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A segmentation method for myocardial ischemia/infarction applicable in heart photos
Salety Ferreira Baracho1, Daniel José Lins Leal Pinheiro1, Carlos Marcelo Gurjão de Godoy1
1Institute of Science and Technology, Federal University of São Paulo, UNIFESP, São José dos Campos, SP, Brazil.
Insights
This study introduces an automated method for segmenting cardiac ischemia in heart photos, achieving 83.24% accuracy. The technique aids visualization and has potential for both animal and human cardiomyopathology research.
Area of Science:
- Biomedical imaging
- Cardiovascular pathology
- Image processing
Background:
- Myocardial infarction (heart attack) results from prolonged cardiac ischemia.
- Accurate segmentation of ischemia/infarction in medical images is crucial for diagnosis and research.
- Existing methods often rely on complex imaging modalities; photo-based segmentation offers an alternative.
Purpose of the Study:
- To develop and validate an automated method for segmenting cardiac ischemia in heart photographs.
- To assess the accuracy of the proposed method against manual segmentation by specialists.
- To evaluate the algorithm's applicability to both rat and human heart images.
Main Methods:
- Heart images were preprocessed to separate the heart from the background using the GrabCut method.
- Cardiac ischemia regions were segmented using Fuzzy Clustering.
- Post-processing involved morphological operations to refine segmentation accuracy.
Main Results:
- The automated segmentation achieved a mean accuracy of 83.24% ± 4.16%.
- This accuracy is comparable to the inter-operator variability observed between human specialists (18.94% ± 5.30%).
- The algorithm successfully segmented infarction areas in human heart images from public databases.
Conclusions:
- The developed algorithm effectively visualizes cardiac ischemia/infarction regions in heart photos.
- The method demonstrates versatility and potential for application in both animal and human cardiomyopathology studies.
- This technique offers a valuable tool to advance research in heart disease.
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.
