Automatic Classification of Intracardiac Tumor and Thrombi in Echocardiography based on Sparse Representation

ABSTRACT:

Identification of intracardiac masses in echocardiograms is one important task in cardiac disease diagnosis. To improve diagnosis accuracy, a novel fully automatic classification method based on the sparse representation is proposed to distinguish intracardiac tumor and thrombi in echocardiography. Firstly, a region-of-interest is cropped to define the mass area. Then, a unique globally denoising method is employed to remove the speckle and preserve the anatomical structure. Subsequently, the contour of the mass and its connected atrial wall are described by the K-singular value decomposition and a modified active contour model. Finally, the motion, the boundary as well as the texture features are processed by a sparse representation classifier to distinguish two masses. 97 clinical echocardiogram sequences are collected to assess the effectiveness. Compared with other state-of-the-art classifiers, our proposed method demonstrates the best performance by achieving an accuracy of 96.91%, a sensitivity of 100% and a specificity of 93.02%. It explicates that our method is capable of classifying intracardiac tumor and thrombi in echocardiography, potentially to assist the cardiologists in the clinical practice.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

• In recent years, there has been a growing interest in sparse representation. The sparse concept originated from the transform-domain methods, which assumed that true signals could be sparsely represented by a linear combination of few basis elements in the transform domain. Instead of using fixed and orthogonal transforms, images can be described by sparse linear combinations of an overcomplete dictionary.

• Applications of the sparse representation include denoising, compression, regularization in inverse problems and classification. The K-Singular Value Decomposition (K-SVD) is one of the typical methods in the sparse representation, which utilizes overcomplete dictionaries obtained from a preliminary training procedure

DISADVANTAGES OF EXISTING SYSTEM:

They are unsatisfactory in the intracardiac mass segmentation owing to the movement of cardiac chamber during the cardiac cycle. In the systolic stage, the chamber shrinks so small that it is filled with an intracardiac mass, with the atrial wall and the mass boundary overlapped.

PROPOSED SYSTEM:

• In this paper, a novel method is proposed to classify the intracardiac tumor and thrombi in echocardiograms. Different from other approaches, the contribution of this method is incorporating the sparse representation methodology into the whole algorithm.

• The proposed method may offer several useful innovations. In the despeckling module, a novel globally denoising approach combining the K-SVD and the nonlocal-means algorithm (NLM) is employed to remove the speckle. Such a denoising method is quite general and able to cope with large amount of speckle noise and artifacts in echocardiograms.

• In the mass segmentation, the K-SVD is described to seek the initial contour of the mass and its connected atrial wall, and a modified ACM with a new external force is applied to refine the contour. Then, for each segmented mass, nine features are extracted, involving the motion, the boundary and the texture features.

ADVANTAGES OF PROPOSED SYSTEM:

Besides the regular features in a single frame, the spatial-temporal information and new texture characters are also considered. Finally, a sparse representation classifier (SRC) is applied to assess the overall classification performance.

MODULES:

1. Image Acquisition

2. Preprocessing

3. Segmentation

4. Feature Extraction

5. Classification

MODULE DESCRIPTION:

1. Image Acquisition:

• Image acquisition in image processing can be broadly defined as the action of retrieving an image from some source, usually a hardware-based source, so it can be passed through whatever processes need to occur afterward.

• Performing image acquisition in image processing is always the first step in the workflow sequence because, without an image, no processing is possible. The image that is acquired is completely unprocessed and is the result of whatever hardware was used to generate it, which can be very important in some fields to have a consistent baseline from which to work.

2. Preprocessing:

• The objective of the preprocessing phase is to apply possible image enhancement techniques to obtain the required visual quality of the image.

• First ROI selection is implemented.

• In the pre-processing, the noise will be removed by utilizing the non-local mean filter which does not update a pixel’s value with an average of the pixels around it, instead updates it using a weighted average of the pixels judged to be most kindred. The weight of each pixel depends on the distance between its intensity grey level vector and that of the target pixel. De-noised image of each pixel i of the non-local means is computed with the (1).

3. Segmentation:

• In this stage, region based segmentation method like modified active contour model is used.

1. Active Contour Method:

After preprocessing stage, ACM is employed to segment the tumor and thrombi in echocardiography. For the segmentation method, intra cardiac tumor and thrombi region will be segmented into two sides that are right and left. For the right side of image, the initialization mask is created suitable for the right region. Resizing the image pixels into only the region of interest using initialization mask is significant for efficient image processing.

• For further processing, the image and initialization mask will undergo the local region-based active contour method. Then affected region was segmented.

ii. Morphological Operation:

• Morphology is a technique for extracting the information from an image which is representation and description of region shape. In this paper morphological operations are used in post processing mainly as a filter. Its fundamental operations are Boundary pixels and low frequency pixels are eliminated from image.

• Erosion: it shrinks objects in the binary image

• Dilation: grows or thickens the objects in binary image

4. Feature Extraction:

i. Texture Feature:

In statistical texture analysis, texture features are computed from the statistical distribution of observed combinations of intensities at specified positions relative to each other in the image. According to the number of intensity points (pixels) in each combination, statistics are classified into first-order, second-order and higher-order statistics.

The Gray Level Co-ocurrence Matrix (GLCM) method is a way of extracting second order statistical texture features. The approach has been used in a number of applications, Third and higher order textures consider the relationships among three or more pixels.

Gray Level Co-Occurrence Matrix (GLCM) has proved to be a popular statistical method of extracting textural feature from images.

5. Classification:

The classification process is done over the segmented images. The main novelty here is the adoption of sparse representation. Sparse representation classifier is applied over the segmented images and the classification is done.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System           : Pentium Dual Core.

• Hard Disk      : 120 GB.

• Monitor          : 15’’ LED

• Input Devices : Keyboard, Mouse

• Ram                : 1GB.

SOFTWARE REQUIREMENTS:

• Operating system : Windows 7.

• Coding Language : MATLAB

• Tool                       : MATLAB R2013A

REFERENCE:

Yi Guo, Yuanyuan Wang*, Senoir Member, IEEE, Dehong Kong, Xianhong Shu, “Automatic Classification of Intracardiac Tumor and Thrombi in Echocardiography based on Sparse Representation” IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS.