A novel approach for segmentation and registration of Echo-cardiographic Images

Vidhyadhari Gondle (homepage)

Echo-cardiographic images provide a wealth of information about the heart(size, shape, blood flow rate, etc) and are therefore used to assess the functioning of heart. Automated analysis of echo-cardiographic images are aimed at extracting a displacement field which represents the heart motion. Such a field is critical for extracting higher order information which is needed for diagnosis of heart diseases. However, these images are very noisy which poses a huge challenge to image analysis.

Most of the methods used for the analysis of the echo-cardiographic images are designed in such a way that they are very specific to the noise present in the echo-cardiographic images. These methods can be categorized into two categories: (i) de-noise the signal prior to analysis and (ii) formulate input as a noisy signal to model the noise using statistical noise model. In this thesis we propose algorithms for analysis of echo-cardiographic images which do not require any pre-processing step or explicit handling of noise present in the images.

We present novel algorithms for segmentation and registration of echo-cardiographic images in this thesis. These two algorithms are designed based upon noise-robust image representation. This image representation is obtained by computing a local feature descriptor at every pixel location. The feature descriptor is derived using the Radon-Transform to effectively characterise local image context. The advantage of this representation is that, in addition to being robust to noise, it provides a good detail of the distribution of the pixel intensities in the image. Next, an unsupervised clustering is performed in the feature space to segment regions in the image. This feature-space representation is also used to extract hierarchical information for image registration.

The performance of the proposed methods is tested on both synthetic and real images. A comparison against well established feature descriptors is carried out to demonstrate the strengths and applicability of the proposed representation. Overall, the results indicate promise in the strategy of doing segmenta- tion of noisy data in image.

In this thesis, the algorithms are designed in such a way that the algorithm works efficiently even in presence of high level of speckle noise and doesn't require any pre-processing. Moreover it can be easily adapted to any other modality. The main contributions of this thesis are: 1. Noise-robust representation of an image in feature space. 2. Segmentation of an image using feature space. 3. Registration of images using hierarchical information. (more...)


Year of completion:  December 2013
 Advisor : Jayanthi Sivaswamy

Related Publications

  • Vidhyadhari G. and Jayanthi Sivaswamy - Echo-Cardiographic Segmentation: Via Feature-Space Clustering Proceedings of Seventh National Conference on Communications (NCC 2011),28-30 Jan, 2011, Bangalore, India. [PDF]