H. Sarabi Sarvarani; F. Abdali-Mohammadi
Abstract
Bone age assessment is a method that is constantly used for investigating growth abnormalities, endocrine gland treatment, and pediatric syndromes. Since the advent of digital imaging, for several decades the bone age assessment has been performed by visually examining the ossification of the left hand, ...
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Bone age assessment is a method that is constantly used for investigating growth abnormalities, endocrine gland treatment, and pediatric syndromes. Since the advent of digital imaging, for several decades the bone age assessment has been performed by visually examining the ossification of the left hand, usually using the G&P reference method. However, the subjective nature of hand-craft methods, the large number of ossification centers in the hand, and the huge changes in ossification stages lead to some difficulties in the evaluation of the bone age. Therefore, many efforts were made to develop image processing methods. These methods automatically extract the main features of the bone formation stages to effectively and more accurately assess the bone age. In this paper, a new fully automatic method is proposed to reduce the errors of subjective methods and improve the automatic methods of age estimation. This model was applied to 1400 radiographs of healthy children from 0 to 18 years of age and gathered from 4 continents. This method starts with the extraction of all regions of the hand, the five fingers and the wrist, and independently calculates the age of each region through examination of the joints and growth regions associated with these regions by CNN networks; It ends with the final age assessment through an ensemble of CNNs. The results indicated that the proposed method has an average assessment accuracy of 81% and has a better performance in comparison to the commercial system that is currently in use.
M. Sepahvand; F. Abdali-Mohammadi
Abstract
The success of handwriting recognition methods based on digitizer-pen signal processing is mostly dependent on the defined features. Strong and discriminating feature descriptors can play the main role in improving the accuracy of pattern recognition. Moreover, most recognition studies utilize local ...
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The success of handwriting recognition methods based on digitizer-pen signal processing is mostly dependent on the defined features. Strong and discriminating feature descriptors can play the main role in improving the accuracy of pattern recognition. Moreover, most recognition studies utilize local features or sequences of them. Whereas, it has been shown that the combination of global and local features can increase the recognition accuracy. This paper addresses two mentioned topics. First, a new high discriminative local feature, called Rotation Invariant Histogram of Degrees (RIHoD), is proposed for online digitizer-pen handwriting signals. Second, a feature representation layer is proposed, which maps local features into global ones in a new space using some learning kernels. Different aspects of the proposed local feature and learned global feature are analyzed and its efficiency is evaluated in several online handwriting recognition scenarios.
H.5. Image Processing and Computer Vision
F. Abdali-Mohammadi; A. Poorshamam
Abstract
Accurately detection of retinal landmarks, like optic disc, is an important step in the computer aided diagnosis frameworks. This paper presents an efficient method for automatic detection of the optic disc’s center and estimating its boundary. The center and initial diameter of optic disc are ...
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Accurately detection of retinal landmarks, like optic disc, is an important step in the computer aided diagnosis frameworks. This paper presents an efficient method for automatic detection of the optic disc’s center and estimating its boundary. The center and initial diameter of optic disc are estimated by employing an ANN classifier. The ANN classifier employs visual features of vessels and their background tissue to classify extracted main vessels of retina into two groups: the vessels inside the optic disc and the vessels outside the optic disc. To this end, average intensity values and standard deviation of RGB channels, average width and orientation of the vessels and density of the detected vessels their junction points in a window around each central pixel of main vessels are employed. The center of detected vessels, which are belonging to the inside of the optic disc region, is adopted as the optic disc center and the average length of them in vertical and horizontal directions is selected as initial diameter of the optic disc circle. Then exact boundary of the optic disc is extracted using radial analysis of the initial circle. The performance of the proposed method is measured on the publicly available DRIONS, DRIVE and DIARETDB1 databases and compared with several state-of-the-art methods. The proposed method shows much higher mean overlap (70.6%) in the same range of detection accuracy (97.7%) and center distance (12 pixels). The average sensitivity and predictive values of the proposed optic disc detection method are 80.3% and 84.6% respectively.
