Aleksandra Mojsilovic

Characterization of visually similar diffuse diseases from B-scan liver images using nonseparable wavelet transform

This paper describes a new approach for texture characterization, based on nonseparable wavelet decomposition, and its application for the discrimination of visually similar diffuse diseases of liver. The proposed feature-extraction algorithm applies nonseparable quincunx wavelet transform and uses energies of the transformed regions to characterize textures. Classification experiments on a set of three different tissue types show that the scale/frequency approach, particularly one based on the nonseparable wavelet transform, could be a reliable method for a texture characterization and analysis of B-scan liver images. Comparison between the quincunx and the traditional wavelet decomposition suggests that the quincunx transform is more appropriate for characterization of noisy data, and practical applications, requiring description with lower rotational sensitivity.

Matching and retrieval based on the vocabulary and grammar of color patterns

While it is recognized that images are described through color, texture and shapes of objects in the scene, general image understanding is still difficult. Thus, to perform image retrieval in a human-like manner one has to choose a specific domain, understand how users achieve similarity within that domain and then build a system that duplicates human performance. Since color and texture are fundamental aspects of human perception we propose a set of techniques for retrieval of color patterns. To determine how humans judge similarity of color patterns we performed a subjective study. Based on the results of the study five most relevant visual categories for the perception of pattern similarity were identified. We also determined the hierarchy of rules governing the use of these categories. Based on these results we designed a system which accepts one or more texture images as input, and depending on the query, produces a set of choices that follow human behavior in pattern matching. Processing steps in our model follow those of the human visual system, resulting in perceptually based features and distance measures. As expected, search results closely correlate with human choices.

The vocabulary and grammar of color patterns

We determine the basic categories and the hierarchy of rules used by humans in judging similarity and matching of color patterns. The categories are: (1) overall color; (2) directionality and orientation; (3) regularity and placement; (4) color purity; (5) complexity and heaviness. These categories form the pattern vocabulary which is governed by the grammar rules. Both the vocabulary and the grammar were obtained as a result of a subjective experiment. Experimental data were interpreted using multidimensional scaling techniques yielding the vocabulary and the hierarchical clustering analysis, yielding the grammar rules. Finally, we give a short overview of the existing techniques that can be used to extract and measure the elements of the vocabulary.

Characterization of visually similar diffuse diseases from B-scan liver images with the nonseparable wavelet transform

This paper describes the application of the nonseparable wavelet decomposition for the discrimination of diffuse diseases of liver. The proposed feature extraction algorithm uses the filter bank performing the quincunx transform and characterizes textures by a set of channel variances estimated at the output of each filter. Classification experiments on a set of three different tissue types show that this approach could be a reliable method for analysis of B-scan liver images.

Myocardial Tissue Characterization After Acute Myocardial Infarction With Wavelet Image Decomposition A Novel Approach for the Detection of Myocardial Viability in the Early Postinfarction Period

BACKGROUNDnOnly a few texture measures can be used for texture characterization of infarcted myocardium and detection of reperfused myocardium early after infarction. This study was conducted to establish the relationship between texture properties of infarcted myocardium and infarct-related artery patency by quantitative computer analysis of 2-dimensional echocardiographic images with the wavelet-based method for texture characterization, evaluate the relationship between texture properties and myocardial viability, and correlate histopathologic changes after experimental infarction with the texture measures.nnnMETHODS AND RESULTSnWe analyzed 2-dimensional transthoracic echocardiographic images in 18 patients at different time points after infarction using the wavelet transform method. Regional wall motion of infarcted segments was analyzed on a follow-up echocardiographic study obtained 6 months after infarction. To verify the accuracy of the proposed texture measure and energy difference cutoff value, we prospectively evaluated another group of 19 patients. In addition, histopathologic changes in 9 dogs with experimental infarction were correlated with the texture measures. Sensitivity, specificity, and accuracy of the wavelet method for detection of reperfusion in the study group were 73%, 86%, and 78%, respectively, on day 2; 91%, 86%, and 89%, at 1 week; and 100%, 100%, and 100% at 3 weeks. Among 9 patients with improvement in regional wall motion on a follow-up study, 7 on day 2, 8 at 1 week, and 9 at 3 weeks were classified into the reperfused group by the wavelet method. Histopathologic features associated with the classification of reperfusion by the wavelet method were infarct transmurality (P=0.024) and degree of necrosis (P=0.028).nnnCONCLUSIONSnOur clinical and experimental data suggest that the wavelet method can be used to differentiate between viable myocardium with recovery potential and definite myocardial necrosis in the early postinfarction period.

