Prospective algorithmic approach for this image in OpenCV - algorithm

Question

I am looking for advice from people having extensive experience with computer vision. I have a collection of ultrasonographic B&W images like the one below (without the stars and dotted line):
What I would like to do is detect the contour of a blood vessel (for example, the one highlighted by the yellow star). Of course my first step would be to define the ROI and maximize the contrast. But what would then be the best algorithm to use? Segmentation with the watershed algorithm? Something else?
I am little unsettled because of the image blur...
Edit:
As requested in the comments, here would be an example of source and result images:

Answer 1

Following is a simple approach to your problem, if I understood you correctly. My result is shown below.
And here is the code
int max_area_threshold = 10000;
int min_area_threshold = 1000;
float rational_threshold = 0.7;
cv::Mat img = cv::imread("sample.jpg", CV_8UC1);
cv::Mat img_binary;
//Create binary imae by tresholding
cv::threshold(img, img_binary, 25, 255, CV_THRESH_BINARY);
//Invert black-white
cv::bitwise_not(img_binary, img_binary);
//Eliminating small segments
cv::erode(img_binary, img_binary, cv::Mat(), cv::Point(-1, -1), 2, 1, 1);
cv::dilate(img_binary, img_binary, cv::Mat(), cv::Point(-1, -1), 1, 1, 1);
//Find contours
std::vector<std::vector<cv::Point>> contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours( img_binary, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
for( int i = 0; i< contours.size(); i++ )
{
if(contours[i].size() < 5)
continue;
//Fit ellipse to contour
cv::RotatedRect boundRect = cv::fitEllipse(contours[i]);
//Check the squareness of the bounding box
if(abs((boundRect.size.width / (float)boundRect.size.height)-1.0) > rational_threshold)
continue;
//Elliminate too big segments
if(boundRect.boundingRect().area() > max_area_threshold)
continue;
//Elliminate too small segments
if(boundRect.boundingRect().area() < min_area_threshold)
continue;
drawContours(img, contours, i, cv::Scalar(255), 0.2, 8, hierarchy, 0, cv::Point() );
}
cv::imwrite("result.jpg", img);
I hope it helps.