Computer-aided detection of lung nodules using outer surface features

DEMİR Ö., ÇAMURCU A. Y.

BIO-MEDICAL MATERIALS AND ENGINEERING, cilt.26, 2015 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26
  • Basım Tarihi: 2015
  • Doi Numarası: 10.3233/bme-151418
  • Dergi Adı: BIO-MEDICAL MATERIALS AND ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Lung nodule detection, CAD systems, texture features, medical image processing, classification, IMAGE DATABASE CONSORTIUM, AUTOMATIC DETECTION, PULMONARY NODULES, CT SCANS, SEGMENTATION, ALGORITHM, CLASSIFICATION, LIDC
  • Marmara Üniversitesi Adresli: Evet

Özet

In this study, a computer-aided detection (CAD) system was developed for the detection of lung nodules in computed tomography images. The CAD system consists of four phases, including two-dimensional and three-dimensional preprocessing phases. In the feature extraction phase, four different groups of features are extracted from volume of interests: morphological features, statistical and histogram features, statistical and histogram features of outer surface, and texture features of outer surface. The support vector machine algorithm is optimized using particle swarm optimization for classification. The CAD system provides 97.37% sensitivity, 86.38% selectivity, 88.97% accuracy and 2.7 false positive per scan using three groups of classification features. After the inclusion of outer surface texture features, classification results of the CAD system reaches 98.03% sensitivity, 87.71% selectivity, 90.12% accuracy and 2.45 false positive per scan. Experimental results demonstrate that outer surface texture features of nodule candidates are useful to increase sensitivity and decrease the number of false positives in the detection of lung nodules in computed tomography images.