Wavelet domain features for texture description, classification and replicability analysis

In this paper we present a new wavelet domain technique for texture analysis and test of pattern replicability. The main property of the proposed features is that they measure texture quality along the most important perceptual dimensions. In other words, we quantify and classify textures according to their directionality, symmetry regularity and type of regularity. After the feature extraction, texture classification is performed by traversing a tree. The algorithm is tested on a database with 340 images demonstrating an excellent classification accuracy.

Classification of the ultrasound liver images with the 2N/spl times/1-D wavelet transform

The authors propose a new separable extension of the 1-D wavelet transform to the 2-D case and describe its application to the texture characterization problem. Comparing to previous decompositions, with the same resolution levels for each subband in horizontal and vertical directions, the new method has different resolutions for different directions. The new algorithm is applied to 84 ultrasound liver images, to detect liver cirrhosis in its early stage. The classification accuracy was 92%. The method was compared to other texture description methods (gray level cooccurrence, Laws filters, pyramid and tree structured wavelet decompositions). The proposed 2N/spl times/1-D wavelet decomposition, gave the highest classification rate, showing its applicability for the approach based analysis of the large class of natural textures.

Texture analysis and classification with the nonseparable wavelet transform

This paper investigates the application of nonseparable wavelet transform for the texture characterization. On a set of 21 Brodatz textures we have performed traditional dyadic wavelet decomposition (in two levels) and nonseparable quincunx decomposition (in six levels), testing their ability to classify textures in: (a) normal working conditions, (b) noisy environment, and (c) with respect to rotation. Our experiments have shown that the quincunx transform is appropriate for characterization of noisy data, small number of resolution levels and shorter feature vectors and rotationally invariant description.

Retrieval of color patterns based on perceptual dimensions of texture and human similiarity rules

While it is recognized that images are described through color, texture and shapes of objects in the scene, the general image understanding is still very difficult. Thus, to perform an image retrieval in a human-like manner one has to choose a specific domain, understand how users achieve similarity within that domain and then build a system that duplicates human performance. Since color and texture are fundamental aspects of human perception we propose a set of techniques for retrieval of color patterns. To determine how humans judge similarity of color patterns we performed a subjective study. Based on the result of the study five most relevant visual categories for the perception of pattern similarity were identified. We also determined the hierarchy of rules governing the use of these categories. Based on these results we designed a system which accepts one or more texture images as input, and depending on the query, produces a set of choices that follow human behavior in pattern matching. Processing steps in our model follow those of the human visual system, resulting in perceptually based features and distance measures. As expected, search results closely correlate wit human choices.

Analysis and characterization of myocardial tissue with the wavelet image extension [US images]

Some computer applications for tissue characterization in medicine operate with tissue samples taken from small area of interest so that only few methods can be used. These methods should be insensitive to noise and image distortions and yet reliable enough. Here, the authors propose a new approach for texture analysis, based on the wavelet transform. The idea of this method is to decompose analyzed image with the filter bank derived from an orthonormal wavelet basis and to form image approximations with higher resolution. Energies calculated at the outputs of the filter bank are used as texture features in an unsupervised classification procedure based on a modification of the statistical T-test. The method is tested clinically, for characterization of infarcted myocardium and reported classification results are very promising.