Analysis and classification of myocardial infarction tissue from echocardiography images based on texture analysis

Texture analysis is an important characteristic for automatic visual inspection for surface and object identification from medical images and other type of images. This paper presents an application of wavelet extension and Gray level cooccurrence matrix (GLCM) for diagnosis of myocardial infarction tissue from echocardiography images. Many of applications approach have provided good result in different fields of application, but could not implemented at all when texture samples are small dimensions caused by low quality of images. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposition images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. The gray level co-occurrence matrices are computed for each sub-band. The feature vector of testing image and other feature vector as normal image classified by Mahalanobis distance to decide whether the test image is infarction or not

Application of Fractal and Wavelets in Microcalcification Detection

Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications

Identification of Myocardial Infarction Tissue Based on Texture Analysis From Echocardiography Images

Texture is an important characteristic that can be used for identification and detection for surface defect or abnormalities. This research has an algorithm for identifying heart with suspected myocardial infarction problem based on texture analysis applied on echocardiography images. Texture tissue sample images taken from echocardiography sub-image (ROI). There are two tissue classes: Type 1 corresponds to normal myocardial tissue, whereas Type 2 corresponds to infarcted myocardium with small dimension. Therefore, in order to investigate possible in differences tissue between patient with infarction tissue or not, we proposed a Wavelet Extension Transform and Gray Level Co-occurrence matrix.Wavelet Extension Transform is used to form an image approximation with higher resolution. The gray level co-occurrence matrices are computed for each sub-band. The feature vector of testing image and other feature vector as normal image classified by Mahalanobis distance to decide whether the test image is infarction or not. The method is tested with real data from echocardiography images of human heart. For each patient to be analyzed tissue samples are taken from not-affected area and tissue samples are taken from image segments corresponding to the infarcted area of myocardium. The result of this experiment can detect difference image from echocardiography as normal myocardium and infarcted myocardial tissue

IDENTIFICATION OF MYOCARDIAL INFARCTION TISSUE BASED ON TEXTURE ANALYSIS FROM ECHOCARDIOGRAPHY IMAGES

Texture is an important characteristic that can be used for identification and detection for surface defect or abnormalities. This research has an algorithm for identifying heart with suspected myocardial infarction problem based on texture analysis applied on echocardiography images. Texture tissue sample images taken from echocardiography sub-image (ROI).  There are two tissue classes: Type 1 corresponds to normal myocardial tissue, whereas Type 2 corresponds to infarcted myocardium with small dimension. Therefore, in order to investigate possible in differences tissue between patient with infarction tissue or not, we proposed a Wavelet Extension Transform and Gray Level Co-occurrence matrix.Wavelet Extension Transform is used to form an image approximation with higher resolution. The gray level co-occurrence matrices are computed for each sub-band. The feature vector of testing image and other feature vector as normal image classified by Mahalanobis distance to decide whether the test image is infarction or not. The method is tested with real data from echocardiography images of human heart. For each patient to be analyzed tissue samples are  taken from not-affected area  and tissue samples are taken from image segments corresponding to the infarcted area of myocardium. The result of this experiment can detect difference image from echocardiography as normal myocardium and infarcted myocardial tissue

A Novel Active Contour Model for Texture Segmentation

Texture is intuitively defined as a repeated arrangement of a basic pattern or object in an image. There is no mathematical definition of a texture though. The human visual system is able to identify and segment different textures in a given image. Automating this task for a computer is far from trivial. There are three major components of any texture segmentation algorithm: (a) The features used to represent a texture, (b) the metric induced on this representation space and (c) the clustering algorithm that runs over these features in order to segment a given image into different textures. In this paper, we propose an active contour based novel unsupervised algorithm for texture segmentation. We use intensity covariance matrices of regions as the defining feature of textures and find regions that have the most inter-region dissimilar covariance matrices using active contours. Since covariance matrices are symmetric positive definite, we use geodesic distance defined on the manifold of symmetric positive definite matrices PD(n) as a measure of dissimlarity between such matrices. We demonstrate performance of our algorithm on both artificial and real texture images