Filter-Based Synthetic Transmit and Receive Focusing

2001 ◽  
Vol 23 (2) ◽  
pp. 73-89 ◽  
Author(s):  
Meng-Lin Li ◽  
Pai-Chi Li
Keyword(s):  
Image Quality ◽  
Ultrasonic Imaging ◽  
Imaging Systems ◽  
System Complexity ◽  
Quality Degradation ◽  
Filtering Technique ◽  
Focal Zone

Most diagnostic ultrasonic imaging systems perform fixed focusing on transmit and dynamic focusing on receive. Such systems suffer from image quality degradation at depths away from the transmit focal zone. Several dynamic transmit focusing techniques have been previously investigated. Among them, a filter-based, retrospective focusing technique was proposed to increase the length of the transmit focal zone. In this paper, the filter-based technique is extended from dynamic receive focusing to fixed receive focusing. It is shown that the filtering technique with fixed receive focusing can achieve an image quality similar to that of dynamic receive focusing with filtering. The performance of the proposed approach is verified using real ultrasound data. It is shown that the proposed approach with fixed receive focusing requires a longer filter than that with dynamic receive focusing. Nonetheless, system complexity is greatly reduced with synthetic transmit and receive focusing because the dynamic receive focusing circuit is no longer needed.

ROI Based Post Image Quality Assessment Technique on Multiple Localized Filtering Method On Kinect HD Sensor Mobile Robot

2018 ◽  
Vol 3 (2) ◽  
pp. 145
Author(s):  
Kholilatul Wardani ◽  
Aditya Kurniawan
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Assessment Model ◽  
Sensor Data ◽  
Edge Region ◽  
Kinect Sensor ◽  
Image Region ◽  
Filtering Technique

 The ROI (Region of Interest) Image Quality Assessment is an image quality assessment model based on the SSI (Structural Similarity Index) index used in the specific image region desired to be assessed. Output assessmen value used by this image assessment model is 1 which means identical and -1 which means not identical. Assessment model of ROI Quality Assessment in this research is used to measure image quality on Kinect sensor capture result used in Mobile HD Robot after applied Multiple Localized Filtering Technique. The filter is applied to each capture sensor depth result on Kinect, with the aim to eliminate structural noise that occurs in the Kinect sensor. Assessment is done by comparing image quality before filter and after filter applied to certain region. The kinect sensor will be conditioned to capture a square black object measuring 10cm x 10cm perpendicular to a homogeneous background (white with RGB code 255,255,255). The results of kinect sensor data will be taken through EWRF 3022 by visual basic 6.0 program periodically 10 times each session with frequency 1 time per minute. The results of this trial show the same similar index (value 1: identical) in the luminance, contrast, and structural section of the edge region or edge region of the specimen. The value indicates that the Multiple Localized Filtering Technique applied to the noise generated by the Kinect sensor, based on the ROI Image Quality Assessment model has no effect on the image quality generated by the sensor.

scholarly journals Two simple criteria to estimate an objective’s performance when imaging in non design tissue clearing solutions

2019 ◽  
Author(s):  
Sabrina Asteriti ◽  
Valeria Ricci ◽  
Lorenzo Cangiano
Keyword(s):  
Image Quality ◽  
High Refractive Index ◽  
Optimal Conditions ◽  
Point Spread Functions ◽  
Tissue Clearing ◽  
Fluorescent Microscope ◽  
Immersion Medium ◽  
Quality Degradation ◽  
Point Spread ◽  
The Impact

ABSTRACTTissue clearing techniques are undergoing a renaissance motivated by the need to image fluorescence deep in biological samples without physical sectioning. Optical transparency is achieved by equilibrating tissues with high refractive index (RI) solutions, which require expensive optimized objectives to avoid aberrations. One may thus need to assess whether an available objective is suitable for a specific clearing solution, or the impact on imaging of small mismatches between cleared sample and objective design RIs. We derived closed form approximations for image quality degradation versus RI mismatch and other parameters available to the microscopist. We validated them with computed (and experimentally confirmed) aberrated point spread functions, and by imaging fluorescent neurons in high RI solutions. Crucially, we propose two simple numerical criteria to establish: (i) the degradation in image quality (brightness and resolution) from optimal conditions of any clearing solution/objective combination; (ii) which objective, among several, achieves the highest resolution in a given immersion medium. These criteria apply directly to the widefield fluorescent microscope but are also closely relevant to more advanced microscopes.

A new beamforming technique for ultrasonic imaging systems

Ultrasonics ◽  
2000 ◽  
Vol 38 (1-8) ◽  
pp. 156-160
Author(s):  
G. Cincotti ◽  
G. Cardone ◽  
P. Gori ◽  
M. Pappalardo
Keyword(s):  
Ultrasonic Imaging ◽  
Imaging Systems ◽  
Beamforming Technique

Image quality degradation of object-color metamer mismatching in digital camera color reproduction

2018 ◽  
Vol 57 (11) ◽  
pp. 2851 ◽  
Author(s):  
Jueqin Qiu ◽  
Haisong Xu ◽  
Zhengnan Ye ◽  
Changyu Diao
Keyword(s):  
Image Quality ◽  
Digital Camera ◽  
Color Reproduction ◽  
Quality Degradation

scholarly journals Choosing the Optimal Spatial Domain Measure of Enhancement for Mammogram Images

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Karen Panetta ◽  
Arash Samani ◽  
Sos Agaian
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Visual Image ◽  
Imaging Systems ◽  
Medical Professionals ◽  
Optimal Operating ◽  
Negative Effect ◽  
Detection And Diagnosis ◽  
Mammogram Images

Medical imaging systems often require image enhancement, such as improving the image contrast, to provide medical professionals with the best visual image quality. This helps in anomaly detection and diagnosis. Most enhancement algorithms are iterative processes that require many parameters be selected. Poor or nonoptimal parameter selection can have a negative effect on the enhancement process. In this paper, a quantitative metric for measuring the image quality is used to select the optimal operating parameters for the enhancement algorithms. A variety of measures evaluating the quality of an image enhancement will be presented along with each measure’s basis for analysis, namely, on image content and image attributes. We also provide guidelines for systematically choosing the proper measure of image quality for medical images.

Sign in / Sign up

Export Citation Format

Share Document