It's hard for me to tell, but it sounds like you want to automatically detect the mammograms where the outline is incorrect. I'm assuming the desired outline is the perimeter of some convex region in the image.
I'm not sure whether clustering is going to be the ideal approach here. The first approach that comes to mind for me is something like this: we might want to build a classifier $C$ that accepts an image $I$ and a point $(x,y)$ as input and outputs either 0 or 1 according to whether $(x,y)$ is considered inside the region. As features, you might include at least the following:
the grayscale intensity of $I(x,y)$, i.e., the darkness of the pixel at location $(x,y)$ in $I$.
the grayscale intensity of the neighborhood of $(x,y)$ in $I$. This could be computed as the intensity of $I'(x,y)$, where $I'$ is the result of applying a Gaussian blur to $I$.
for each line $\ell$ that's part of the outline you were given for $I$, the value of $f(\ell,(x,y))$, where you define $f(\ell,(x,y))$ to be $1$ or $0$ depending on which side of $\ell$ the point $(x,y)$ is on. For uniformity, you might choose a common orientation where one side of $\ell$ is towards the "inside" of the convex region given by the outline, and then let $f(\ell,(x,y))=1$ if $(x,y)$ is on that side of $\ell$ and let it be $0$ if $(x,y)$ is on the opposite side.
for each line $\ell'$ that's part of the edges you detected by applying an edge detector to $I$, the value of $f(\ell,(x,y))$.
Given these features, or some other suitable features of your choice, you might try to build a classifier $C$ by training on thousands of mammograms where you have ground truth (where you know what the correct outline is); for each such mammogram image $I$, you might pick a few hundred points $(x,y)$ at random, and then train on the resulting hundreds of thousands of inputs to $C$. Since you know ground truth, you'll know what the desired/correct value for $C(I,(x,y))$ is, so you can apply supervised learning to try to infer a classifier $C$.
Once you've built a classifier $C$, you might then apply it to every new mammogram in your test set to try to infer the correct outline.
I don't know whether this will be effective in practice, nor whether it solves the problem you want to solve, but this is the first thing that springs to mind for me.