LEVEL SET ALGORITHMS COMPARISON FOR MULTI-SLICE CT LEFT VENTRICLE SEGMENTATION

Transcription

1 LEVEL SET ALGORITHMS COMPARISON FOR MULTI-SLICE CT LEFT VENTRICLE SEGMENTATION 1 Investigador Prometeo, Universidad de Cuenca, Departamento de Electrónica y Telecomunicaciones, Cuenca, Ecuador 2 Departamento de Eléctrica, Electrónica y Telecomunicaciones, Cuenca, Ecuador 3 Departamento de Ciencias de la Computación, Cuenca, Ecuador

2 Outline of the presentation Introduction Level Sets Segmentation Level Sets Algorithms Performance Comparison Results Conclusions and future work 11/11/2016

3 INTRODUCTION Right Ventricle Left Ventricle 11/11/2016 Right Atrium Left Atrium Cardiac Motion Radial contraction Translation Torsion

4 Introduction OBJECTIVES To compare the performance of level set algorithms for segmentation of Multi-slice CT images. To propose a semiautomatic initialization algorithm based on mathematical morphology, watershed segmentation algorithms and region growing algorithms. 11/11/2016

5 11/11/2016 Level Sets Segmentation

6 Algorithms for Level Sets Segmentation Creaseg Matlab Based Level-Set Segmentation Platform CREATIS Laboratory France Olivier Bernard, Associate Professor Thomas Dietenbeck, PhD Student Denis Friboulet, Professor 11/11/2016

7 11/11/2016 Level Set Segmentation Algorithms Caselles Algorithm Contour based method The velocity term is calculated based on the image gradient Chan-Vese Algorithm Area based segmentation Algorithm Allows recovering of boundaries between regions with diferent intensities Lankton Algorithm local region based algorithm Allows the segmentation of objects with heterogeneous intensities. Bernard Algorithm A B-spline basis is used as a level set implicit function A region based method that uses the separable property of B-splines for attaining an efficient implementation Shi Algorithm An approximation for the level set curve evolution suitable for real-time implementation

8 Methods Multi-Slice CT image dataset -A 4-D MSCT database for one patient, including 20 instants of the cardiac cycle. -The size of the 3D object is 512 x 512 x 201 -Axial slices of size 512 x 512 were extracted from different instants of the cardiac cycle and from different locations along the Left Ventricle (LV) -The set was subdivided in 7 images for training and 83 images for validation Image Preprocessing -Median filtering of each slice using a window size of 7x7 - Manual segmentation performed by a medical doctor used as reference Evaluation -Dice coefficient between the segmentation obtained and the reference -The Dice coefficient measures the degree of overlap between two binary images 11/11/2016

9 METHODS 11/11/2016 Parameter setting and tuning The set of parameters was determined using a small set of 7 slices One of the parameters was varied while the rest remained fixed For each parameter, the Dice coefficient was calculated for all the images and the maximum average defined the parameter value Algorithms Comparison The algorithms comparison was performed using the test set of 83 images The initialization shape was manually traced using the CREASEG GUI Dice coefficient and execution time was stored. Algorithms Comparison on Noisy Images A synthetic image showing two concentric disks was generated. Each of the disks had a different gray level value. The image was contaminated with zero mean Gaussian noise The images were segmented without preprocessing filtering using an initialization shape traced by the user

10 METHODS Initialization using Marker-Controlled Watershed and Region Growing Segmentation -The foreground markers are calculated as connected regions of pixels within the objects using morphological reconstruction -The background markers are also calculated using morphological reconstruction as connected regions of pixels that do not belong to any object -The segmentation function based on the gradient is modified using minima imposition at the background and foreground marker locations -The watershed segmentation is performed on the modified segmentation function

11 RESULTS Transformada discreta del coseno 11/11/2016

12 11/11/2016 RESULTS

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15 RESULTS

16 CONCLUSIONS AND FUTURE WORK Level set segmentation algorithms are robust for segmenting noisy images corrupted with Gaussian noise The comparison of algorithms on real MSCT images shows that the Caselles and Chan-Vese algorithms attain the higher Dice coefficients with less outliers We proposed an algorithm for initialization of the segmentation of LV in MSCT images. The algorithm is based in a combination of gray-level mathematical morphology, watershed segmentation techniques and region growing segmentation Future research is aimed at extending the segmentation algorithm for working in 3-D. The segmentation of the rest of cardiac cavities is also one of the objectives of this research project. 11/11/2016

17 11/11/2016 MARGARITA