palm veins enhancement with OpenCV

Question

I'm trying to implement in OpenCV an algorithm to bring out the details of a palm vein pattern. I've based myself on a paper called "A Contactless Biometric System Using Palm Print and Palm Vein Features" that I've found on the Internet. The part I'm interested in is the chapter 3.2 Pre-processing. The steps involved are shown there.

I'd like to do the implementation using OpenCV but until now I'm stuck hard. Especially they use a Laplacian filter on the response of a low-pass filter to isolate the principal veins but my result gets very noisy, no matter the parameters I try!

Any help would be greatly appreciated!

Answer 1

Ok finally I've figured out by myself how to do it. Here is my code :

#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#define THRESHOLD 150
#define BRIGHT 0.7
#define DARK 0.2

using namespace std;
using namespace cv;

int main()
{

    // Read source image in grayscale mode
    Mat img = imread("roi.png", CV_LOAD_IMAGE_GRAYSCALE);

    // Apply ??? algorithm from https://stackoverflow.com/a/14874992/2501769
    Mat enhanced, float_gray, blur, num, den;
    img.convertTo(float_gray, CV_32F, 1.0/255.0);
    cv::GaussianBlur(float_gray, blur, Size(0,0), 10);
    num = float_gray - blur;
    cv::GaussianBlur(num.mul(num), blur, Size(0,0), 20);
    cv::pow(blur, 0.5, den);
    enhanced = num / den;
    cv::normalize(enhanced, enhanced, 0.0, 255.0, NORM_MINMAX, -1);
    enhanced.convertTo(enhanced, CV_8UC1);

    // Low-pass filter
    Mat gaussian;
    cv::GaussianBlur(enhanced, gaussian, Size(0,0), 3);

    // High-pass filter on computed low-pass image
    Mat laplace;
    Laplacian(gaussian, laplace, CV_32F, 19);
    double lapmin, lapmax;
    minMaxLoc(laplace, &lapmin, &lapmax);
    double scale = 127/ max(-lapmin, lapmax);
    laplace.convertTo(laplace, CV_8U, scale, 128);

    // Thresholding using empirical value of 150 to create a vein mask
    Mat mask;
    cv::threshold(laplace, mask, THRESHOLD, 255, CV_THRESH_BINARY);

    // Clean-up the mask using open morphological operation
    morphologyEx(mask,mask,cv::MORPH_OPEN,
        getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(5,5)));

    // Connect the neighboring areas using close morphological operation
    Mat connected;
    morphologyEx(mask,mask,cv::MORPH_CLOSE,
        getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(11,11)));

    // Blurry the mask for a smoother enhancement
    cv::GaussianBlur(mask, mask, Size(15,15), 0);

    // Blurry a little bit the image as well to remove noise
    cv::GaussianBlur(enhanced, enhanced, Size(3,3), 0);

    // The mask is used to amplify the veins
    Mat result(enhanced);
    ushort new_pixel;
    double coeff;
    for(int i=0;i<mask.rows;i++){
        for(int j=0;j<mask.cols;j++){
            coeff = (1.0-(mask.at<uchar>(i,j)/255.0))*BRIGHT + (1-DARK);
            new_pixel = coeff * enhanced.at<uchar>(i,j);
            result.at<uchar>(i,j) = (new_pixel>255) ? 255 : new_pixel;
        }
    }

    // Show results
    imshow("frame", img);
    waitKey();

    imshow("frame", result);
    waitKey();

    return 0;
}

So the main steps of the paper are followed here. For some parts I've inspired myself on code I've found. It's the case for the first processing I apply that I've found here. Also for the High-pass filter (laplacian) I've inspired myself on the code given in OpenCV 2 Computer Vision Application Programming Cookbook.

Finally I've done some little improvements by allowing to modify the brightness of the background and the darkness of the veins (see defines BRIGHT and DARK). I've also decided to blur a bit the mask to have a more "natural" enhancement.

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Here the results (Source / Paper result / My result) :