Prostate segmentation by sparse representation based classification

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.

Fig. 1

(a) and (b) are two axial slices from different treatment images of the same patient. Blue contours are the prostate contours manually delineated by experts. (c) is an illustration of context locations of the center pixel by red points.

Fig. 2

The first, second and third column represents the results of the first, second and third classification iteration, respectively

Fig. 3

Left-top figure shows the DICE measures of our method. Right-top, left-bottom and right-bottom figures are centroid distances in lateral (x), anterior-posterior (y) and superior-inferior (z) directions, respectively.

Fig. 4

Comparison of the segmentation results between linear regression and residual norm comparison. Blue contours are the prostate boundaries manually delineated by experts. Red and green contours are the segmentation results of the proposed method with linear regression and residual norm comparison, respectively. This indicates that our proposed method with linear regression achieves better results, especially in the beginning and ending slices of the prostate.