Measures Vladimir Zlokolica, Aleksandra Piˇzurica and Wilfried Philips Circuits and Systems for Video.
CSSE463: Image Recognition Day 6 Yesterday: Yesterday: Local, global, and point operators all operate on entire image, changing one pixel at a time!! Local,
EE663 Image Processing Edge Detection 2 Dr. Samir H. Abdul-Jauwad Electrical Engineering Department King Fahd University of Petroleum & Minerals.
Probabilistic video stabilization using Kalman filtering and mosaicking.
Introduction to Image Quality Assessment
Artistic Edge and Corner Enhancing Smoothing
Basic Image Processing January 26, 30 and February 1.
A Gentle Introduction to Bilateral Filtering and its Applications Limitation? Pierre Kornprobst (INRIA) 0:20.
Chapter 10: Image Segmentation
Presentation Image Filters
Medical Image Analysis Image Enhancement Figures come from the textbook: Medical Image Analysis, by Atam P. Dhawan, IEEE Press, 2003.
Lecture 03 Area Based Image Processing Lecture 03 Area Based Image Processing Mata kuliah: T Computer Vision Tahun: 2010.
Detection of nerves in Ultrasound Images using edge detection techniques NIRANJAN TALLAPALLY.
Course 9 Texture. Definition: Texture is repeating patterns of local variations in image intensity, which is too fine to be distinguished. Texture evokes.
December 9, 2014Computer Vision Lecture 23: Motion Analysis 1 Now we will talk about… Motion Analysis.
Digital Image Processing Lecture 10: Image Restoration March 28, 2005 Prof. Charlene Tsai.
Chapter 5: Neighborhood Processing
Vision Lab, Dept. of EE, NCTU Jui-Nan Chang
Digital Image Processing Lecture 10: Image Restoration
Ch5 Image Restoration CS446 Instructor: Nada ALZaben.
Digital Image Processing Lecture 16: Segmentation: Detection of Discontinuities Prof. Charlene Tsai.
Halftoning With Pre- Computed Maps Objective Image Quality Measures Halftoning and Objective Quality Measures for Halftoned Images.
I-SMOOTH FOR IMPROVED MINIMUM CLASSIFICATION ERROR TRAINING Haozheng Li, Cosmin Munteanu Pei-ning Chen Department of Computer Science & Information Engineering.
Face Image-Based Gender Recognition Using Complex-Valued Neural Network Instructor :Dr. Dong-Chul Kim Indrani Gorripati.
Course14 Dynamic Vision. Biological vision can cope with changing world Moving and changing objects Change illumination Change View-point.
Image enhancement Last update Heejune Ahn, SeoulTech.
Digital Image Processing Lecture 16: Segmentation: Detection of Discontinuities May 2, 2005 Prof. Charlene Tsai.
Instructor: Mircea Nicolescu Lecture 5 CS 485 / 685 Computer Vision.
Image Filtering with GLSL DI1.03 蔡依儒. Outline Convolution Convolution Convolution implementation using GLSL Convolution implementation using GLSL Commonly.
Digital Image Processing Week V Thurdsak LEAUHATONG.
Image Contrast Enhancement Based on a Histogram Transformation of Local Standard Deviation Dah-Chung Chang* and Wen-Rong Wu, Member, IEEE IEEE TRANSACTIONS.
Detection of nerves in Ultrasound Images using edge detection techniques NIRANJAN TALLAPALLY.
Edge Preserving Spatially Varying Mixtures for Image Segmentation Giorgos Sfikas, Christophoros Nikou, Nikolaos Galatsanos (CVPR 2008) Presented by Lihan.
Filters– Chapter 6. Filter Difference between a Filter and a Point Operation is that a Filter utilizes a neighborhood of pixels from the input image to.
A School of Mechanical Engineering, Hebei University of Technology, Tianjin , China Research on Removing Shadow in Workpiece Image Based on Homomorphic.
Zhaoxia Fu, Yan Han Measurement Volume 45, Issue 4, May 2012, Pages 650–655 Reporter: Jing-Siang, Chen.
A New Approach of Anisotropic Diffusion: Medical Image Application Valencia 18th-19th 2010 Y. TOUFIQUE*, L.MASMOUDI*, R.CHERKAOUI EL MOURSLI*, M. CHERKAOUI.
TISSUE HARMONIC IMAGING (THI) Aimi Alwani bt Mat Nawi A
Yun, Hyuk Jin. Theory A.Nonuniformity Model where at location x, v is the measured signal, u is the true signal emitted by the tissue, is an unknown.
Medical Image Analysis
Trilateral Filtering of Range Images Using Normal Inner Products
Chapter 10 Image Segmentation
Building Adaptive Basis Function with Continuous Self-Organizing Map
Digital Image Processing Lecture 16: Segmentation: Detection of Discontinuities Prof. Charlene Tsai.
Range Image Segmentation for Modeling and Object Detection in Urban Scenes Cecilia Chen & Ioannis Stamos Computer Science Department Graduate Center, Hunter.
Digital Image Processing Lecture 10: Image Restoration
Registration of Pathological Images
Image Enhancement via Adaptive Unsharp Masking
PLIP BASED UNSHARP MASKING FOR MEDICAL IMAGE ENHANCEMENT
The Chinese University of Hong Kong
Spatially Varying Frequency Compounding of Ultrasound Images
Prostate Edge Detection Using a Knowledge Base
Lecture 1: Images and image filtering
Contrast-Aware Halftoning
Basic Image Processing
Morphing WU PO-HUNG.
Ultrasound Despeckling for Contrast Enhancement
Source: Journal of Structural Biology 160 (2007)
Lecture 1: Images and image filtering
Color image noise removal algorithm utilizing hybrid vector filtering
Presentation transcript:

