Implementing Regional Growing Segmentation in Python using PyQt and C++ with Qt source code.

Region growing is a popular image segmentation technique that is used to partition an image into regions or objects based on certain predefined criteria. This technique is commonly used in medical imaging, remote sensing, and computer vision applications.

In this tutorial, we will be implementing a CT region growing segmentation algorithm using Python with PyQt for the GUI and C++ with Qt for the backend processing. We will be using the PyQt and Qt libraries to create a user-friendly interface for loading CT images, setting region growing parameters, and visualizing the segmentation results.

Before we get started, you will need to have the following libraries installed on your system:

• Python 3
• PyQt5
• OpenCV
• NumPy
• C++ compiler (such as g++)
• Qt5

Step 1: Setup the project structure

Create a new directory for your project and create the following subdirectories inside it:

• src: This directory will contain the C++ source files for the region growing algorithm.
• gui: This directory will contain the Python source files for the PyQt GUI.
• images: This directory will contain the CT images for testing the segmentation algorithm.

Step 2: Implement the C++ region growing algorithm

Create a new C++ source file in the src directory and implement the region growing algorithm. Here is a simple implementation of the region growing algorithm:

#include <iostream>
#include <vector>
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>

void regionGrowing(cv::Mat& image, cv::Mat& segmentedImage, int seedX, int seedY, double threshold)
{
    std::vector<cv::Point> stack;
    stack.push_back(cv::Point(seedX, seedY));

    while (!stack.empty())
    {
        cv::Point currentPoint = stack.back();
        stack.pop_back();

        if (segmentedImage.at<uchar>(currentPoint) != 0)
            continue;

        double diff = std::abs(image.at<uchar>(currentPoint) - image.at<uchar>(seedX, seedY));

        if (diff <= threshold)
        {
            segmentedImage.at<uchar>(currentPoint) = 255;

            for (int i = -1; i <= 1; i++)
            {
                for (int j = -1; j <= 1; j++)
                {
                    int x = currentPoint.x + i;
                    int y = currentPoint.y + j;

                    if (x >= 0 && x < image.cols && y >= 0 && y< image.rows)
                    {
                        stack.push_back(cv::Point(x, y));
                    }
                }
            }
        }
    }
}

Step 3: Implement the PyQt GUI

Create a new Python source file in the gui directory and implement the PyQt GUI. Here is a simple implementation of the GUI:

import sys
import cv2
import numpy as np
from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QVBoxLayout, QPushButton, QSlider, QFileDialog
from PyQt5.QtGui import QPixmap

class RegionGrowingSegmentation(QMainWindow):
    def __init__(self):
        super().__init__()

        self.image = None
        self.segmentedImage = None

        self.initUI()

    def initUI(self):
        layout = QVBoxLayout()

        self.label = QLabel()
        layout.addWidget(self.label)

        openButton = QPushButton('Open Image')
        openButton.clicked.connect(self.openImage)
        layout.addWidget(openButton)

        thresholdSlider = QSlider()
        thresholdSlider.setMinimum(0)
        thresholdSlider.setMaximum(255)
        thresholdSlider.setValue(100)
        thresholdSlider.setOrientation(1)
        thresholdSlider.valueChanged.connect(self.segmentImage)
        layout.addWidget(thresholdSlider)

        self.setLayout(layout)

    def openImage(self):
        fileName, _ = QFileDialog.getOpenFileName(self, 'Open Image', '', 'Image files (*.png *.jpg *.jpeg)')

        if fileName:
            self.image = cv2.imread(fileName, cv2.IMREAD_GRAYSCALE)
            self.segmentedImage = np.zeros_like(self.image)

            pixmap = QPixmap(fileName)
            self.label.setPixmap(pixmap)

    def segmentImage(self, threshold):
        if self.image is not None:
            # Call the region growing algorithm
            regionGrowing(self.image, self.segmentedImage, 0, 0, threshold)

            cv2.imshow('Segmented Image', self.segmentedImage)

Step 4: Integrate the C++ backend with the Python GUI

Now, we need to compile the C++ region growing algorithm into a shared library and call it from the Python GUI using ctypes.

Compile the C++ source file into a shared library using the following command:

g++ -shared -fPIC -o libregionGrowing.so regionGrowing.cpp `pkg-config --cflags --libs opencv`

Next, you will need to load the shared library and call the regionGrowing function from the Python GUI:

import ctypes

lib = ctypes.CDLL('./src/libregionGrowing.so')
regionGrowing = lib.regionGrowing

if __name__ == '__main__':
    app = QApplication(sys.argv)
    mainWindow = RegionGrowingSegmentation()
    mainWindow.show()
    sys.exit(app.exec_())

That’s it! You should now have a fully functioning CT region growing segmentation application using Python with PyQt for the GUI and C++ with Qt for the backend processing. Feel free to customize the GUI and region growing algorithm to suit your needs.