Amir Rajaei

Symbolic representation and classification of medical X-ray images

In this paper, we propose a symbolic approach for classification of medical X-ray images. Graph cut segmentation is applied to segment the body part of medical X-ray images. A complete directed graph is constructed using the centroid points in the boundary image of the segmented body part image. The complete directed graph is in turn used to extract features of distance and orientation. Further, the boundaries of segmented images are represented by its skeleton end points. Shape features are then extracted from the represented skeleton end points. To assimilate feature variations, we propose to symbolically represent the extracted features of each class in the form of interval valued features. Based on the proposed symbolic representation, symbolic classifier is then used for classification of medical X-ray images. Experimental results reveal the efficiency of our proposed symbolic classification model.

Feature Extraction of Currency Notes: An Approach Based on Wavelet Transform

In this paper, we propose to extract the texture features of currency note images. To extract the features, first the Discrete Wavelet Transform (DWT) in particular Daubechies 1 (DB1) is utilized on a currency note and the approximate coefficient matrix of the transformed image is obtained. A set of coefficient statistical moments are then extracted from the approximate efficient matrix. The extracted features are stored in a feature vector. The extracted features can be used for recognition, classification and retrieval of currency notes.

Classification of Medical Imaging Modalities Based on Visual and Signal Features

In this paper, we present an approach to classify medical imaging modalities. Medical images are preprocessed in order to remove noises and enhance their content. The features based on texture, appearance and signal are extracted. The extracted features are concatenated to each other and considered for classification. KNN and SVM classifiers are applied to classify medical imaging modalities. The proposed approach is conducted on IMageCLEF2010 dataset. We achieve classification accuracy 95.39 % that presents the efficiency of our proposed approach.

Symbolic Classification of Medical Imaging Modalities

In this paper, we propose a symbolic approach for classification of medical imaging modalities. Texture, appearance, and signal features are extracted from medical images. We propose to represent the extracted features by an interval valued feature vector. Unlike the conventional methods, the interval valued feature vector representation is able to preserve the variations existing among the extracted features of medical images. Based on the proposed symbolic representation, we present a method of classifying medical imaging modalities. The proposed classification method makes use of a symbolic similarity measure for classification. Experimentation is carried out on a benchmark medical imaging modalities database. Our proposed approach achieves classification within negligible time as it is based on a simple matching scheme.

Symbolic features for classification of medical X-ray body organ images

In this paper, we propose a model for symbolic representation and classification of medical X-ray body organ images. Medical X-ray body organ images are segmented using graph cut segmentation method. Based on the boundary of a segmented body organ image, the geometric centroid points are localized. A complete directed graph is then constructed over the centroid points. Subsequently, distance and orientation features are extracted from the constructed graph. The obtained features are used to form an interval valued feature vector representation. Finally, a symbolic classifier is explored to classify medical X-ray body organ images. Our proposed model is simple and efficient.

Matching and Retrieval of Medical Images

Digital imaging has revolutionized the field of medical imaging and has led to the development of sophisticated computer hardware technologies and specialized software that empower physicians to better distinguish abnormalities, characterize findings, supervise interventions and predict prognosis. In fact, CAD is one of the major research subjects in medical imaging and diagnostic radiology. These benefits have motivated researchers to develop dedicated systems to specific medical domains from clinical decision making to medical education and research. The medical imaging field has generated additional interest in methods and tools for the management and analysis of these images. It is important to extend such applications by supporting the retrieval of medical images by content.