How can I visualise and extract 3D data without using isosurface function

I have a model of a tumour that evolves over time, essentially a 4D matrix of cellc concentration c(r,t) size (m,n,p,t). I can do some nice looking things using the built in functions to visualise it and make movies of growth. However I want to be able to extract the isosurface to make some quantitative calculations, or surface area, volume and a measure of the irregularity of the isosurface, essentially a gradient function.

The data is a grid of voxels with a cell concentration c at each voxel. Say I want to look at the surface where c > x. I can extract a matrix with logical indexing by ISO = Tumour > x, which then gives me a 3D matrix. I want to visualise this and calculate the surface area. I can do volume by sum(sum(sum(ISO)))*voxelsize, but visualising the surface without isosurface is stumping me. Also calculating the gradient. I wrote a 2D code:

%%Anisotropy Measure using gradient integral

%A quantitative measure of tumour anisotropy, 

%1/(2*pi*R)*Integraldxdy* absolute value of grad phi 

%discretised version: 

%Sigma(ijk) abs(grad c(r)) =

%sqrt((partialc/partialx)^2+(partialc/partialy)^2+(partialc/partialz))dV

%partialc/partialx = ((Ci+1,j,k - Ci-1,j,k)/2deltax)

%practice code 

RR = rand(10);

dx = 1

for i = 1:8, j = 1:10

      dcdxsq =((RR(i+2,j) - RR(i,j))/2).^2

end

for i=1:10, j=1:8

      dcdysq=((RR(i,j+2) - RR(i,j))/2).^2

end

Aniso = sqrt(sum(sum(dcdxsq)) + sum(sum(dcdysq))) 

figure

surf(RR)

str = sprintf('Random surface with Anisotropy = %f',Aniso);

title(str);

now I want to do this with the 3D data, is there a neat way to do it without counting all the interior points of the surface? Essentially it's still a 2D surface, I just need to extract it neatly.

Suggestions?