A Note on the Use of (Gray Level, Local Average Gray Level) Space as an Aid in Threshold Selection.

It is a difficult task to binarize image under uneven illumination, and this problem is always met in the image recognition system, such as two-dimensional barcode scanning terminal. In this paper, an efficient approach is proposed to binarize image which can tolerant uneven illumination and different light intensity. The method initializes thresholds with local average gray level and adjusts thresholds by calculating light density ratio. Due to characteristic of our approach, it can even obtain a sound result by limiting number of iterations which will seriously reduce computations and space cost. According to experiments, we can find that our method can achieve a good performance and meet the real-time requirement and quality demand for barcode scanning terminal.

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Frequency and average gray-level information for thermal ablation status in ultrasound B-Mode sequences

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-0023 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Jens Ziegle ◽

Alfredo Illanes ◽

Axel Boese ◽

Michael Friebe

Keyword(s):

Thermal Ablation ◽

Ex Vivo ◽

Cell Damage ◽

Potential Temperature ◽

Image Sequences ◽

Target Tissue ◽

Gray Level ◽

Porcine Liver ◽

Time Frequency ◽

Average Gray Level

AbstractDuring thermal ablation in a target tissue the information about temperature is crucial for decision making of successful therapy. An observable temporal and spatial temperature propagation would give a visual feedback of irreversible cell damage of the target tissue. Potential temperature features in ultrasound (US) B-Mode image sequences during radiofrequency (RF) ablation in ex-vivo porcine liver were found and analysed. These features could help to detect the transition between reversible and irreversible damage of the ablated target tissue. Experimental RF ablations of ex-vivo porcine liver were imaged with US B-Mode imaging and image sequences were recorded. Temperature was simultaneously measured within the liver tissue around a bipolar RF needle electrode. In the B-Mode images, regions of interest (ROIs) around the centre of the measurement spots were analysed in post-processing using average gray-level (AVGL) compared against temperature. The pole of maximum energy level in the time-frequency domain of the AVGL changes was investigated in relation to the measured temperatures. Frequency shifts of the pole were observed which could be related to transitions between the states of tissue damage.

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A New Approach for B Ultrasound Images Enhancement

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.785-786.1391 ◽

2013 ◽

Vol 785-786 ◽

pp. 1391-1394

Author(s):

Chun Ying Pang ◽

Qi Yu Jiao

Keyword(s):

Clinical Practice ◽

Image Enhancement ◽

Ultrasound Images ◽

Gray Level ◽

Threshold Selection ◽

New Approach ◽

Fuzzy Algorithm ◽

Enhancement Method

To make B ultrasound images clear, a new image enhancement method was studied.The study used an improved fuzzy algorithm based on gray-level to process B ultrasound images. And the process is largely simple by adding threshold selection which could meet different clinical demand. Several kinds of enhancement algorithms for B ultrasound images are evaluated and compared by using MATLAB to process the same image. The results after contrast show that the improved fuzzy algorithm is effective and achieved in clinical practice.

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Non-invasive Estimation of Temperature during Physiotherapeutic Ultrasound Application Using the Average Gray-Level Content of B-Mode Images: A Metrological Approach

Ultrasound in Medicine & Biology ◽

10.1016/j.ultrasmedbio.2017.04.008 ◽

2017 ◽

Vol 43 (9) ◽

pp. 1938-1952 ◽

Cited By ~ 5

Author(s):

André V. Alvarenga ◽

Volker Wilkens ◽

Olga Georg ◽

Rodrigo P.B. Costa-Félix

Keyword(s):

Gray Level ◽

Non Invasive ◽

Ultrasound Application ◽

Metrological Approach ◽

Average Gray Level

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Hyperacuity Image Sensors

MRS Proceedings ◽

10.1557/proc-377-833 ◽

1995 ◽

Vol 377 ◽

Cited By ~ 1

Author(s):

David K. Biegelsen ◽

Warren B. Jackson ◽

René Lujan ◽

David Jared ◽

Richard L. Weisfield

Keyword(s):

Amorphous Silicon ◽

Image Sensors ◽

Gray Level ◽

Silicon Sensors ◽

Sensor Processing ◽

Position Sensitive ◽

The Individual ◽

Position Sensitive Detectors ◽

Silicon Sensor ◽

Average Gray Level

ABSTRACTThe human visual system perceives much smaller spatial steps in edges between high contrast regions than equivalents fine, periodic features. This characteristic is known as hyperacuity. We have designed, simulated, fabricated and characterized amorphous silicon sensors which provide hyperacuity information. The individual pixels are position sensitive detectors, the outputs of which provide the x and y first moments of the cell illumination pattern as well as the average gray level. In the simplest case the top electrode of a standard p-i-n diode sensor is replaced by four edge strip electrodes. Both quadrilateral cells (having all four lateral electrodes on the same side of the p-i-n diode) and duolateral cells (having x-electrodes on top and y-electrodes on bottom) have been tested. Results of probing the cells with rastered spots show that both types provide usable linearity and sensitivity. The duolateral structure provides greater orthogonality of the x and y information. One μm spatial resolution can be achieved with devices compatible with standard amorphous silicon sensor processing.

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CONNECTIONIST MODEL BINARIZATION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001491000363 ◽

1991 ◽

Vol 05 (04) ◽

pp. 629-644 ◽

Cited By ~ 5

Author(s):

NOBORU BABAGUCHI ◽

KOJI YAMADA ◽

KOICHI KISE ◽

YOSHIKAZU TEZUKA

Keyword(s):

Back Propagation ◽

Connectionist Model ◽

Gray Level ◽

Threshold Selection ◽

Back Propagation Algorithm ◽

Image Binarization ◽

Multilayer Network ◽

Binarization Method ◽

Gray Level Histogram ◽

Binarized Image

Image binarization is a task to convert gray-level images into bi-level ones. Its underlying notion can be simply thought of as threshold selection. However, the result of binarization will cause significant influence on the process of image recognition or understanding. In this paper we discuss a new binarization method, named CMB (connectionist model binarization), which uses the connectionist model. In the method a gray-level histogram is input to a multilayer network trained with the back-propagation algorithm to obtain a threshold which gives a visually suitable binarized image. From the experimental results, it was verified that CMB is an effective binarization method in comparison with other methods.

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