Akademik Veri Yönetim Sistemi
Journal of Cancer Research and Therapeutics, cilt.12, sa.2, ss.787-792, 2016 (SCI-Expanded)
Aims: The aim of this study is to develop a computer-aided diagnosis system for bone scintigraphy scans. (CADBOSS). CADBOSS can detect metastases with a high success rates. The primary purpose of CADBOSS is as supplementary software to facilitate physician's decision making. Materials and Methods: CADBOSS consists of various elements, such as hotspot segmentation, feature extraction/selection and classification. A level set active contour segmentation algorithm was used for the detection of hotspots. Moreover, a novel image gridding method was proposed for feature extraction of metastatic regions. An artificial neural network classifier was used to determine whether metastases were present. Performance evaluation of CADBOSS was performed with the help of an image database which included 130 images. (30 non-metastases and 100 metastases) collected from 60 volunteers. All images were obtained within approximately 3 hours after injecting a small amount of radioactive material 99mTc-MDP into the patients and then carrying out scanning with a gamma camera. The 10-fold cross-validation technique was used for all tests. Results: CADBOSS could correctly identify in 120 out of 130 images. Thus, the accuracy, sensitivity, and specificity of CADBOSS were 92.30%, 94%, and 86.67%, respectively. Moreover, CADBOSS increased physician's success in detecting metastases from 95.38% to 96.9%. Conclusions: Detailed experiments showed that CADBOSS outperforms state-of-the-art computer-aided diagnosis. (CAD) systems and reasonably improves physician' diagnostic success.