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Full-Text Articles in Medicine and Health Sciences
The Effect Of Glucosamine, Chondroitin And Harpagophytum Procumbens On Femoral Hyaline Cartilage Thickness In Patients With Knee Osteoarthritis– An Mri Versus Ultrasonography Study, Florentin Ananu Vreju, Paulina Lucia Ciurea, Anca Rosu, Beatrice Andreea Chisalau, Cristina Dorina Parvanescu, Sineta Cristina Firulescu, Adina Turcu Stiolica, Andreea Lili Barbulescu, Stefan Cristian Dinescu, Cristiana Iulia Dumitrescu, Roxana Mihaela Dumitrascu, Cristina Criveanu, Lucretiu Radu, Mihai Tusaliu, Daniela Dumitrescu
The Effect Of Glucosamine, Chondroitin And Harpagophytum Procumbens On Femoral Hyaline Cartilage Thickness In Patients With Knee Osteoarthritis– An Mri Versus Ultrasonography Study, Florentin Ananu Vreju, Paulina Lucia Ciurea, Anca Rosu, Beatrice Andreea Chisalau, Cristina Dorina Parvanescu, Sineta Cristina Firulescu, Adina Turcu Stiolica, Andreea Lili Barbulescu, Stefan Cristian Dinescu, Cristiana Iulia Dumitrescu, Roxana Mihaela Dumitrascu, Cristina Criveanu, Lucretiu Radu, Mihai Tusaliu, Daniela Dumitrescu
Journal of Mind and Medical Sciences
Background: the evaluation of cartilage thickness has become possible with new techniques such as musculoskeletal ultrasonography (US) and magnetic resonance imagining (MRI), making the evaluation of the treatment response and the progression of the disease more accurate. Objective: to evaluate the efficacy of a Symptomatic Slow Acting Drug for Osteoarthritis using both US and MRI for measuring cartilage thickness at baseline and after 1 year. Methods: The study included the clinical evaluation of 20 patients at baseline, at 6 and 12 months as well as imaging exams (US and MRI) at baseline and after 1 year. Measurements …
Brain Image Clustering By Wavelet Energy And Cbsso Optimization Algorithm, Hasan Hosseinzadeh, Mohammad Sedaghat
Brain Image Clustering By Wavelet Energy And Cbsso Optimization Algorithm, Hasan Hosseinzadeh, Mohammad Sedaghat
Journal of Mind and Medical Sciences
Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights.
The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed …