基于opencv的超声扇形区域检测（C++）

简介

从超声采集的图像会带有部分用户操作界面的组件，若使用全局图片进行病灶检测，操作界面的组件可能会对检测结果造成干扰，通过扇形区域检测可防止这种情况的出现。

Code

LargestConnecttedComponent函数出自该博客《opencv 获取图像最大连通域 c++和python版》

// 获取检测区域边界
cv::Rect GetBorder(cv::Mat img) {
    cv::Mat gray;
    // rgb转灰度图
    cvtColor(img, gray, cv::COLOR_BGR2GRAY);
    int counts[256] = { 0 };
    int value;
    // 统计0~255各颜色出现次数
    for (int row_id = 0; row_id < gray.rows; row_id++) {
        for (int col_id = 0; col_id < gray.cols; col_id++)
        {
            counts[(int)gray.at(row_id, col_id)] += 1;
        }
    }
    // 找出出现次数最多的颜色
    int max_val = 0;
    int max_index = 0;
    for (int i = 0; i < 256; i++)
    {
        if (counts[i] > max_val) {
            max_index = i;
            max_val = counts[i];
        }
    }
 	
    cv::Mat thresh, morph_open_mat, morph_close_mat;
    // 阈值处理
    threshold(gray, thresh, max_index, 255, cv::THRESH_BINARY);
    // 定义kernel
    cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(1, 5));
    // 开运算
    morphologyEx(thresh, morph_open_mat, cv::MORPH_OPEN, kernel);
    // 闭运算
    morphologyEx(morph_open_mat, morph_close_mat, cv::MORPH_CLOSE, kernel);
    cv::Mat lcc_mat;
    // 最大连通区域检测
    LargestConnecttedComponent(morph_close_mat, lcc_mat);
    int max_x = 0, max_y = 0, min_x = gray.cols, min_y = gray.rows;
    // 找到矩形角点
    for (int row_id = 0; row_id < gray.rows; row_id++) {
        for (int col_id = 0; col_id < gray.cols; col_id++)
        {
            if ((int)lcc_mat.at(row_id, col_id) == 255) {
                if (col_id > max_x) {
                    max_x = col_id;
                }
                if (col_id < min_x) {
                    min_x = col_id;
                }
                if (row_id > max_y) {
                    max_y = row_id;
                }
                if (row_id < min_y) {
                    min_y = row_id;
                }
            }
        }
    }
    // 返回角点
    cv::Rect rect(cv::Point(min_x, min_y), cv::Point(max_x, max_y));
    return rect;
}

效果