Quantitative Coronary Analysis Medical Image Processing Improved by Combining Wavelet Edge Detection and Segmentation
Tsair-Fwu Lee, Chang-Yu Lee, Pei‐Ju Chao, Chieh Lee, Changyu Wang, Chun‐Hsiung Fang · 2009
The quantitative coronary analysis (QCA) is a useful method for physicians to diagnose heart artery stenosis. So the precision of coronary angiography image quality is very definite important. In this study we present a precision 2-D image processing method that combines gradient segmentation and region segmentation approaches with an entropy maximization procedure. This method allows us to utilize all available information to achieve the most robust segmentation results for coronary angiography analysis. The aim is to improve the methods of 2-D coronary angiography image processing those usually lack such as precision. The example of coronary angiography data of true medical images were used to test the validity of our method. We found that our method not only achieved the precision we sought but also has many interesting applications that shall be most useful in medical practices.