Biomedical Engineering Reference
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wrongly classified as spongy bone region, then this pixel will be eliminated; in the
contrary, if the pixel should belong to spongy bone region, but has been wrongly
classified as soft-tissue region, then the pixel will be restored. The analysis is
based on extending eight directions of the pixel until it 'touches' the edge pixels,
if all direction of the pixel 'touches' the edges, then the pixel is considered as a
bounded pixel within the bone and hence it is restored. In contrast, if at least one
direction of the pixel does not 'touch' the edge pixel after extending, then the pixel
is considered as a non-bounded pixel within the bone and hence it is eliminated.
The resultant image of Fig. 3.20 a, b after BARNAE are shown in Fig. 3.28 a, b,
respectively.
3.6 Summary
In previous chapter, the weaknesses of conventional segmentation methods have
been identified. This concludes the desired segmentation criteria in order to guide
the mechanism of the proposed framework of segmentation. The segmentation is
performed to partition the hand bone from its background and soft-tissue region
in the beginning of this chapter. The challenges of hand bone segmentation is the
overlapping intensity between the soft-tissue region and the spongy bone region
within the hand bone. A segmentation framework consisting of three main mod-
ules has been proposed and implemented to solve the problem: pre-processing,
ACR in fuzzy quadruple division framework and quality assurance process. Each
of them plays equally important role in tackling the challenge. Pre-processing
consists of two main components: histogram equalization and anisotropic diffu-
sion. The proposed histogram equalization, MBOBHE is specially customized to
protrude the features of the hand bone and to curb the problem of uneven illumi-
nation of the radiograph to prepare a radiograph invariant of illumination for the
subsequent processings of ACR segmentation. Besides, the anisotropic diffusion
Fig. 3.28 The resultant
blocks after BARNAE
 
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