List of Articles radiography

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  1 - A Review of Physiological Structures and Dental Disorders of Canids Using Radiography and Computed Tomography
  Ferdos Fekri Amir Zakian Mohsen  Abbasi Omid Zehtabvar Alireza Vajhi
  Canids are heterodont and diphyodont animals, which most of adult canids have 42 permanent teeth. Nowadays, attention to the oral and dental disorders in pets has gained a special region in veterinary medicine and on the top of these problems can be referred to dental i More

  Canids are heterodont and diphyodont animals, which most of adult canids have 42 permanent teeth. Nowadays, attention to the oral and dental disorders in pets has gained a special region in veterinary medicine and on the top of these problems can be referred to dental infections and other periodontal diseases. Anatomy and positioning of teeth and periodontal diseases also could be observed by diagnostic imaging techniques included radiography and CT-scan. Radiography is an effective method for dental examination with low-cost and fast execution times and can be performed during surgery. Although, computed tomography is high contrasted method in oral cavity examination that facilitates the diagnosis of multiple disease. Precise and detailed imaging of the region of interest even before changes become clinically apparent, the reduced examination time than oral radiography, 3D imaging in different views, but expensiveness and risks associated with anesthesia may be the restrictive factors. Manuscript profile
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  2 - Improving Age Estimation of Dental Panoramic Images Based on Image Contrast Correction by Spatial Entropy Method
  Masoume Mohseni Hussain Montazery Kordy Mehdi Ezoji
  In forensic dentistry, age is estimated using dental radiographs. Our goal is to automate these steps using image processing and pattern recognition techniques. With a dental radiograph, the contour is extracted and features such as apex, width and tooth length are dete More

  In forensic dentistry, age is estimated using dental radiographs. Our goal is to automate these steps using image processing and pattern recognition techniques. With a dental radiograph, the contour is extracted and features such as apex, width and tooth length are determined, which are used to estimate age. Optimizing the resolution of radiographic images is an important step in contour extraction and age estimation. In this article, the aim is to improve the image resolution in order to extract the appropriate area and proper segmentation of the tooth, which makes it possible to estimate age better. In this model, due to the low resolution of radiographic images, in order to increase the accuracy of extracting the desired area of each tooth (ROI), the image resolution increases using spatial entropy based on the spatial distribution of pixel brightness, along with another increasing resolution method, like the Laplacian pyramids. Increasing the resolution of the image leads to the extraction of appropriate ROI and the removal of unwanted areas. The database used in this study is 154 adolescent panoramic radiographs, of which 73 are male and 81 are female. This database is prepared from Babol University of Medical Sciences. The results show that by using fixed tooth segmentation methods and only by applying the proposed effective method to improve image resolution, the extraction of appropriate ROI increased from 66% to 78% which shows a good improvement. The extracted ROI is then delivered to the segmented block and the contour extracted. After contour extraction, age is estimated. The age estimation using the proposed method is closer to the manual age estimate compared to the method that does not use the proposed algorithm to increase the image resolution. Manuscript profile
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  3 - Automatic Lung Diseases Identification using Discrete Cosine Transform-based Features in Radiography Images
  Shamim Yousefi Samad Najjar-Ghabel
  The use of raw radiography results in lung disease identification has not acceptable performance. Machine learning can help identify diseases more accurately. Extensive studies were performed in classical and deep learning-based disease identification, but these methods More

  The use of raw radiography results in lung disease identification has not acceptable performance. Machine learning can help identify diseases more accurately. Extensive studies were performed in classical and deep learning-based disease identification, but these methods do not have acceptable accuracy and efficiency or require high learning data. In this paper, a new method is presented for automatic interstitial lung disease identification on radiography images to address these challenges. In the first step, patient information is removed from the images; the remaining pixels are standardized for more precise processing. In the second step, the reliability of the proposed method is improved by Radon transform, extra data is removed using the Top-hat filter, and the detection rate is increased by Discrete Wavelet Transform and Discrete Cosine Transform. Then, the number of final features is reduced with Locality Sensitive Discriminant Analysis. The processed images are divided into learning and test categories in the third step to create different models using learning data. Finally, the best model is selected using test data. Simulation results on the NIH dataset show that the decision tree provides the most accurate model by improving the harmonic mean of sensitivity and accuracy by up to 1.09times compared to similar approaches. Manuscript profile