The human body consists of various bones with different elemental compositions and densities. The mass attenuation coefficients (μm), effective atomic and electronic cross sections (σa and σe), effective atomic numbers and electron densities (Zeff and Neff), and computed tomography (CT) numbers of the human body’s various bones have been determined for the main gamma rays of 67gallium, 153samarium, 99mtechnetium, 201thallium, 131iodine radionuclides and gamma rays from positron annihilation. To calculate these parameters, MCNPX code, WinXCom, and XMuDat computer programs and also the Auto-Zeff software and interpolation method have been used. The results of the MCNPX code were in good agreement with the WinXCom and XMuDat programs with differences of ≤4.42 %. It was found that the σa, σe, Zeff, and CT number parameters decrease as photon energy increases and the density of bone samples decreases. The μm and Neff values decrease with the increase in photon energy based on the results. Furthermore, they are approximately independent of the bone sample density. Of all the bone types, the σa, σe, Zeff, and CT number values of cortical bone are the greatest ones, while those of spongiosa are the lowest. Observed good agreement reveals that the chosen Monte Carlo code and computer programs could be useful to calculate the photon interaction characteristics of different bone types.
Neuro-muscular and musculoskeletal disorders and injuries highly affect the life style and the motion abilities of an individual. The primary purpose of this work is to develop a systematic method for detection of the level of muscle power declining in musculoskeletal and Neuro-muscular disorders. To this aim, the EMG signals of five skeletal muscles as biceps, deltoid, triceps, tibialis anterior and quadriceps muscles are recorded in three states of isometric contraction (ISO), maximum voluntary contraction (MVC) and dynamic contraction from 22 normal subjects aged between 20 and 30 half of them are male. Totally, 14 combinatory extracted features are analyzed to find which of them or a combinatory set of them are discriminative and selective for muscle force quantification and classification. The neuro-fuzzy system is trained with 70 percent of the recorded EMG cut off windows and then it is employed for classification and modeling purposes. For each muscle the most effective extracted features are found for males and females separately by a reference classifier. In the experiments, after the optimum set of combinatory features is found by a reference classifier, the neuro-fuzzy classifier is validated in comparison to some other well-known classifiers in classification of the recorded EMG signals with the three states of contractions corresponding to the extracted features. Then, different structures of the neuro-fuzzy classifier are also comparatively analyzed to find the optimum structure of the classifier used.
Diabetic retinopathy is one of the most important causes of visual impairment. In this paper, a supervised automatic lesion detection in digital retina images for diagnosis and screening purposes. The aim of this study is to present a supervised approach for exudate detection in fundus images and also to analyze the method to find the optimum structure. Cellular automata model is used as the base for this task. To improve the adaptability and efficiency of the cellular automata, the rules are updating by a learning process to produce the cellular learning automata. Then, the algorithm is transferred to fuzzy domain for the task of digital retina image analysis. Automaton is created with simple and extended Moore neighborhood. Rule selection and rule updating are performed automatically and the score and penalty assignments are applied to the cells toward a segmentation process. To evaluate the proposed method, statistical parameters of sensitivity, specificity and accuracy are used. A comprehensive experiment is then executed comprising two main phases. First all structural parameters of the automaton are optimized in an investigation study and then a comparison is made between the proposed method with six other well-known methods to verify the results. In the best structure the statistical parameters of sensitivity, specificity and accuracy are computed as 96.3%, 98.7% and 96.1% for STARE retina image dataset.
In this paper an Evolutionary-based hybrid thresholding method is presented and implemented on nano-scale light microscopic images. Because of background non-uniform illumination, Segmentation of nano-scale light microscopic images is a hard task in real world, and also fundamental task in image processing. An adaptive and efficient thresholding method based on image spatial correlation histogram and Shanbag entropy is proposed in this paper. Genetic algorithm as a parameter optimizing tool is also employed to fine-tune the parameters and coefficients. The microscopic nano-scale images of rat prostate cancer cells with the spatial resolution of few tens of nanometers and nuclear track images (few tens to few hundred nanometers in spatial resolution) are segmented by the proposed thresholding method and the misclassification error and track detection rate are used as the criteria for evaluation purpose. The results exhibit the efficiency and capability of the proposed method in thresholding the real world image dataset.
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