Periodontal disease is a kind of typical dental diseases, which affects many adults. The presence of alveolar bone resorption, which can be observed from dental panoramic radiographs, is one of the most important signs of the progression of periodontal disease. Automatically evaluating alveolar-bone resorption is of important clinic meaning in dental radiology. The purpose of this study was to propose a novel system for automated alveolar-bone-resorption evaluation from digital dental panoramic radiographs for the first time. The proposed system enables visualization and quantitative evaluation of alveolar bone resorption degree surrounding the teeth. It has the following procedures: (1) pre-processing for a test image; (2) detection of tooth root apices with Gabor filter and curve fitting for the root apex line; (3) detection of features related with alveolar bone by using image phase congruency map and template matching and curving fitting for the alveolar line; (4) detection of occlusion line with selected Gabor filter; (5) finally, evaluation of the quantitative alveolar-bone-resorption degree in the area surrounding teeth by simply computing the average ratio of the height of the alveolar bone and the height of the teeth. The proposed scheme was applied to 30 patient cases of digital panoramic radiographs, with alveolar bone resorption of different stages. Our initial trial on these test cases indicates that the quantitative evaluation results are correlated with the alveolar-boneresorption degree, although the performance still needs further improvement. Therefore it has potential clinical practicability.
In this paper, we present a texture classification method based on texton learned via sparse representation (SR) with new feature histogram maps in the classification of emphysema. First, an overcomplete dictionary of textons is learned via KSVD learning on every class image patches in the training dataset. In this stage, high-pass filter is introduced to exclude patches in smooth area to speed up the dictionary learning process. Second, 3D joint-SR coefficients and intensity histograms of the test images are used for characterizing regions of interest (ROIs) instead of conventional feature histograms constructed from SR coefficients of the test images over the dictionary. Classification is then performed using a classifier with distance as a histogram dissimilarity measure. Four hundreds and seventy annotated ROIs extracted from 14 test subjects, including 6 paraseptal emphysema (PSE) subjects, 5 centrilobular emphysema (CLE)
subjects and 3 panlobular emphysema (PLE) subjects, are used to evaluate the effectiveness and robustness of the
proposed method. The proposed method is tested on 167 PSE, 240 CLE and 63 PLE ROIs consisting of mild, moderate
and severe pulmonary emphysema. The accuracy of the proposed system is around 74%, 88% and 89% for PSE, CLE
and PLE, respectively.
Measurement of visual quality is of fundamental importance for numerous image and video processing applications. This
paper presented a novel and concise reduced reference (RR) image quality assessment method. Statistics of local binary
pattern (LBP) is introduced as a similarity measure to form a novel RR image quality assessment (IQA) method for the
first time. With this method, first, the test image is decomposed with a multi-scale transform. Second, LBP encoding
maps are extracted for each of subband images. Third, the histograms are extracted from the LBP encoding map to form
the RR features. In this way, image structure primitive information for RR features extraction can be reduced greatly.
Hence, new RR IQA method is formed with only at most 56 RR features. The experimental results on two large scale
IQA databases show that the statistic of LBPs is fairly robust and reliable to RR IQA task. The proposed methods show
strong correlations with subjective quality evaluations.
We aim at using a new texton based texture classification method in the classification of pulmonary emphysema in
computed tomography (CT) images of the lungs. Different from conventional computer-aided diagnosis (CAD)
pulmonary emphysema classification methods, in this paper, firstly, the dictionary of texton is learned via applying
sparse representation(SR) to image patches in the training dataset. Then the SR coefficients of the test images over the
dictionary are used to construct the histograms for texture presentations. Finally, classification is performed by using a
nearest neighbor classifier with a histogram dissimilarity measure as distance. The proposed approach is tested on 3840
annotated regions of interest consisting of normal tissue and mild, moderate and severe pulmonary emphysema of three
subtypes. The performance of the proposed system, with an accuracy of about 88%, is comparably higher than state of
the art method based on the basic rotation invariant local binary pattern histograms and the texture classification method
based on texton learning by k-means, which performs almost the best among other approaches in the literature.
Objective Image Quality Assessment (IQA) model investigation is a hot topic in recent times. This paper proposed a
novel and efficient universal Reduced Reference (RR) image quality assessment method based upon the statistics of edge
discrimination. Firstly, binary edge maps created from the multi-scale wavelet transform modulus maxima were used as
the low level feature to discriminate the difference between the reference and distorted image for IQA purpose. Then the
gradient operator was applied on the binary map to produce the so called edge pattern map. The histogram of edge
pattern map was used to verify the pattern of the edges of reference and distorted image, respectively. The RR features
extracted from the histogram was used to discriminate the difference of edge pattern maps, and then form a new RR IQA
model. Comparing to the typical RR model (Zhou Wang's method, 2005), only 12 features (96 bits) are needed instead
of 18 features (162 bits) in Zhou Wang et al.'s method with better overall performance.
The research on image quality assessment (IQA) has been become a hot topic in most area concerning image processing.
Seeking for the efficient IQA model with the neurophysiology support is naturally the goal people put the efforts to
pursue. In this paper, we argue that comparing the edges position of reference and distorted image can well measure the
image structural distortion and become an efficient IQA metric, while the edge is detected from the primitive structures
of image convolving with LOG filters. The proposed metric is called NSER that has been designed following a simple
logic based on the cosine distance of the primitive structures and two accessible improvements. Validation is taken by
comparison of the well-known state-of-the-art IQA metrics: VIF, MS-SSIM, VSNR over the six IQA databases: LIVE,
TID2008, MICT, IVC, A57, and CSIQ. Experiments show that NSER works stably across all the six databases and
achieves the good performance.
This paper proposed a novel contrast equalization algorithm for display or print ready processing of x-ray chest radiograph based on a multi-scale decomposition and reconstruction architecture. Firstly, using this architecture, the original image is decomposed into multi-scale components. At this stage, three methods are used. The first two methods are based on Gauss convolution filters and the third is based on mean curvature motion equation, which is one of nonlinear partial differential equation (PDE) models. Secondly the components at different scale are weighted using a set of controlled equalization coefficients and then integrated into the display or print ready image to ensure the improvement of the visibility of weakly contrasting details and the contrast between tissues in different area. Preliminary experiments on clinical images in deed testify the superiority of our algorithm. This algorithm can effectively improve contrast of low-contrast-region, increase the detail visibility of the rendered image to CRT or film as the image latitude has adequate broad.
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