Comparisons of state of the art methods for Left Ventricle Segmentation
16 state of the art methods are compared:
The 16 state-of-the-art segmentation methods are SDD thresholding method
(zhenzhou_threshold_selection_updated.m), Adaptive thresholding (adaptivethreshold.m and
averagefilter.m), Active contour without edge(region_seg.m), Region-based active contour
(local_AC_MS.m and local_AC_UM.m), GM(CMF_Cut.m), Level set (drlse_edge.m), Cross entropy
(minCE1.m), Fuzzy entropy (entropy_fuzzy_threshold1.m), ISO (isodata1.m), Maximum entropy
(maxentropie.m), Otsu (otsu.m), EM (EMSeg.m), K-means (kmeans.m), Soft (fth.m and
im_expand.m), Fuzzy C means ( fcmthresh.m and trapmf_mat.m) and Local thresholding
117 images are used to test the image segmentation methods:
The Cardiac MR images and the benchmark manual contours are from Medical Image
Computing and Computer Assisted Intervention (MICCAI) 2009. In this MATLAB cabinet, the
117 images are from Online_SC_HF_I9，Online_SC_HF_I10, Online_SC_HF_I11,
Online_SC_HYP12, Training_SC_HF_I4，Training_SC_HF_NI3, Training_SC_HF_NI4 and
Training_SC_HF_NI36. The data of the region of interest of these 117 images are integrated
into 117_ROI.mat and the benchmark manual contours corresponding to these images are
integrated into 117_Manual.mat.
APD and DICE are the measures to evaluate the segmentation accuracy. The results will be
writted into 117frame_data.xlsx.
Please Run Demo.m and QuantitativeComparison.m for the comparisons.
The codes are written by ZhenZhou Wang and JingJing Xiong, Please contact
firstname.lastname@example.org for more information.
For the LV segmentation, please kindly refer to my past publications:
 Robust and automatic diagnosis of the intraventricular mechanical dyssynchrony for the
left ventricle in cardiac magnetic resonance images, International Journal of Computer
Assisted Radiology and Surgery,2017
 Segmentation of the Left Ventricle in Short-axis Sequences by Combining Deformation
Flow and Optical Flow, IET Image Processing, 2016
 A New Approach for Automatic Identification of the Ventricular Boundary in Cine
Magnetic Resonance Images, Journal of Medical Imaging and Health Informatics, 2016
 An Efficient and Robust Method for Automatically Identifying the Left Ventricular
Boundary in Cine Magnetic Resonance Images, IEEE Transactions on Automation Science and
zhenzhou wang (2023). Image segmentation methods Comparison with MRI (https://www.mathworks.com/matlabcentral/fileexchange/62574-image-segmentation-methods-comparison-with-mri), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
- Image Processing and Computer Vision > Image Processing Toolbox > Image Segmentation and Analysis > Image Segmentation >
- Sciences > Neuroscience > Human Brain Mapping > MRI >
- Sciences > Biological and Health Sciences > Biomedical Imaging > MRI >
- Engineering > Biomedical Engineering > Biomedical Imaging >
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Comparison of Image Segmentation methods with MRI/
N-cut is removed because of the exe is not permitted for publishment.