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We investigated the association between the textural features obtained from F-FDG images, metabolic parameters (SUVmax SUVmean, MTV, TLG), and tumor histopathological characteristics (stage and Ki-67 proliferation index) in non-small cell lung cancer (NSCLC). The FDG-PET images of 67 patients with NSCLC were evaluated. MATLAB technical computing language was employed in the extraction of 137 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and Laws' texture filters. Textural features and metabolic parameters were statistically analyzed in terms of good discrimination power between tumor stages, and selected features/parameters were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). We showed that one textural feature (gray-level nonuniformity, GLN) obtained using GLRLM approach and nine textural features using Laws' approach were successful in discriminating all tumor stages, unlike metabolic parameters. There were significant correlations between Ki-67 index and some of the textural features computed using Laws' method (r = 0.6, p = 0.013). In terms of automatic classification of tumor stage, the accuracy was approximately 84% with k-NN classifier (k = 3) and SVM, using selected five features. Texture analysis of FDG-PET images has a potential to be an objective tool to assess tumor histopathological characteristics. The textural features obtained using Laws' approach could be useful in the discrimination of tumor stage.

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ÖzetçeKaraciğer sirozu, karaciğer hücrelerinin ölmeye ve bunlardan işlevsel düğüm biçiminde dokular oluşmaya bağladığında ortaya çıkar. Fibroz tespitinde iğne biyopsisi altın standarttır. Bu teknik, doğru tanıya ulaşma açısından iyi bir teknik olmasına rağmen, invazif bir yöntem olması dezavantaj oluşturur. Tıbbi görüntü işleme ve yapay zeka tekniklerindeki gelişmeler, karaciğer dokularının sınıflandırılması için bilgisayar destekli tanı sistemlerinin kullanılabilme potansiyelini artırmıştır. Bu çalışmada görüntü analizini kullanarak sirozun tanısında yardımcı olacak bir takım objektif ölçüler üretmeyi amaçladık. Sirozlu ve sağlıklı parankima doku bölgesini ayırt etmek için, karaciğer bilgisayarlı tomografi (BT) görüntülerinin renk düzeyi tekrar oluş matrisi (Gray Level Cooccurrence Matrix, GLCM) den hesaplanan ikinci dereceden doku (texture) özellikleri ve birinci dereceden istatistiki doku özelliklerini kullandık. Ardından elde edilen tüm bu özellikler kullanılarak destek vektör makineleri (DVM) ile sağlıklı ve sirozlu kişilerin karaciğer BT görüntüleri sınıflandırılmıştır. 10 kat çapraz geçerlilik yöntemi ile elde edilen en yüksek sınıflandırma başarısı %85.19 olarak hesaplanmıştır. AbstractLiver with cirrhosis emerges when the cells of liver begin to die and the tissues become a functional knot from these. In the diagnosis of fibrosis, the needle biopsy is a golden standard. Although this technique is a good techique in reaching accurate diagnosis, its being an invasive method arises disadvantage. The developments in medical image processing and artificial intelligence techniques have advanced the potential of using diagnosis system in classification of liver tissues. In this study, we have aimed at producing some objective measures using image analysis, which will be of assistance in the diagnosis of cirrhosis. In order to differentiate between regions of liver with cirrhosis and healthy parenchymal tissues, we have used first order statistical texture features and second order texture features computed from gray level cooccurrence matrix of liver computerized tomography (CT) images. Then liver CT images of healthy people and people with cirrhosis have been classified with support vector machines (SVM) by using all these acquired features. The most successful classification has been calculated as 85.19% with the method of 10 fold crossvalidation.