IIIT Hyderabad A NOVEL APPROACH TO SEGMENTATION AND REGISTRATION OF ECHO-CARDIOGRAPHIC IMAGES Vidhyadhari Gondle Supervisor: Jayanthi Sivaswamy CVIT, IIIT Hyderabad, Hyderabad, India

IIIT Hyderabad 2 Echo-cardiographic Images Echo-cardiogram is the sonogram of heart. It is one of the primary diagnostics conducted due to its non- invasive nature. Ultrasound Imaging Echo-cardiogram Analysis of Echo-cardiographic Images is challenging because of presence of speckle.

IIIT Hyderabad 3 Echo-cardiographic images Phenomena that occur during image acquisition: Absorption Specular Reflection Diffuse Reflection Speckle noise is caused by diffuse reflection which occurs because of back-scattering

IIIT Hyderabad 4 Motivation The challenges that occur during processing the echo- cardiographic images are due to the physics involved in acquisition of the images. Problems in processing of echo-cardiographic images are: Presence of speckle noise. Blurring of spatial information. Discontinuities in the contours. Image analysis algorithm aimed for ultrasound images must be robust to speckle noise and should be able to detect the image features in low contrast.

IIIT Hyderabad Present Methods Pre-processing based approaches. Use of noise model in the formulation of algorithm. Echo-cardiographic Image Analysis. Loss of information like speckle pattern. Difficulties in the designing of the algorithm. Complex formulation.

IIIT Hyderabad 6 Problem Statement Objective: To devise a solution for Segmentation and Registration of echo-cardiographic images that can handle noise naturally. Proposed Solution: A noise-robust representation of an image. Image is mapped from intensity space to feature space.

IIIT Hyderabad 7 Feature Descriptor Requirements of the Feature Descriptor: Capture local context of a pixel since it gives information about speckle. Stability with respect to small distortions and invariance to geometric distortions. Noise Robustness. We design Radon Transform based feature vector.

IIIT Hyderabad 8 Radon Transform

IIIT Hyderabad Feature Descriptor 9

IIIT Hyderabad 10 Strengths of R-Transform Gives high response in the presence of high intensities hence can be used to differentiate between bright regions and dark regions. Good Representation of shape or context around the pixel. Resistant to the blurring effect present in the image because blurring spreads the intensity values in neighbourhood.

IIIT Hyderabad Proposed Methods: We proposed two algorithms based on this feature descriptor for echo-cardiographic images. – Image Segmentation Pixels are grouped into regions based upon certain properties – Image Registration Aligning two Images I 1, I 2 taken at different times/different modalities/different view points using Mapping function T where I 1 (x) ~ I 2 (T(x)).

IIIT Hyderabad Segmentation

IIIT Hyderabad Outline Labelled Image K-Means Clustering Clustering in Feature space and Back-Mapping. Noise-Robust Representation Mapping the image from intensity space to feature space Input Image Segmentation

IIIT Hyderabad 14 Algorithm has three parts. Noise-Robust Representation of Image. At each pixel a feature vector is computed based upon radon transform. K-Means Clustering in Feature Space. Simplest unsupervised learning in feature space to classify the pixels based upon the feature vectors. Mapping back the class labels to the image. Once the points in the feature space are given the labels by the clustering algorithm, we need to assign the same labels to the pixels in the image. Algorithm Segmentation

IIIT Hyderabad 15 K-Means Clustering K-Means clustering is used to group the pixels with similar feature-vectors. K-Means is one of simplest and efficient ways to clustering when compared to other clustering methods like Agglomerative, Fuzzy C-Means, and Mixture of Gaussians. The third part of the algorithm is mapping the assigned labels back to the pixels. Segmentation

IIIT Hyderabad 16 Assessment The proposed method is tested on synthetic and real Ultrasound Images. The results obtained are compared with other feature-descriptors to test its efficiency: – Geometric Blur. (GC) – Histogram of Oriented Gradients.(HC) – DAISY.(DC) Segmentation

IIIT Hyderabad 17 Settings The proposed method is implemented using the following parameter setting. The Radon Transform is computed on 10X10 image patches in 37 different orientations. Radon Transform is 37X10. Final Feature Descriptor is 37X1 Segmentation

IIIT Hyderabad 18 Evaluation Measures Segmentation

IIIT Hyderabad 19 Evaluation Measures The range of Rate of Misclassification is [0-1]. Low rate of misclassification indicates better performance of the algorithm. The range of Dice Coefficient is from [0-1]. High value indicates better performance of the algorithm. Segmentation

IIIT Hyderabad 20 Synthetic Data A synthetic image is generated to have a dark circle on a white background with the intensity of the pixel varying inversely with the radius. Speckle noise with different parameters is added to different regions to model ultrasound images. Segmentation

IIIT Hyderabad Segmentation HC ) DC ) GC FSC ) where HC = Histogram of Oriented gradients; DC = Daisy; GC = Geometric Blur and FSC = Proposed Feature Descriptor

IIIT Hyderabad 22 Quantitative Results Feature Rate of Misclassification Dice's Coefficient DC GC FSC Segmentation where DC = Daisy Feature Descriptor; GC = Geometric Blur and FSC = Proposed Feature Descriptor

IIIT Hyderabad 23 Real Data Echocardiographic data is generally acquired in a video form. We have evaluated our results on 3 such data sets. The ground truth was marked by an expert. Segmentation

IIIT Hyderabad Segmentation Original ImageLabelled Image Segmented Result overlaid on Ground Truth

