scholarly journals A Fast Otsu Thresholding Method Based on an Improved 2D Histogram

2021 ◽  
Vol 15 ◽  
pp. 953-959
Author(s):  
Yanli Tan ◽  
Yongqiang Zhao
Keyword(s):  
Image Segmentation ◽  
Gray Level ◽  
Integral Image ◽  
Otsu Thresholding ◽  
Computational Speed ◽  
Otsu Method ◽  
Simulation Results ◽  
Edge Features ◽  
2D Histogram ◽  
Average Gray Level

The regional division of a traditional 2D histogram is difficult to obtain satisfactory image segmentation results. Based on the gray level-gradient 2D histogram, we proposed a fast 2D Otsu method based on integral image. In this method, the average gray level is replaced by the gray level gradient in the neighborhood of pixels, and the edge features of the image are extracted according to the gray level difference between adjacent pixels to improve the segmentation effect. Calculating the integral image from the two-dimensional histogram reduces the computational complexity of searching the optimal threshold, thus reducing the amount of computation. The simulation results demonstrate that the proposed algorithm has better performance in image segmentation, with the increased computational speed and improved real-time capability.

scholarly journals Fast Image Segmentation Based on Efficient Implementation of the Chan-Vese Model with Discrete Gray Level Sets

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Songsong Li ◽  
Qingpu Zhang
Keyword(s):  
Image Segmentation ◽  
Level Sets ◽  
Lagrange Equation ◽  
Gray Level ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Computational Speed ◽  
Low Pass ◽  
The One ◽  
Segmentation Models

A new image segmentation based on fast implementation of the Chan-Vese model is proposed. This approach differs from previous methods in that we do not need to solve the Euler-Lagrange equation of the underlying variational problem. First, through experiments, we observe that for the smooth image segmentation, Chan-Vese model (CVM) can be simplified. Utilizing the Gaussian low pass filter, we pretreat the original image and regularize the level curves. Then, we calculate the energy directly on discrete gray level sets, find the minimizer of the energy, and obtain the segmentation results. We analyze the algorithm and prove that under discrete gray level sets, the global minimum of the energy is same as the one obtained by the previous methods. Another advantage of this method is that the reinitialization is not needed. Since there are at most 255 discrete gray level sets, the algorithm improves the computational speed dramatically. And the complexity of the algorithm isO(N), whereNis the number of pixels in the image. So even for the large images, it is also very efficient. We apply our segmentation algorithm to synthetic and real world images to emphasize the performances of our model compared with other segmentation models.

scholarly journals Research on 3D crack segmentation of CT images of oil rock core

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258463
Author(s):  
Yongning Zou ◽  
Gongjie Yao ◽  
Jue Wang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Ct Images ◽  
Gray Level ◽  
Ct Image ◽  
Median Filtering ◽  
Background Region ◽  
Level Set Segmentation ◽  
Binary Segmentation ◽  
Average Gray Level

In this paper, we propose a framework for CT image segmentation of oil rock core. According to the characteristics of CT image of oil rock core, the existing level set segmentation algorithm is improved. Firstly, an algorithm of Chan-Vese (C-V) model is carried out to segment rock core from image background. Secondly the gray level of image background region is replaced by the average gray level of rock core, so that image background does not affect the binary segmentation. Next, median filtering processing is carried out. Finally, an algorithm of local binary fitting (LBF) model is executed to obtain the crack region. The proposed algorithm has been applied to oil rock core CT images with promising results.

Combining Global Probability Density Difference and Local Gray Level Fitting for Ultrasound Image Segmentation

2010 ◽  
Vol 36 (7) ◽  
pp. 951-959 ◽  
Author(s):  
Bo LIU ◽  
Jian-Hua HUANG ◽  
Xiang-Long TANG ◽  
Jia-Feng LIU ◽  
Ying-Tao ZHANG
Keyword(s):  
Image Segmentation ◽  
Probability Density ◽  
Ultrasound Image ◽  
Density Difference ◽  
Gray Level ◽  
Ultrasound Image Segmentation

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

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.

