Image Processing method for spiky selection
There are more corners on the borders of your "spiky object", so one approach would be to tune a corner detector for this.

For example, I calculated the determinant of the structure tensor (Mathematica code below) of a distance-transformed image:



Binarizing with hysteresis yields this image, which should be a good starting point for the segmentation algorithm of your choice:



Mathematica code (src is the source image you posted)

At first, i calculate a distance transform of the input image. This creates contrasts over the whole object area (instead of just the border), so the whole object can be detected.

dist = ImageData[DistanceTransform[src]];


Next I prepare the components of the structure tensor. The filter size for the gaussian derivatives if 5, the window size is 20. 

gx = GaussianFilter[dist, 5, {1, 0}];
gy = GaussianFilter[dist, 5, {0, 1}];
gx2 = GaussianFilter[gx^2, 20];
gxy = GaussianFilter[gx*gy, 20];
gy2 = GaussianFilter[gy^2, 20];   


To calculate the corner filter at each pixel, I simply plug these into the symbolic determinant of the structure tensor:

corners = Det[{{dx2, dxy}, {dxy, dy2}}] /. {dx2 -&gt; gx2, dxy -&gt; gxy, dy2 -&gt; gy2};


Which is basically the same as:

corners = gx2 * gy2 - gxy * gxy;


Converting this to an image and scaling it to 0..1 range yields the corner detector image above.

Finally, binarizing it with the right thresholds gives the final, binary image:

MorphologicalBinarize[Image[corners], {0.025, 0.1}]