Color Image Clustering using Block Truncation Algorithm

With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters

Bag-of-Features Image Indexing and Classification in Microsoft SQL Server Relational Database

This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.Comment: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, 24-26 June 201

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

Gabor filters for rotation invariant texture classification

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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