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

Tooth shape reconstruction from dental micro CT images

2010 ◽  
pp. 300-301
Author(s):  
Shin Kasahara ◽  
Shinichiro Omachi ◽  
Hirotomo Aso ◽  
Kousuke Saito ◽  
Satoshi Yamada
Keyword(s):  
Ct Images ◽  
Shape Reconstruction ◽  
Micro Ct ◽  
Tooth Shape

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.

Tooth shape reconstruction from ct images using spline Curves

2007 ◽  
Author(s):  
Shinichiro Omachi ◽  
Kousuke Saito ◽  
Hirotomo Aso ◽  
Shin Kasahara ◽  
Satoshi Yamada ◽  
...  
Keyword(s):  
Ct Images ◽  
Shape Reconstruction ◽  
Tooth Shape ◽  
Spline Curves

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

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.

scholarly journals A COMPREHENSIVE FRAMEWORK FOR AUTOMATIC DETECTION OF PULMONARY NODULES IN LUNG CT IMAGES

2014 ◽  
Vol 33 (1) ◽  
pp. 13 ◽  
Author(s):  
Mehdi Alilou ◽  
Vassili Kovalev ◽  
Eduard Snezhko ◽  
Vahid Taimouri
Keyword(s):  
Early Stage ◽  
Pulmonary Nodules ◽  
Maximum Intensity Projection ◽  
Region Growing ◽  
Ct Images ◽  
Support Vector ◽  
Solitary Pulmonary Nodules ◽  
Computed Tomography Images ◽  
Lung Structure ◽  
Multiple Thresholds

Solitary pulmonary nodules may indicate an early stage of lung cancer. Hence, the early detection of nodules is the most efficient way for saving the lives of patients. The aim of this paper is to present a comprehensive Computer Aided Diagnosis (CADx) framework for detection of the lung nodules in computed tomography images. The four major components of the developed framework are lung segmentation, identification of candidate nodules, classification and visualization. The process starts with segmentation of lung regions from the thorax. Then, inside the segmented lung regions, candidate nodules are identified using an approach based on multiple thresholds followed by morphological opening and 3D region growing algorithm. Finally, a combination of a rule-based procedure and support vector machine classifier (SVM) is utilized to classify the candidate nodules. The proposed CADx method was validated on CT images of 60 patients, containing the total of 211 nodules, selected from the publicly available Lung Image Database Consortium (LIDC) image dataset. Comparing to the other state of the art methods, the proposed framework demonstrated acceptable detection performance (Sensitivity: 0.80; Fp/Scan: 3.9). Furthermore, we visualize a range of anatomical structures including the 3D lung structure and the segmented nodules along with the Maximum Intensity Projection (MIP) volume rendering method that will enable the radiologists to accurately and easily estimate the distance between the lung structures and the nodules which are frequently difficult at best to recognize from CT images.

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