Medical Image Segmentation by More Sensitive Adaptive Thresholding

Kim, Cheol-Hwan; Yoon, Jiyoung; Lee, Yun‐Jung

doi:10.1109/icitcs.2016.7740353

Cited by 9 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To solve this problem, we have supplemented the adaptive segmentation algorithm. It divides the region of interest primarily using the adaptive threshold method and applies the morphology method to the divided regions [6]. In this method, thin-shaped areas with small brightness values are selected and made into connected objects.…”

Section: Fig 3 the Results Of Tooth Segmentation On Ctmentioning

confidence: 99%

Development of 3D Printing Software for Medical Customized Treatments

2019

JESR

View full text Add to dashboard Cite

3D printing technology is widely used in the medical field. In particular, many customized treatments are being produced and studied. Customized treatments can be created using 3D printers, and 3D models and Gcodes for individuals can be created using tomographic images. It is important to create a 3D model by precise segmentation of tomographic images. In order to increase the precision of segmentation, we applied the adaptive thresholding method with morphological process. We applied a look-up table to quickly correct holes and vertex errors in 3D models. In this paper, we proposed the 3D printing software development method for customized treatments.

show abstract

Section: Fig 3 the Results Of Tooth Segmentation On Ctmentioning

confidence: 99%

Development of 3D Printing Software for Medical Customized Treatments

2019

JESR

View full text Add to dashboard Cite

show abstract

“…Global threshold segmentation cannot consider all pixels in the image, making it challenging to accurately separate the targets from the background. To improve the real-time performance of detection, a fast adaptive thresholding segmentation algorithm is adopted [19] . This algorithm traverses the entire image to calculate the average grayscale value of the last s pixels.…”

Section: Fast Adaptive Threshold Segmentationmentioning

confidence: 99%

Research on surface defect detection of aerospace electronic components based on machine vision

Zhang,

Pei,

Liu

et al. 2023

Third International Conference on Optics and Communication Technology (ICOCT 2023)

View full text Add to dashboard Cite

Aiming at the problems of low detection efficiency and high labor cost for manual aerospace electronic components surface, this paper proposes a surface defect detection method based on machine vision. Firstly, based on the design of the components surface defect detection device, the characteristics of the surface image are analyzed. Then, the image is preprocessed by bilateral filtering algorithm and morphological operation, and the edge is removed while the noise is removed. Finally, the fast adaptive threshold is adopted. Segmentation to achieve defect detection on the components surface. The experimental results show that the detection method has a good detection effect on the surface of the components. The detection rate and false detection rate are 3.2% and3.8%, respectively, which provides a feasible solution for the detection of defects on the surface of aerospace electronic products. Has a certain practical value.

show abstract

“…Even though the cluster centroid did not directly represent any seed, we could approximate the nearest pixel to the centroid as seed using Euclidean distance as shown in (4).…”

Section: Automatic Generation Of Seedsmentioning

confidence: 99%

“…Compared to fixed threshold, adaptive threshold is more adaptive application-wise due to its flexible threshold setting. Therefore, the application of threshold method is wide; ranging from medical image segmentation [4], computer-aided diagnosis [5] to image denoising [6].…”

Section: Introductionmentioning

confidence: 99%

Local mean based adaptive thresholding to classify the cartilage and background superpixels

Gan

Salam

Khaizi

et al. 2019

IJEECS

View full text Add to dashboard Cite

<em><span>Semi-automatic segmentation is common in medical image processing because anatomical geometries demonstrated by human anatomical parts often requires manual supervision to provide desirable results. However, semi-automatic segmentation has been infamous for requiring excessive human intervention and time consuming. In order to reduce a forementioned problems, seed labels have been generated automatically using superpixels in our previous works. A fixed threshold method has been implemented to classify cartilage and background superpixels but this method is reported to lack the adaptiveness to changing image properties in 3D magnetic resonance image of knee. As a result, the coverage of background seeds are not sufficient to cover whole background area in some cases. In this work, we proposed a local mean based adaptive threshold method as a better alternative to the fixed threshold method. We calculated local mean for each block in an integral image and then use it to differentiate background superpixels from cartilage superpixels. The method is robust to illumination changes and simple to use. We tested the adaptive threshold on 35 knee images of different anatomical geometries and proved the proposed method could provide more comprehensive background seed labels distribution compared to fixed threshold method</span></em>

show abstract

Medical Image Segmentation by More Sensitive Adaptive Thresholding

Cited by 9 publications

References 6 publications

Development of 3D Printing Software for Medical Customized Treatments

Development of 3D Printing Software for Medical Customized Treatments

Research on surface defect detection of aerospace electronic components based on machine vision

Local mean based adaptive thresholding to classify the cartilage and background superpixels

Contact Info

Product

Resources

About