Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

P. Korfiatis, C. Kalogeropoulou, D. Daoussis, T. Petsas, A. Adonopoulos, L. Costaridou

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. The proposed segmentation scheme is based on supervised texture classification between lung tissue (normal and abnormal) and surrounding tissue (pleura and thoracic wall) in the lung border region. This region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924±0.021, and for shape differentiation mean, rms and maximum distance were 1.663±0.816, 2.334±1.574 and 8.0515±6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

Original languageEnglish (US)
Article numberP07013
JournalJournal of Instrumentation
Volume4
Issue number7
DOIs
StatePublished - Nov 16 2009

Keywords

  • Computerized Tomography (CT) and Computed Radiography (CR)
  • Data processing methods
  • Pattern recognition, cluster finding, calibration and fitting methods

ASJC Scopus subject areas

  • Instrumentation
  • Mathematical Physics

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