A Spatially Adaptive Filter Reducing Arc Stripe Noise for Sector Scan Medical Ultrasound Imaging Qianren Xu Mohamed Kamel Magdy M. A. Salama

2 Outline Introduction Method Experiment Results Conclusion

3 Introduction Sector scan ultrasound images usually have arc stripes; They do not represent the physical structure of the tissue; Thus they can be viewed as a kind of noise.

4 The Source of the Arc Stripe Noise Assume that there are point targets with same size. The lateral size of image of these points increase beyond focal zone. These laterally wider images will be superimposed on the far sides of the focal location, and thus these target points that are originally separated will show as arc stripes in far-field and near- field.

5 The properties of these noise Special geometric properties of the arc stripe noise: Circular symmetry. The intensity and size of the arc stripes change with the radial depth. The proposed filter is based on the geometric properties

6 Proposed Filter It consists of two components: Radially adaptive filtering operators Common Gaussian filtering operator +

7 Radially Adaptive Filtering Operators: Basic Radial Filtering Operators at Special Directions x y

8 Radially Adaptive Filtering Operators: Radial Filtering Operators at Arbitrary Directions The filtering operator at any azimuth angle θ is determined by soft weighted summation of neighbor basic radial filtering operators

9 The Combined Filter Weighted Summation of the Radial Filtering Operator and Gaussian Filtering Operator: Radial filtering operators aim to reduce random directional noise Common Gaussian filter is used 1) to counteract the radial stripe artifact, and 2) suppress the non- directional noise

10 An Example of the Combined Filter

11 Selection of Parameters 1.The weight ω g and ω g are determined by the ratio of non-directional and arc stripe noise components 2.The Gaussian standard deviation σ of op g and op m are determined by the size of non-directional and arc stripe noise noises respectively 3.The size of filter mask is determined by noise size

12 Testing Image on the Radial Filter (b) Filtered image by the radial filter (a) Original testing image

13 Filtered image by Gaussian filter Filtered image by the proposed filter

14 Example II (a) The original image of fetus (b) The filtered image

15 Conclusion This paper identifies a significant noise, the arc stripes in sector scan medical ultra-sound image, and generalizes the characteristics of the arc stripe noise. The proposed filtering algorithm deals with the arc stripe noise by utilizing the geometric characteristics of the special noise, The parameters of the filter are adapted with the radial depth in order to effectively smooth noise and deblur the useful image detail. The results show that the proposed filter obviously enhances image quality and is superior to common smoothing filter.

16 Thanks for your time Questions?