Fast Image Segmentation Using Two-Dimensional Otsu Based on Estimation of Distribution Algorithm

Wang, Wuli; Duan, Liming; Wang, Yong

doi:10.1155/2017/1735176

Cited by 27 publications

(13 citation statements)

References 29 publications

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“…Otsu algorithm generalized from a single threshold to a multi-threshold, but the essence of the algorithm is still an exhaustive method with very high time complexity. When the traditional multi-threshold Otsu method calculates the optimal threshold, it takes a long time to find the optimal threshold for the L N sub-exhaustive traversal calculation of the gray space [20,21] . Therefore, if the threshold search range can be reduced, the segmentation efficiency can be improved.…”

Section: Improved Otsu Segmentation Threshold Methodsmentioning

confidence: 99%

Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature AdaBoost classifier

Wang

Wei-liang

et al. 2020

International Journal of Agricultural and Biological Engineering

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Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines. This study proposed a three-dimensional (3D) recognition method combining monocular vision and machine learning algorithms. According to the color characteristics of the targets, to convert the original color picture into YC b C r mode and use the 2D Otsu algorithm to perform gray level image segmentation on the C b channel. Then the Haar-feature training was carried out. The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms, while its false recognized rate was 3.91%. The strong classifier was formed by weight combination, and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system. The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck. It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature. The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm. Its correct rate can reach 95%. It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.

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Section: Improved Otsu Segmentation Threshold Methodsmentioning

confidence: 99%

Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature AdaBoost classifier

Wang

Wei-liang

et al. 2020

International Journal of Agricultural and Biological Engineering

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“…Another way of segmentation is automatically finding the optimal threshold value by observing the distributed pixel values. Otsu's segmentation (Wang et al, 2017) finds this threshold that classifies the image into multiple clusters so that intraclass variance is minimized. Using the histogram of the image, we get the spread of intensities of the pixel values which is used to classify them based on the number of clusters the user specifies.…”

Section: Otsu's Algorithmmentioning

confidence: 99%

Validation of Segmented Brain Tumor from MRI Images Using 3D Printingthe

Nayak

Balachandra

Manjunath

et al. 2021

Asian Pac J Cancer Prev

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Background: Early diagnosis of a brain tumor is important for improving the treatment possibilities. Manually segmenting the tumor from the volumetric data is time-consuming, and the visualization of the tumor is rather challenging. Methods: This paper proposes a user-guided brain tumour segmentation from MRI (Magnetic Resonance Imaging) images developed using Medical Imaging Interaction Toolkit (MITK) and printing the segmented object using the 3D printer for tumour quantification. The proposed method includes segmenting the tumour interactively using connected threshold method, then printing the physical object from the segmented volume of interest. Then the distance between two voxels was measured using electronic callipers on the 3D volume in a specific direction. And next, the same distance was measured in the same direction on the 3D printed object. Results: The technique was tested with n=5 samples (20 readings) of brain MRI images from RIDER Neuro MRI dataset of National Cancer Institute. MITK provides various tools that enable image visualization, registration, and contouring. We were able to achieve the same measurements using both the approaches and this has been tested statistically with paired t-test method. Through this and the observer's opinion, the accuracy of the segmentation was proved. Conclusion: When the difference in measurement of tumor volume through the electronic calipers and with 3D printed object equates to zero, proves that the segmentation technique is accurate. This helps to delineate the tumor more accurately during radio therapy.

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“…For this reason, images having complex boundaries may yield a poor threshold performance. Therefore, Otsu's 1-D method is extended to the 2-D version, where the 2-D histogram of an image is used [16][17][18]. Also, the 2-D Otsu's criteria has been extended to be implemented to multilevel threshold.…”

Section: A Two Dimension Otsu Techniquementioning

confidence: 99%

Optimal Global Threshold based on Two Dimension Otsu for Block Size Decision in Intra Prediction of H.264/AVC Coding

Gharghory¹

2019

IJACSA

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The Advanced Video Coding (H.264/AVC) has proved its ability in finding the tradeoff between the compressed bit rate value and the visual quality of video comparing to the others of traditional coding. One of the most encoder stages consuming time is the intra prediction in which different sizes of a block are exhaustively examined for selecting the suitable block size to the best block mode decision. In this paper, an efficient approach is suggested to select the best block size for the intra prediction adaptively to achieve high compression efficiency. The proposed approach exploits the idea of quad tree decomposition for blocks partitioning based on a predefined threshold value. An optimal global threshold value based on two dimension Otsu technique is suggested for the decision of block division in this work. The proposed technique is carried out on different set of videos resolutions with different quantization parameters using Matlab software. The comparison of the proposed approach with the reference JM18.6 video coding is done in terms of bit rate (BR), time saving and peak signal to noise ratio (PSNR). A tangible acceleration on the running time can be accomplished besides improvement in both of visual quality and bit rate with some of QCIF and CIF videos resolution by the proposed technique. The simulation results demonstrate saving in time by average 42% to 68% with CIF and QCIF videos. Concerning the visual quality in terms of Bjontegaard Delta parameters, the PSNR improved in which its value increased from 0.2 to 1.6, while the value of BR reduced from 0.79 to 15.3 respectively with some videos of resolutions QCIF, CIF and 720p. In addition, the performance of the suggested approach with the high resolution videos achieves minor improvement with some of them while has a slightly degradation with others of them.

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Fast Image Segmentation Using Two-Dimensional Otsu Based on Estimation of Distribution Algorithm

Cited by 27 publications

References 29 publications

Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature AdaBoost classifier

Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature AdaBoost classifier

Validation of Segmented Brain Tumor from MRI Images Using 3D Printingthe

Optimal Global Threshold based on Two Dimension Otsu for Block Size Decision in Intra Prediction of H.264/AVC Coding

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