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    Semi-automatic Assessment of Cervical Vertebral Maturation Stages using Cephalograph Images and Centroid-based Clustering

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    KoriÅ”tenjem radiograma istraživala se učinkovitost različitih numeričkih tehnika za poluautomatske procjene stupnja sazrijevanja vratnih kralježaka (CVM). Metode: Kefalogrami 211 pacijenata snimljeni su i spremljeni u digitalnom obliku. Nakon toga su, s pomoću posebno razvijenog softvera i tih pohranjenih radiograma, specijalisti ortodoncije označili i mjerili za svakog pacijenta nekoliko karakterističnih kefalometrijskih obilježja. Rezultati su bili potrebni za automatsko određivanje stupnja sazrijevanja vratnih kralježaka s nekoliko numeričkih tehnika, među kojima K znači klasteriranje (grupiranje), a Fuzzy C ā€“ dusteriranje (rasipanje). Rezultati su uspoređeni s podacima koje su dobili specijalisti. Rezultati: Najbolji rezultati dobiveni su koriÅ”tenjem Fuzzy C rasipanja. Točna ocjena stupnja CVM-a iznosila je oko 70 posto, a procjena klase bila je viÅ”a od 99 posto. Zaključak: Eksperimentalni rezultati pokazuju da se može razviti potpuno automatizirani sustav za procjenu i predviđanje stupnjeva CVM-a, premda joÅ” treba rijeÅ”iti manje teÅ”koće prije primjene u kliničkoj praksi.Introduction: Effectiveness of different numerical techniques for use in semi-automatic assessment of cervical vertebral maturation stages (CVM) using radiograph images was investigated. Methods: Lateral cephalographs of 211 patients were recorded and stored in a digital form. Using the specially developed software application, orthodontic experts marked and measured several characteristic cephalometric parameters for every patient. The results of these measurements were used to automatically determine the cervical vertebral maturation stage using numerical techniques, including the K-means clustering and the Fuzzy C-means clustering. These results were compared with the assessment made manually by the trained orthodontists.Results: The best results were achieved using the modified Fuzzy-C means clustering. Identification of the correct CVM stage was around 70%, while the assessment including the adjacent classes [+/- 1 developmental stage] was over 99%. Conclusions: Experimental results show that it may be possible to develop a fully automated system to assess CVM stages, although there are still minor issues that need to be addressed before the methodā€™s implementation in the clinical practice
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