Computerized Medical Imaging and Graphics
Volume 33, Issue 1 , Pages 72-82, January 2009

Automatic segmentation and recognition of lungs and lesion from CT scans of thorax

  • Manish Kakar

      Affiliations

    • Department of Radiation Biology, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical centre, Oslo, Norway
    • Corresponding Author InformationCorresponding author. Tel.: +47 22781225; fax: +47 22781207.
    • ,
  • Dag Rune Olsen

      Affiliations

    • Department of Radiation Biology, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical centre, Oslo, Norway
    • Department of Medical Physics, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical centre, Oslo, Norway
    • Department of Physics, University of Oslo, Oslo, Norway

Received 12 February 2008; received in revised form 3 October 2008; accepted 30 October 2008.

Abstract 

In this study, a fully automated texture-based segmentation and recognition system for lesion and lungs from CT of thorax is presented. For the segmentation part, we have extracted texture features by Gabor filtering the images, and, then combined these features to segment the target volume by using Fuzzy C Means (FCM) clustering. Since clustering is sensitive to initialization of cluster prototypes, optimal initialization of the cluster prototypes was done by using a Genetic Algorithm.

For the recognition stage, we have used cortex like mechanism for extracting statistical features in addition to shape-based features. The segmented regions showed a high degree of imbalance between positive and negative samples, so we employed over and under sampling for balancing the data. Finally, the balanced and normalized data was subjected to Support Vector Machine (SimpleSVM) for training and testing.

Results reveal an accuracy of delineation to be 94.06%, 94.32% and 89.04% for left lung, right lung and lesion, respectively. Average sensitivity of the SVM classifier was seen to be 89.48%.

Keywords: Texture analysis, Gabor filters, Segmentation, Genetic Algorithm, SVM, Tracking

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PII: S0895-6111(08)00118-3

doi:10.1016/j.compmedimag.2008.10.009

Computerized Medical Imaging and Graphics
Volume 33, Issue 1 , Pages 72-82, January 2009

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