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Here the top, bottom, right and left edges of the bounding box are precisely fitted to cover entire tumor accurately. The locations of the four edges of the bounding box are fitted by the following the procedure below.
Fitting of Top edge: The Precise location of the top edge of crudely detected sub-region is searched from the bottom edge of the sub-region in vertical upward direction. Bhattacharya Coefficient at each search location is noted down. The segmented region with the top edge precisely fitted is shown in the Fig.
Fitting of Bottom edge: The Precise location of bottom edge of the rectangular sub- region is searched from fitted top edge of the sub-region in vertical downward direction. The segmented region with the bottom edge precisely fitted is shown in the Fig.
Fitting of Right edge: The Precise location of the right edge of rectangular sub-region is searched from the left edge of the sub-region in horizontal right direction. The segmented region with the right edge precisely fitted is shown in the Fig. Fitting of Left edge: The Precise location of the left edge of rectangular sub-region is searched from the fitted right edge of the sub-region in horizontal left direction.
The segmented region with the left edge precisely fitted is shown in the Fig. Crudely Segmented tumor Fixing of top edge Fixing of bottom edge Fixing of right edge Fixing of left edge a b-i b-ii b-iii b-iv c-i c-ii c-iii c-iv Fig.
Locating of top, bottom, right and left edges of the crudely segmented sub-region: Case-3 Tumor from left to right. Outputs of Crude and Precise Segmentation for the three cases Fig. It is observed that the output of Crude Segmentation is a sub-region that does not contain tumor entirely and exactly.
Whereas, the output sub-region of Precise detection contains tumor entirely and exactly. Segmentation of Tumor Regions. Hence, it is essential that, the bounding box should localize the tumor, entirely and exactly.
It is observed from the Fig. Whereas, that in our algorithm, Yellow Bounding Box, in Fig. Detected Tumor Regions by Bounding Box. Performance of Detection Algorithms To quantify the performance of our algorithm we use Dice coefficient  given by equation 2: The modulus sign appearing in the Dice coefficient expression denotes cardinality number of pixels in this case of a set.
The closer the Dice coefficient is to unity, the better the segmentation is. Performance of Detection Algorithms 4. The locations of top, bottom, right and left edges of the crudely segmented tumor sub-region as shown in Fig.
The dots in the each plot show the positions of edges placed by an expert radiologist. It can be observed from the plots of Fig. Crudely Segmented tumor Fixing of top edge Fixing of bottom edge Fixing of right edge Fixing of left edge a b-i b-ii b-iii b-iv c-i c-ii c-iii c-iv d-i d-ii d-iii d-iv e-i e-ii e-iii e-iv Fig 8: Fixing of top, bottom, right and left edges of the crudely segmented sub-region: Case-1 Tumor from left to right.
Conclusion The localization of brain tumors by a bounding box using the proposed approach is more precise than that of other histogram-based bounding box techniques. The brain tumors are circumscribed by bounding box entirely and exactly.Change detection Essay 10/14/ Change Detection Basic Question Yes, it was harder for me to detect a change in the picture when it flickered because it was harder for me to maintain focus on the change I was supposed to be looking for.
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