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I wanna check that this is what you're looking for before I go on a long explanation of something that could be irrelevant.

I have a series of 2d images of a tumor. I'm constructing a 3d shell from the image slices and creating a .ply file from that shell.

2D slices

3D Reconstruction

Is this the sort of thing that you're looking for?

Edit:

I downloaded the dataset and ran it through the program.

I set the resolution of the image to 100x100 to reduce the number of points in the .ply file. You can increase it or decrease it as you like.

Program for creating the .ply file

I'm putting together the solution in the comments in a more orderly fashion in case someone else stumbles on this question.

Your error:

How are Voxels generated from 2D MRI scans?

Generating voxels from MRI Scans was actually the original motivation for marching cubes. The basic concept is to think of the MRI scans as slices. If these slices lie in the XY plane at different Z values, the pixels of the scans make up the nodes of the cubes. Figure 2 in the white paper shows how this is used to construct the cubes. http://academy.cba.mit.edu/classes/scanning_printing/MarchingCubes.pdf

The lightness/darkness of the pixels is then used to decide whether the node is considered "on" or "off". With a cube of 8 nodes and each node can be either 0 or 1 then there are 2^8 = 256 possible configurations. Through symmetry the authors show that there are really only 15 unique cases that need to be considered.

However, I would point you to this white paper as well that shows that the 15 original cases can actually be handled in different ways leading to 33 different cases: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.7139&rep=rep1&type=pdf

Now on a personal note: having implemented both of these algorithms myself, if you are looking for an algorithm to reconstruct 3d surfaces I would strongly recommend using marching tetrahedrons as the number of cases is much lower and much easier to implement.

I removed some values from your code and create a basic structure. Check whether this is the solution you are looking for

You are setting the center of rotation in pixels and not in physical space.

SimpleITK ( and ITK ) perform transformation and resampling in physical space and not index space. It should be unnecessary to set the origin of you image to 0. I believe the you should use imagetoresize.TransformContinuousIndexToPhysicalPoint(center_index) to obtain the center in physical space.

Google uses a sort of proxy for loading e-mail images so they can cache them. Because you are hosting your application from localhost, the Google proxy cannot reach your image and a broken image is shown in your email.

The reason this works with Yahoo and Zoho is that they link to the image directly and you are probably running the application on the same machine. If you would open these emails on another machine, the links would also be broken.

The solution for this is hosting your images on a server that can be reached by anyone using a domain name.

I am almost sure that there is a bug somewhere in your code around these functions. I tried simple example, following your paradigm, and it works perfectly (PCL 1.8 built from sources, OpenCV 3.1 built from sources, g++ 5.x or g++ 6.x, Ubuntu 16.10):