scholarly journals Fast and Accurate Semiautomatic Segmentation of Individual Teeth from Dental CT Images

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ho Chul Kang ◽  
Chankyu Choi ◽  
Juneseuk Shin ◽  
Jeongjin Lee ◽  
Yeong-Gil Shin
Keyword(s):  
Multicore Processors ◽  
Region Growing ◽  
Volume Measurement ◽  
Ct Images ◽  
Average Error ◽  
Dental Surgery ◽  
Dental Ct ◽  
Seeded Region Growing ◽  
Conventional Methods ◽  
The Individual

DIn this paper, we propose a fast and accurate semiautomatic method to effectively distinguish individual teeth from the sockets of teeth in dental CT images. Parameter values of thresholding and shapes of the teeth are propagated to the neighboring slice, based on the separated teeth from reference images. After the propagation of threshold values and shapes of the teeth, the histogram of the current slice was analyzed. The individual teeth are automatically separated and segmented by using seeded region growing. Then, the newly generated separation information is iteratively propagated to the neighboring slice. Our method was validated by ten sets of dental CT scans, and the results were compared with the manually segmented result and conventional methods. The average error of absolute value of volume measurement was2.29±0.56%, which was more accurate than conventional methods. Boosting up the speed with the multicore processors was shown to be 2.4 times faster than a single core processor. The proposed method identified the individual teeth accurately, demonstrating that it can give dentists substantial assistance during dental surgery.

scholarly journals Tooth shape reconstruction from dental CT images with the region-growing method

2014 ◽  
Vol 43 (6) ◽  
pp. 20140080 ◽  
Author(s):  
R Yanagisawa ◽  
Y Sugaya ◽  
S Kasahara ◽  
S Omachi
Keyword(s):  
Region Growing ◽  
Ct Images ◽  
Shape Reconstruction ◽  
Tooth Shape ◽  
Dental Ct

An improved seeded region growing algorithm

1997 ◽  
Vol 18 (10) ◽  
pp. 1065-1071 ◽  
Author(s):  
Andrew Mehnert ◽  
Paul Jackway
Keyword(s):  
Region Growing ◽  
Seeded Region Growing

scholarly journals Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

2020 ◽  
Vol 10 (7) ◽  
pp. 2346 ◽  
Author(s):  
May Phu Paing ◽  
Kazuhiko Hamamoto ◽  
Supan Tungjitkusolmun ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Random Forest ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Region Growing ◽  
Favorable Result ◽  
Dimensional Chain ◽  
Chain Code ◽  
Seeded Region Growing ◽  
Chain Coding ◽  
Diagnosis Of Lung Cancer

The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.

Region-growing fully convolutional neural network interactive segmentation of liver CT images

2021 ◽  
Vol 36 (9) ◽  
pp. 1294-1304
Author(s):  
Li-juan ZHANG ◽  
◽  
Run ZHANG ◽  
Dong-ming LI ◽  
Yang LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Region Growing ◽  
Ct Images ◽  
Interactive Segmentation ◽  
Liver Ct

Generation of a 3D model of the inside volume of shoes for e-commerce applications using industrial X-ray computed tomography

2021 ◽  
Author(s):  
J Wittmann ◽  
G Herl ◽  
J Hiller
Keyword(s):  
Computed Tomography ◽  
3D Model ◽  
Region Growing ◽  
Surface Model ◽  
Economic Potential ◽  
X Ray ◽  
Internet Users ◽  
Surface Data ◽  
X Ray Computed ◽  
The Individual

Abstract In 2018, 47 % of global internet users had purchased footwear products through the internet, making it the second most popular online shopping category worldwide right after clothing with 57 %. In the same year, on average, about every sixth parcel delivered in Germany (16.3 %) was returned. With the effort and costs that are associated with the return of shoes, the objective of reducing the number of returns for shoes promises an enormous economic potential and helps to reduce the CO2 emissions due to a lower trafic volume. This paper presents a workflow for determining the inside volume surface of shoes using industrial X-ray computed tomography (CT). The fundamental idea is based on the Region Growing (RG) method for the segmentation of the shoe's inner volume. Experiments are performed to illustrate the correlation of image quality and segmentation result. After obtaining the 3D surface model of an individual foot, the inner volume surface data of a scanned shoe can then be registered and evaluated in order to provide a reliable feedback for the customer regarding the accuracy of fit of a shoe and the individual foot on the basis of an overall "metric of comfort" before buying online. This step is not part of the work at hand. Conclusions are drawn and suggestions for improving the robustness and the exibility of the workflow are given, so it can be adapted to various shoe types and implemented in a fully automated measurement process in the future.

scholarly journals Liver tumour classification using average correction higher order local autocorrelation coefficient and legendre moments

2018 ◽  
Vol 7 (2.6) ◽  
pp. 306
Author(s):  
Aravinda H.L ◽  
M.V Sudhamani
Keyword(s):  
Texture Feature ◽  
Region Growing ◽  
Ct Images ◽  
Higher Order ◽  
Liver Carcinoma ◽  
Tumor Segmentation ◽  
Abdominal Ct ◽  
Simple Linear Iterative Clustering ◽  
Classification Of Tumors ◽  
Legendre Moments

The major reasons for liver carcinoma are cirrhosis and hepatitis.  In order to  identify carcinoma in the liver abdominal CT images are used. From abdominal CT images, segmentation of liver portion using adaptive region growing, tumor segmentation from extracted liver using Simple Linear Iterative Clustering is already implemented. In this paper, classification of tumors as benign or malignant is accomplished using Rough-set classifier based on texture feature extracted using Average Correction Higher Order Local Autocorrelation Coefficients and Legendre moments. Classification accuracy achieved in proposed scheme is 90%. The results obtained are promising and have been compared with existing methods.

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