Primary high contrast ver... - Yale Image Finder

Figure 4: Primary high contrast version of the smoothed image from Fig. 2.

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Other Images from "A coarse-to-fine approach to prostate boundary segmentation in ultrasound images":

Figure 1 Schematic diagram of the proposed approa...

Figure 4 Primary high contrast version of the smo...

Figure 12 Automatic and manual boundaries are show...

Figure 6 local polar coordinate schema and ellipt...

Figure 11 The result of applying Canny edge detect...

Figure 3 Input membership function for locally ad...

Figure 5 (a): The result of global thresholding o...

Figure 7 Isolated object corresponding to the pro...

Figure 2 Top: Original image, Bottom: Smoothed im...

Figure 9 Membership functions for input gray leve...

Figure 13 A low quality TRUS image and the result ...

Figure 8 the membership function μlocation based ...

Figure 10 Top: Original image. Bottom: Enhanced Im...

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Abstract

BackgroundIn this paper a novel method for prostate segmentation in transrectal ultrasound images is presented.MethodsA segmentation procedure consisting of four main stages is proposed. In the first stage, a locally adaptive contrast enhancement method is used to generate a well-contrasted image. In the second stage, this enhanced image is thresholded to extract an area containing the prostate (or large portions of it). Morphological operators are then applied to obtain a point inside of this area. Afterwards, a Kalman estimator is employed to distinguish the boundary from irrelevant parts (usually caused by shadow) and generate a coarsely segmented version of the prostate. In the third stage, dilation and erosion operators are applied to extract outer and inner boundaries from the coarsely estimated version. Consequently, fuzzy membership functions describing regional and gray-level information are employed to selectively enhance the contrast within the prostate region. In the last stage, the prostate boundary is extracted using strong edges obtained from selectively enhanced image and information from the vicinity of the coarse estimation.ResultsA total average similarity of 98.76%(± 0.68) with gold standards was achieved.ConclusionThe proposed approach represents a robust and accurate approach to prostate segmentation.