Prostate segmentation by sparse representation based classification.

Yaozong Gao, Shu Liao, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

14 Citations (Scopus)

Abstract

Accurate segmentation of prostate in CT images is important in image-guided radiotherapy. However, it is difficult to localize the prostate in CT images due to low image contrast, unpredicted motion and large appearance variations across different treatment days. To address these issues, we propose a sparse representation based classification method to accurately segment the prostate. The main contributions of this paper are: (1) A discriminant dictionary learning technique is proposed to overcome the limitation of the traditional Sparse Representation based classifier (SRC). (2) Context features are incorporated into SRC to refine the prostate boundary in an iterative scheme. (3) A residue-based linear regression model is trained to increase the classification performance of SRC and extend it from hard classification to soft classification. To segment the prostate, the new treatment image is first rigidly aligned to the planning image space based on the pelvic bones. Then two sets of location-adaptive SRCs along two coordinate directions are applied on the aligned treatment image to produce a probability map, based on which all previously segmented images of the same patient are rigidly aligned onto the new treatment image and majority voting strategy is further adopted to finally segment the prostate in the new treatment image. The proposed method has been evaluated on a CT dataset consisting of 15 patients and 230 CT images. Promising results have been achieved.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages451-458
Number of pages8
Volume15
EditionPt 3
Publication statusPublished - 2012 Dec 1

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Prostate
Linear Models
Image-Guided Radiotherapy
Pelvic Bones
Therapeutics
Politics
Learning

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Gao, Y., Liao, S., & Shen, D. (2012). Prostate segmentation by sparse representation based classification. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 3 ed., Vol. 15, pp. 451-458)

Prostate segmentation by sparse representation based classification. / Gao, Yaozong; Liao, Shu; Shen, Dinggang.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 15 Pt 3. ed. 2012. p. 451-458.

Research output: Chapter in Book/Report/Conference proceedingChapter

Gao, Y, Liao, S & Shen, D 2012, Prostate segmentation by sparse representation based classification. in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 edn, vol. 15, pp. 451-458.
Gao Y, Liao S, Shen D. Prostate segmentation by sparse representation based classification. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Pt 3 ed. Vol. 15. 2012. p. 451-458
Gao, Yaozong ; Liao, Shu ; Shen, Dinggang. / Prostate segmentation by sparse representation based classification. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 15 Pt 3. ed. 2012. pp. 451-458
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