# Estimation of the Prostate Volume from Abdominal Ultrasound Images by Image-Patch Voting
## 2 Previous Work  
We first briefly review prostate-segmentation methods on US images, as automated prostate volume estimation is primarily performed using segmentation methods. In general, almost all prostate-segmentation studies in the US modality have been performed on TRUS images. To our knowledge, there had been only one study available [10] on AUS images apart from our previous work [14; 15]. Therefore, this section will also be a review of prostate segmentation on TRUS images. Early work on prostate segmentation began with edge-based methods, which often use filters to extract edges from medical images. However, low SNR values in US images caused broken edges, and these algorithms needed to be supported by texture information. [PERSON] et al. [16] used the Radial Bas-Relief (RBR) technique to outline the prostate border. [PERSON] et al. [17] used the harmonic method, which eliminates noise and encodes a smooth boundary. [PERSON] et al. [18] used the local standard deviation to determine varying homogeneous regions to detect edges. A three-stage method was applied by [PERSON] et al. [19]. To reduce speckle noise, they first applied a stick filter, then the image was smoothed using an anisotropic diffusion filter, and in the third step, preliminary information such as the shape and the echo model were used. A final step was the manual attachment of the edges, integrating patient-specific anatomical information. Deformable models were also used in US prostate-segmentation studies and overcame the broken boundary problems in edge-based methods. Deformable models provide a complete contour of the prostate and try to preserve the shape information by internal forces while being placed in the best position representing the prostate border of the image by external forces. [PERSON] et al. [20] suggested using localized multiscale contour parameterization based on 1D dyadic wavelet transform for elastic deformation constraint to particular object shapes. [PERSON] et al. [21] required manual initialization of four points from the contour. The estimated contour was then automatically deformed to fit the image better. [PERSON] et al. [22] used a 3D deformable model where internal forces were based on local curvature and external forces were based on volumetric data by applying an appropriate edge filter. [PERSON] et al. [23] represented the prostate border using Gabor filter banks to characterize it in a multiscale and multi-orientation fashion. A 3D deformable model was proposed by [PERSON] et al. [24] initialized by considering six manually selected points. Figure 2: The prostate contours in transverse and sagittal MR images. Due to its high SNR values, MR is accepted as the gold standard in medical image-analysis studies of the prostate. A texture matching-based deformable model for 3D TRUS images was proposed by [PERSON] et al. [25]. This method used Gabor Support Vector Machines (G-SVMS) on the model surface to capture texture priors for prostate and non-prostate tissues differentiation. Region-based methods focused on the intensity distributions of the prostate region. Graph-partition algorithms and region-based level sets were used in prostate-segmentation algorithms to overcome the absence of the strong edges problem. A region-based level-set method was used by [PERSON] et al. [26] after a fast-discriminative approach. [PERSON] et al. [27] used a graph partition scheme where the graph was built with nodes and edges. Nodes were the pixels, and horizontal edges that connect these nodes represented edge-discontinuity penalties. In classifier-based methods, a feature vector was created for each object (pixels, regions, etc.). A training set was built by assigning each object a class label with supervision. The classifier was trained with the training set and learned to assign a class label to an unseen object. [PERSON] et al. [28] used Gabor filter banks to extract texture features from registered longitudinal images of the same subject.