In this study, a software tool was developed to analyse the medical data collected from laryngeal cancer operations by using two data mining techniques. The software, run on real-world medical data, is a tool that enables medical decisions to be reached by analysing past records from patients. The k-means algorithm, which is a clustering algorithm in data mining, was used to point out the intensities in the data set and to display two dimensions on the charts. The data of three screens that were named as selective clustering, different pre- and post-operation stages and clustering operations based on pre-operation T values, were processed using clustering with the k-means algorithm and one screen, which named relapse and survival percentages, was processed through classifying. It helps the future decision-making process by considering false estimates of pre-operation stages of the cases and by using the information gathered from past cases concerning tumour relapse and the survival percentage for prognostication. The characteristics of laryngeal cancer operations data, that involve causal links, were exposed by using two data mining techniques in this application.
Özetçe Bu çalışmada bütün akciğer bölgesini otomatik olarak bölütlemek için bölgenin özellikleri dikkate alınarak histogram tabanlı k-means algoritması geliştirilmiştir. Bölütleme sonunda akciğer lobları çevre dokulardan ayrı olarak elde edilmiştir. Bu yöntem akciğer bölgesinin bilgisayar destekli çeşitli analiz işlemlerinde ilk adım olarak kullanılabilir. Çalışmada üç farklı tomografi cihazından alınan toplam 34 kesit seti işlenmiştir. Yöntemin sağladığı kolaylıkla normalizasyon işlemi uygulanmadan görüntüler %96 başarı oranıyla bölütlenebilmiştir. Noktalar arası uzaklıkları hesaplamak için geleneksel k-means yöntemindeki kartezyen sistem yerine histogram değerleri kullanılmıştır. Böylece aralarında bağlantı olmayan ve farklı yerlerde olan aynı tür dokuların aynı kümeye dahil olması sağlanmıştır. Histogram tabanlı kmeans algoritması, Fuzzy C-means (FCM) ve optimal eşikleme yöntemleriyle karşılaştırıldığında iterasyon sayısı ve bölütleme doğruluğu açısından daha verimli bulunmuştur.
AbstractIn this study, it was developed an histogram based kmeans algorithm by considering the region features in order to segment whole lung region automatically. After the segmentation the lung lobes are extracted from the surrounding tissue. This method could be used as a first step of various computer aided analysis of lungs. In the study, 34 cases which were scanned by the three different tomography systems, the processed. The method provides 96% segmentation success rate without performing normalization process. The histogram values have been used to calculate the distance between the points instead of using the cartesian system of the traditional k-means method. Thus, similar tissues which are not connected and far from each other, were included in the same set. The histogram based k-means algorithm was compared with Fuzzy C-means (FCM) and optimal thresholding method, found more efficient for the iteration number and segmentation accuracy.
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