IIIT Hyderabad 25 Segmentation Histogram of Oriented Gradients DAISYGeometric Blur Proposed Feature Descriptor Labels used for representing classes

IIIT Hyderabad 26 Quantitative Results Image Rate of Misclassification Dice's Coefficient FSCGCFSCGC Mean Segmentation

IIIT Hyderabad 27 Image Registration

IIIT Hyderabad Problem Statement and Approach Image Registration is alignment of any two Images I 1, I 2 taken at different times/different modalities/different view points using Mapping function T where I 1 (x) ~ I 2 (T(x)). We adapted Demon’s algorithm since it well suits complex motion involved in echo-cardiographic images. Registration

IIIT Hyderabad Motivation Demons is a popular algorithm for non-rigid medical image registration. Drawbacks of Demon’s algorithm: –Displacement field computation based on brightness constraint. –Sensitive to noise. Registration

IIIT Hyderabad Considerations in Noise Adaption Desirables: Displacement field shouldn’t get affected by noise. Pixels in Moving Image must be mapped to similar regions in Source Image. Similarity(as measured by SSD) between Moving Image and Source Image must be optimized. Registration

IIIT Hyderabad Computing Similarity We need a measure which: Is independent of noise Gives the information of local context if the image, so that similar regions can be mapped. Registration

IIIT Hyderabad Modified Demon’s Algorithm Output Registered Image Minimizing SSD of Distance measure for Moving Image and Source Image. Computation of Distance measure from Labelled Regions K-Means Clustering Clustering in Feature space and Back-Mapping. Noise-Robust Representation Mapping the image from intensity space to feature space Input Image Registration

IIIT Hyderabad Distance Information We use distance measure information computed after k-means clustering of feature-space representation of the image Intensity in Demon’s algorithm is now replaced with this distance measure Registration Clusters in Feature space Cluster 2 Cluster 3 Cluster 1 Ck x D

IIIT Hyderabad Distance Measure Registration

IIIT Hyderabad Significance of using Distance Measure This Distance Measure represents the variance of the feature vector. In Ideal case we could have used both mean and variance but to simplify the process we use only variance. Also since Demon’s algorithm proceeds by mapping nearest pixels first, nearest regions will be mapped initially. Registration

IIIT Hyderabad Strengths of Distance Measure Representation of Local Context: This measure is computed from Radon-Transform which gives good information about local context. Also it doesn’t get affected with high speckle noise. Localization: The localization is handled internally by brightness constraint in Demon’s algorithm. Registration

IIIT Hyderabad 37 Settings The proposed method is tested on synthetic and real data. Radon Transform is computed on 10 X 10 image patches in 37 different orientations. The dimensions of Radon Transform obtained is 37 X 10. Final feature vector obtained is 37 X 1 Registration

IIIT Hyderabad Evaluation Measures 38 Registration

IIIT Hyderabad 39 Synthetic Data Synthetic Images generated in evaluating Segmentation algorithm are used. Speckle noise, Gaussian noise and Salt & pepper noise with different parameters is added to different regions to validate the algorithms. Registration

IIIT Hyderabad Registration Images Without Noise Gaussian Noise Speckle Noise Salt and Pepper Noise Moving Image Source Image Image Registered using Proposed Algorithm Image Registered using Demons Algorithm Image Registered using Optical flow

IIIT Hyderabad 41 Quantitative Results AlgorithmSSDMDDFRDDF Proposed Algorithm Demons Algorithm Optical Flow Registration

IIIT Hyderabad 42 Real Data Echocardiographic data is generally acquired in a video form. We evaluated experiments on 3 sets of echo-cardiographic data. Registration

IIIT Hyderabad 43 Input Images. (a) Source Image Registration. (b) Moving Image Moving Image is to be aligned with Source Image

IIIT Hyderabad (a) Proposed algorithm) (b) Demon’s algorithm)(c)Optical Flow Registration

IIIT Hyderabad 45 Quantitative Results We present the results only in terms of SSD for 3 data-sets s Proposed algorithm Demon’s algorithm Optical Flow Original Dataset Original Dataset Original Dataset Registration

IIIT Hyderabad 46 Conclusion A feature-space representation of an image is presented. Proposed Radon Transform based Feature descriptor. This feature-space representation was used in Segmentation and Registration algorithms.

IIIT Hyderabad Conclusion Segmentation results have out-performed when compared to other feature descriptors. Image Registration results are comparable to Demon’s algorithm with slight improvement.

IIIT Hyderabad 48 Future Work Time complexity can be improved by computing the feature descriptor at required pixels rather than every pixel of the image. There is a requirement for post-processing of these images. Comparison between normal and abnormal sequences also can be taken as further step to aid in diagnosis.

IIIT Hyderabad 49 References [1] J.-P. Thirion, “Image matching as a diffusion process: an analogy with maxwell’s demons,” Medical Image Analysis, vol. 2, no. 3, pp. 243–260, [2] B. K. Horn and B. G. Schunck, “Determining optical flow,” [3] I. Dydenko, F. Jamal, O. Bernard, J. D’hooge, I. E. Magnin, and D. Friboulet, “A level set framework with a shape and motion prior for segmentation and region tracking in echocardiography,” Medical Image Analysis, vol. 10, no. 2, pp. 162–177, 2006.

IIIT Hyderabad 50 Publications Vidhyadhari Gondle and Jayanthi Sivaswamy, “Echo- Cardiographic Image Segmentation : Via Feature Space Clustering“,in Proceedings of the National Conference on Communications, Bangalore, India, 2011.

IIIT Hyderabad 51 Questions ??? Thank you