Purpose Quantification of teeth is of clinical importance for various computer assisted procedures such as dental implant, orthodontic planning, face, jaw and cosmetic surgeries. In this regard, segmentation is a major step. Methods In this paper, we propose a method for segmentation of teeth in volumetric computed tomography (CT) data using panoramic re-sampling of the dataset in the coronal view and variational level set. The proposed method consists of five steps as follows: first, we extract a mask in a CT images using Otsu thresholding. Second, the teeth are segmented from other bony tissues by utilizing anatomical knowledge of teeth in the jaws. Third, the proposed method is followed by estimating the arc of the upper and lower jaws and panoramic re-sampling of the dataset. Separation of upper and lower jaws and initial segmentation of M. Hosntalab (B) · A. Abbaspour Tehrani-Fard teeth are performed by employing the horizontal and vertical projections of the panoramic dataset, respectively. Based the above mentioned procedures an initial mask for each tooth is obtained. Finally, we utilize the initial mask of teeth and apply a Variational level set to refine initial teeth boundaries to final contours. Results The proposed algorithm was evaluated in the presence of 30 multi-slice CT datasets including 3,600 images. Experimental results reveal the effectiveness of the proposed method. Conclusion In the proposed algorithm, the variational level set technique was utilized to trace the contour of the teeth. In view of the fact that, this technique is based on the characteristic of the overall region of the teeth image, it is possible to extract a very smooth and accurate tooth contour using this technique. In the presence of the available datasets, the proposed technique was successful in teeth segmentation compared to previous techniques.

We provided an integrated solution for teeth classification in multi-slice CT datasets. In this regard, suggested segmentation technique was successful to separate teeth from each other. The employed WFD approach was successful to discriminate and numbering of the teeth in the presence of missing teeth. The solution is independent of anatomical information such as knowing the sequence of teeth and the location of each tooth in the jaw.

Various methods, such as those developed by the Medical Internal Radiation Dosimetry (MIRD) Committee of the Society of Nuclear Medicine or employing dose point kernels, have been applied to the radiation dosimetry of (131)I radionuclide therapy. However, studies have not shown a strong relationship between tumour absorbed dose and its overall therapeutic response, probably due in part to inaccuracies in activity and dose estimation. In the current study, the GATE Monte Carlo computer code was used to facilitate voxel-level radiation dosimetry for organ activities measured in an (131)I-treated thyroid cancer patient. This approach allows incorporation of the size, shape and composition of organs (in the current study, in the Zubal anthropomorphic phantom) and intra-organ and intra-tumour inhomogeneities in the activity distributions. The total activities of the tumours and their heterogeneous distributions were measured from the SPECT images to calculate the dose maps. For investigating the effect of activity distribution on dose distribution, a hypothetical homogeneous distribution of the same total activity was considered in the tumours. It was observed that the tumour mean absorbed dose rates per unit cumulated activity were 0.65E-5 and 0.61E-5 mGY MBq(-1) s(-1) for the uniform and non-uniform distributions in the tumour, respectively, which do not differ considerably. However, the dose-volume histograms (DVH) show that the tumour non-uniform activity distribution decreases the absorbed dose to portions of the tumour volume. In such a case, it can be misleading to quote the mean or maximum absorbed dose, because overall response is likely limited by the tumour volume that receives low (i.e. non-cytocidal) doses. Three-dimensional radiation dosimetry, and calculation of tumour DVHs, may lead to the derivation of clinically reliable dose-response relationships and therefore may ultimately improve treatment planning as well as response assessment for radionuclide therapy.

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an automated dental identification system (ADIS) for human identification in forensic dentistry requires automatic recognition of teeth in dental images. In this paper, we propose a multi-stage technique to classify teeth in multislice CT (MSCT) images. The proposed algorithm consists of the following three stages: segmentation, feature extraction and classification. We segmented the teeth based on our previous experiences. In the feature extraction stage, we introduced a multi-resolution method using wavelet-Fourier descriptor (WFD). Finally, we utilized WFD coefficients as feature vectors for classification in the third stage. Teeth classification is performed by a conventional supervised classifier for teeth identification. Experimental results reveal the effectiveness of the proposed method.

Coronary artery disease (CAD) causes oscillations in peripheral arteries. Oscillations of the walls of the brachial arteries of 51 patients were recorded [together with the electrocardiogram (ECG)] by an accelerometer at different cuff pressures. By analyzing the energy of the oscillations in the 30-250 Hz band, 16 of 22 patients with CAD and 26 of 29 non-CAD subjects were classified correctly, independent of the ECG, and with no effect of heart murmurs.

Gill chloride cells and prolactin hormone are of high importance in the adaptation of euryhaline fish. Guldenstati (Chalcalburnus chalcoides, 1772), an adromous fish, migrates from the Caspian Sea to rivers to have a more successful reproduction. The present study was aimed to evaluate the changes in the number and size of C. chalcoides gill chloride cells as well as to determine the relationship of its plasma prolactin with water salinity. Eighty-four individual C. chalcoides were collected from river (Lale Roud; 0.4 ppt), Lale Roud estuary (3.75 ppt), and Caspian Sea (9.71 ppt). The sampling was lasted for a-12 month period in 2014. The highest (1349 ± 152) and lowest (881 ± 37) number of gill chloride cells were observed in the animals collected from the Caspian Sea and in the river (Lale Roud), respectively. However, plasma prolactin demonstrated the highest level in C. chalcoides caught from the river (0.89 ± 0.02 ng ml-1), but the lowest amount (0.70 ± 0.03 ng ml-1) in the ones collected from the Caspian Sea.

Liver-shape analysis and quantification is still an open research subject. Quantitative assessment of the liver is of clinical importance in various procedures such as diagnosis, treatment planning, and monitoring. Liver-shape classification is of clinical importance for corresponding intra-subject and inter-subject studies. In this research, we propose a novel technique for the liver-shape classification based on Spherical Harmonics (SH) coefficients. The proposed liver-shape classification algorithm consists of the following steps: (a) Preprocessing, including mesh generation and simplification, point-set matching, and surface to template alignment; (b) Liver-shape parameterization, including surface normalization, SH expansion followed by parameter space registration; (c) Feature selection and classification, including frequency based feature selection, feature space reduction by Principal Component Analysis (PCA), and classification. The above multi-step approach is novel in the sense that registration and feature selection for liver-shape classification is proposed and implemented and validated for the normal and diseases liver in the SH domain. Various groups of SH features after applying conventional PCA and/or ordered by p-value PCA are employed in two classifiers including Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) in the presence of 101 liver data sets. Results show that the proposed specific features combined with classifiers outperform existing liver-shape classification techniques that employ liver surface information in the spatial domain. In the available data sets, the proposed method can successful classify normal and diseased livers with a correct classification rate of above 90 %. The performed result in average is higher than conventional liver-shape classification method. Several standard metrics such as Leave-one-out cross-validation and Receiver Operating Characteristic (ROC) analysis are employed in the experiments and confirm the effectiveness of the proposed liver-shape classification with respect to conventional techniques.