An Automatic Approach for Bone Tumor Detection from Non-Standard CT Images Un enfoque automático para la detección de tumores óseos a partir de imágenes de CT no estándar


Reis H. C., BAYRAM B.

Ingenieria e Investigacion, vol.43, no.3, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 43 Issue: 3
  • Publication Date: 2023
  • Doi Number: 10.15446/ing.investig.90748
  • Journal Name: Ingenieria e Investigacion
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Fuente Academica Plus, CAB Abstracts, INSPEC, Veterinary Science Database, zbMATH, Directory of Open Access Journals, DIALNET
  • Keywords: 3D, bone tumor, CT, medical image processing, region-growing algorithm
  • Yıldız Technical University Affiliated: Yes

Abstract

Image processing techniques are applied in many fields of science. This study aims to detect tumors in the foot and create 3D mod-els via computed tomography (CT), as well as to produce biometric data. 1 039 CT images were obtained from a server. The pa-rameters used were a collimation of 64 detectors, a scanning thickness of 0,5-3 mm, and a pixel size of 512 x 512, with a radiometric resolution of the 16-bit gray levels. Noise reduction, segmentation, and morphological analysis were performed on CT scans to detect bone tumors. In addition, this study used digital image processing techniques to create a virtual three-dimensional (3D) model of bone tumors. The performance of our proposal was evaluated by analyzing the receptor operating characteristics (ROC). Accord-ing to the results, the sensitivity, specificity, and precision in tumor detection were 0,96, 1, and 0,98%, respectively, with a 0,99% aver-age F-measure. Radiologist reports were used for the sake of comparison. The proposed technique for detecting bone tumors of the foot via CT can help radiologists with its increased precision, sensitivity, specificity, and F-measure. This method could improve the diagnosis of foot and ankle tumors by allowing for the multidirectional quantification of abnormalities.