The Detection Method of Lane Line Based on the Improved Otsu Threshold Segmentation

2015 ◽  
Vol 741 ◽  
pp. 354-358 ◽  
Author(s):  
Yang Shan Tang ◽  
Dao Hua Xia ◽  
Gui Yang Zhang ◽  
Li Na Ge ◽  
Xin Yang Yan
Keyword(s):  
Image Segmentation ◽  
Processing Time ◽  
Detection Method ◽  
Segmentation Algorithm ◽  
Test Results ◽  
Threshold Segmentation ◽  
Otsu Method ◽  
Time Requirements ◽  
Lane Line ◽  
Center Distance

For overcoming the shortage of Otsu method, proposed an improved Otsu threshold segmentation algorithm. On the basis of Otsu threshold segmentation algorithm, the gray level was divided into two classes according to the image segmentation, to determine the best threshold by comparing their center distance, so as to achieve peak line recognition under the condition of multiple gray levels. Then did experiments on image segmentation of the lane line with MATLAB by traditional Otsu threshold segmentation algorithm and the improved algorithm, the threshold of traditional Otsu threshold segmentation algorithm is 144 and the threshold of the improved Otsu threshold segmentation algorithm is 131, the processing time is within 0.453 s. Test results show that the white part markings appear more, the intersection place of white lines and the background is more clear, so this method can identify lane markings well and meet the real-time requirements.

Image Segmentation Using Rough Set Theory

2014 ◽  
Vol 1 (2) ◽  
pp. 62-74 ◽  
Author(s):  
Payel Roy ◽  
Srijan Goswami ◽  
Sayan Chakraborty ◽  
Ahmad Taher Azar ◽  
Nilanjan Dey
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Gray Level ◽  
Rule Generation ◽  
Local Minima ◽  
Fuzzy Correlation ◽  
Segmentation Methods

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

scholarly journals Modified KNN-LVQ for Stairs Down Detection Based on Digital Image

2021 ◽  
Vol 12 (3) ◽  
pp. 141
Author(s):  
Ahmad Wali Satria Bahari Johan ◽  
Sekar Widyasari Putri ◽  
Granita Hajar ◽  
Ardian Yusuf Wicaksono
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Hybrid Method ◽  
Visually Impaired ◽  
Blind People ◽  
Gray Level ◽  
Computational Speed ◽  
Occurrence Matrix ◽  
Computing Speed

Persons with visual impairments need a tool that can detect obstacles around them. The obstacles that exist can endanger their activities. The obstacle that is quite dangerous for the visually impaired is the stairs down. The stairs down can cause accidents for blind people if they are not aware of their existence. Therefore we need a system that can identify the presence of stairs down. This study uses digital image processing technology in recognizing the stairs down. Digital images are used as input objects which will be extracted using the Gray Level Co-occurrence Matrix method and then classified using the KNN-LVQ hybrid method. The proposed algorithm is tested to determine the accuracy and computational speed obtained. Hybrid KNN-LVQ gets an accuracy of 95%. While the average computing speed obtained is 0.07248 (s).

scholarly journals OILSPILL AND LOOK-ALIKE SPOTS FROM SAR IMAGERY USING OTSU METHOD AND ARTIFICIAL NEURAL NETWORK

2020 ◽  
Vol 4 (11) ◽  
pp. 1-10
Author(s):  
M. Sornam
Keyword(s):  
Image Segmentation ◽  
Oil Spill ◽  
Oil Spills ◽  
Marine Ecosystem ◽  
Dark Spot ◽  
Oil Spill Detection ◽  
Otsu Method ◽  
Artificial Neural ◽  
Propagation Network

Oil spill pollution plays a significant role in damaging marine ecosystem. Discharge of oil due to tanker accidents has the most dangerous effects on marine environment. The main waste source is the ship based operational discharges. Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. One major advantage of SAR is that it can generate imagery under all weather conditions. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish ‘oil spills’ from ‘look-alikes’. The features of detected dark spot are then extracted and classified to discriminate oil spills from look-alikes. This paper describes the development of a new approach to SAR oil spill detection using Segmentation method and Artificial Neural Networks (ANN). A SAR-based oil-spill detection process consists of three stages: image segmentation, feature extraction and object recognition (classification) of the segmented objects as oil spills or look-alikes. The image segmentation was performed with Otsu method. Classification has been done using Back Propagation Network and this network classifies objects into oil spills or look-alikes according to their feature parameters. Improved results have been achieved for the discrimination of oil spills and look-alikes.

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