1134 Muhammad Burhan Khan et al.
Figure 6. a: Original image and (b) result of division of image (a) by average filtered image. c: Original image and (d) result of division of image (b) by average filtered image.
image to be thresholded, m the mean of that image, β a constant, then binary image Ib is determined as
Ib =I ≥βm (1) In our implementation, we have used 0.85 for the value of β.
Watershed-Based Segmentation In this method, the grayscale image is first pre-processed by average filtering, as in the adaptive thresholding-based algorithm, to minimize shade-off. The distance transform of the Otsu thresholded image was used to generate the seed for the watershed algorithm. The algorithm (Meyer, 1994) was subsequently used to segment the flocs and filaments. The preprocessing step appeared to be very important in this method. Better performance of the preprocessing methods results in improved segmentation results. The watershed algorithm has been previously used for
Figure 7. Flow chart of the proposed texture-based segmentation.
3×3 pixels, followed by adaptive histogram equalization for image enhancement. The scaled, histogram-equalized and range filtered image is subtracted from the grayscale image as shown in Figure 7. Subsequently, a scaled global mean thresholding is used for the final binary image. If I is an
complex and porous structure of flocs and PCMartifacts, the filaments are either free, attached to the flocs or overlapped with other filaments or themselves. This makes the PCM images of AS very different and the watershed algorithmalone could not preserve themorphology of flocs and filaments. So, we modified the approach by including average filtering to increase the contrast to the background and lessen the effects
ofPCMartifacts.However, in the case of overlapped filaments
the PCM images of bone marrow stromal cells by Bradhurst et al. (2008) and Debeir et al. (2008).However, those cases, the main purpose was to measure cell confluency and cell growth, without addressing the PCM artifacts. In our work, besides
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