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Sophisticated imaging techniques developed since Ruth's death now can pinpoint an abnormality's location and help distinguish a tumor from an abscess and other benign conditions.
In this stage, we produce a rough estimate of the abnormality's position.
To overcome this limitation, our method uses relationships between the triplets of supporters and the abnormality's position, when voting for the target position.
When a medical doctor unskilled in WCE identifies an abnormality on an image in a training system, ground truth data, which include the period between the abnormality's appearance and disappearance, as well as the abnormality's type and location, are needed to decide whether the identification is correct.
The estimated position of the abnormality in the current frame, ( μ x, μ y ), is computed using the affine transformation of the previous abnormality's position (t x,t y ), i.e., left[begin{array}{c} mu_{x} mu_{y}1 end{array} right] = mathbf{A_{text{textit{s,t,u}}}} left[ begin{array}{c} t_{x} t_{y} 1end{array} right].
The voting scores C x,y) are calculated by multiplying the three matching scores B c f and the weighting function f x,y), which is defined by the distance from the abnormality's position, estimated from the triplet supporter pairs that are stored in the selected supporter pair set G t.
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