A segmentation method for myocardial ischemia/infarction applicable in heart photos

Abstract

The myocardial infarction, known as heart attack, is the ultimate result of a prolonged/untreated cardiac ischemia. The accurate segmentation of the myocardial infarction or ischemia in images obtained from diversified sources, such as Magnetic Resonance Images or Echocardiograph, is worthwhile for the medical area or the animal experimentation. An alternative image source for ischemia/infarction segmentation is the photo, which can depict the actual heart image. This work presents a method for ischemia segmentation in rat heart photos. The method applicability was tested in pictures of human hearts available in public databases from the Internet. At first, heart images were separated from the background using GrabCut method. Secondly, the segmentation of the cardiac ischemia region was performed by using Fuzzy Clustering method. Finally, a sequence of image processing (including morphological operations to remove small components and to fill the holes) was performed to obtain the final segmentation image. All resulting images were compared with the corresponding images containing contours of cardiac ischemia drawn manually by specialists. The mean accuracy was 83.24% ± 04.16%. As for the intrinsically human errors (tracing error between two specialists: 18.94% ± 05.30%), the average accuracy is within the inter-operator variability. As for the human heart pictures obtained from public libraries, the algorithm segmented the infarction areas correctly. The results show that the algorithm effectively helps the visualization of the cardiac ischemia/infarction region and has the potential to be applied to heart images of animals or humans, representing a versatile tool to assist advances in cardiomyopathology studies.

Keywords: Cardiac ischemia; Fuzzy clustering method; GrabCut method; Myocardial infarction; Segmentation image.