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Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. In this work, Rough set theory (RST) has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in...
Microcalcifications are one of the key symptoms facilitating early detection of breast cancer. In this paper, The textural features are extracted from the segmented mammogram image to classify the microcalcifications into benign, malignant or normal. The reduced features are selected from the extracted set of features using reduction algorithms. Initially the reduced features are normalized between...
This paper proposes a method for breast cancer diagnosis in digital mammogram. The article focuses on using texture analysis based on curvelet transform for the classification of tissues. The most discriminative texture features of regions of interest are extracted. Then, a nearest neighbor classifier based on Euclidian distance is constructed. The obtained results calculated using 5-fold cross validation...
This work aims at selecting useful features in critical angles and distances by Gray Level Co-occurrence Matrix (GLCM). In this project, images were labeled based on physician opinion in two groups (malignant or benign). These labeled images were used in classification analysis. Images were opened and read in Matlab software. The tumors were cropped in rectangular shape manually; then graycomatrix...
In this paper, we investigate the classification of masses with texture features. We propose an improved level set method to find the boundary of a mass, based on the initial contour provided by radiologists. After the boundary of a mass is found, texture features from Gray Level Co-occurrence Matrix (GLCM) are extracted from the surrounding area of the boundary of the mass. The extracted texture...
Mammography is probably the best method for early detection of abnormalities in the breast tissue. Higher breast tissue densities significantly reduce the overall detection sensitivity and can lead to false negative results. In automatic detection algorithms, knowledge about breast density can also be useful for setting an appropriate threshold. It is impossible to produce satisfactory classification...
Breast cancer is the most common cancer among women and the leading cause of cancer deaths in women. Mammography plays a major role in the early detection of breast cancer. In this study computer aided detection (CAD) system is designed to classify mammographic abnormalities. CAD system used computerized algorithms in order to detect breast abnormalities. Within this work, breast images from MIAS...
In this paper, an algorithm for texture analysis of clustered calcification based on statistical texture models is proposed. The prior knowledge of both normal and lesion training samples are incorporated into statistical texture models separately. Specifically, beside texture analysis of the lesion tissues, and the resultant statistical parameters can also be used for unknown sample representation...
Strong evidence shows that characteristic patterns of breast tissues as seen on mammography, referred to as mammographic parenchymal patterns, provide crucial information about breast cancer risk. Quantitative evaluation of the characteristic mixture of breast tissues can be used as for mammographic risk assessment as well as for quantification of change of the relative proportion of different breast...
Architectural distortion is a commonly missed sign of breast cancer. This paper investigates the detection of architectural distortion, in mammograms of interval-cancer cases taken prior to the diagnosis of breast cancer, using Gabor filters, phase portrait analysis, fractal dimension, and texture analysis. The methods were used to detect initial candidates for sites of architectural distortion in...
In this paper, we present a content-based image retrieval (CBIR) system called MammoSVD. This CBIR system is developed based on breast density - fatty or dense, and the database used, from the IRMA project, provides images with the ground truth already set. Singular value decomposition (SVD) is proposed for the breast density characterization by the selection of the first singular values, in order...
In this work, we described a new two-stage hierarchical framework for mammogram retrieval. We tested the proposed approach on the reference library from USF-DDSM. For each query ROI (region of interest), the proposed scheme first computes its 14 texture and shape features, then the voting method based on five classifiers is used to classify the ROIs in the reference library, this phase eliminates...
This paper proposes a computer aided decision support system for an automated diagnosis and classification of breast tumor using mammogram. The proposed method differentiates two breast diseases namely benign masses and malignant tumors. From the preprocessed mammogram image, texture and shape features are extracted. The optimal features can be extracted by using a feature selection scheme based on...
Image feature extraction was utilized to retrospectively analyze screening mammograms taken prior to the detection of a malignant mass for early detection of breast cancer. The mammograms of 58 biopsy proven breast cancer patients were collected. In each case, the mammograms taken 10 to 18 months prior to cancer detection were evaluated. For each of the two mammographic projections of the abnormal...
A novel retrieval method for mammograph images was proposed in this paper. The texture structure of mammograph image was firstly extracted by the maximum and minimum of local intensity. Then, a new texture feature, distortion constraint, was introduced based on the speciality of the region of interest. Combined with the weighted moments, the new descriptor was given as an index for mammograph image...
Mass detection in mammograms is a challenging problem. In this paper, we propose a cost-sensitive cascaded method for automatic mass detection, which employs machine learning techniques to detect region of interests (ROI). In detail, we divide the original mammograms into overlapped squared sub-images. For each sub-image, intensity features based on gray histogram, texture features based on spatial...
The current work aims at the classification of breast tissue according to Breast Imaging Reporting and Data System (BIRADS), based on texture features from mammographic images. To this end an integrated software system was developed in visual C++ using the .NET 2.0 Framework. The system takes as inputs pictures in most of the popular bitmap formats as well as DICOM and provides as output a specific...
We investigate the use of mammograms texture features in a clinical evaluation in mammography in order to assist in the classification of an image based on the breast tissue index. Breast tissue indices give the possibility to analyze images and determine the similarity of the images and evaluate the classification of the obtained image The measures of similarity of images used for the texture analysis...
A computerized classification based on morphologic and texture features is proposed to increase the accuracy of the ultrasonic diagnosis of breast tumors. Firstly, tumor boundaries are obtained with the gray-level threshold segmentation algorithm and the dynamic programming method. Then five morphologic features and two texture features are extracted. Finally, an artificial neural network with the...
This work concerns the development of a generalized framework for computer-aided diagnosis of medical images. The system is built to mimic human texture perception as texture has been shown to be an important feature for pathology discrimination in medical images. In particular, it was shown by Julesz that orientation, frequency and scale are important markers for texture discrimination. Consequently,...
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