1. Load an image from bitmap file and apply canny edge filtering

    Image:io handle all image file parsing. By using << and >> operator, the image parser copy the data to the image data.

#include "tipl/tipl.hpp"

#include <iostream>

int main()

{

    tipl::grayscale_image image_data,label_data;

    tipl::io::bitmap bitmap_file;

    tipl::io::nifti nifti_file;

    if(!bitmap_file.load_from_file("canny_edge_test.bmp"))

    {

        std::cout << "cannot read canny_edge_test.bmp" << std::endl;

        return;

    }

    bitmap_file >> image_data;




    tipl::filter::gaussian(image_data);

    tipl::filter::gaussian(image_data);

    tipl::filter::gaussian(image_data);

    tipl::filter::canny_edge(image_data);

    nifti_file << image_data;

    nifti_file.save_to_file("result_canny_edge.nii");

    return 0;

}

2. Load an image and apply anisotropic diffusion filter

#include "tipl/tipl.hpp"

int main()

{

    tipl::io::bitmap reader;

    if(!reader.load_from_file("images/input_bmp.bmp"))

        return 0;

    tipl::grayscale_image I;

    reader >> I;

    // note that the anisotropic diffusion require a wider dynamic range.

    tipl::image<2,int> I2(I);

    tipl::filter::anisotropic_diffusion(I2);

    tipl::grayscale_image out(I2);

    reader << out;

    reader.save_to_file("images/result_anisotropic.bmp");

}

 

    Note that the syntax in the following example can be applied to both 2D or 3D images.

Mean filter

Gaussian filter

Laplacian filter

Sobel filter

    An edge magnitude filter. The result is the abs(gx) + abs(gy) in 2D image.

Canny Edge Detector

Anisotropic diffusion filter

    Edge preserving smoothing.