TY - GEN
T1 - Improved technique to detect the infarction in delayed enhancement image using k-mean method
AU - Metwally, Mohamed K.
AU - El-Gayar, Neamat
AU - Osman, Nael F.
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - Cardiac magnetic resonance (CMR) imaging is an important technique for cardiac diagnosis. Measuring the scar in myocardium is important to cardiologists to assess the viability of the heart. Delayed enhancement (DE) images are acquired after about 10 minutes following injecting the patient with contrast agent so the infracted region appears brighter than its surroundings. A common method to segment the infarction from DE images is based on intensity Thresholding. This technique performed poorly for detecting small infarcts in noisy images. In this work we aim to identify the best threshold value to segment the infarction in case of segmentation using simple Threshold and propose a modified technique to improve the segmentation in noisy images. Our proposed technique is based on enhancing Thresholding using k-means clustering. We test our proposed model using computer simulated and real images with different contrast-to-noise ratio (CNR). We used F-score, which is a combined measure of the precision and sensitivity, to determine the performance of the proposed technique versus simple Thresholding. The results show that the proposed technique outperforms existing methods.
AB - Cardiac magnetic resonance (CMR) imaging is an important technique for cardiac diagnosis. Measuring the scar in myocardium is important to cardiologists to assess the viability of the heart. Delayed enhancement (DE) images are acquired after about 10 minutes following injecting the patient with contrast agent so the infracted region appears brighter than its surroundings. A common method to segment the infarction from DE images is based on intensity Thresholding. This technique performed poorly for detecting small infarcts in noisy images. In this work we aim to identify the best threshold value to segment the infarction in case of segmentation using simple Threshold and propose a modified technique to improve the segmentation in noisy images. Our proposed technique is based on enhancing Thresholding using k-means clustering. We test our proposed model using computer simulated and real images with different contrast-to-noise ratio (CNR). We used F-score, which is a combined measure of the precision and sensitivity, to determine the performance of the proposed technique versus simple Thresholding. The results show that the proposed technique outperforms existing methods.
KW - Cardiac Magnetic resonance
KW - Delayed Enhancement
KW - k-means clustering technique
UR - http://www.scopus.com/inward/record.url?scp=77955402116&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77955402116&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13775-4_12
DO - 10.1007/978-3-642-13775-4_12
M3 - Conference contribution
AN - SCOPUS:77955402116
SN - 3642137741
SN - 9783642137747
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 108
EP - 119
BT - Image Analysis and Recognition - 7th International Conference, ICIAR 2010, Proceedings
T2 - 7th International Conference on Image Analysis and Recognition, ICIAR 2010
Y2 - 21 June 2010 through 23 June 2010
ER -