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Search Results: 1 - 3 of 3 matches for " MINAVATHI "
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International Journal of Machine Intelligence , 2011,
Abstract: Detection and classification of spiculated masses in ultrasound images is still a challenge due to the interference of speckle noise and fuzziness of boundaries. Ultrasound (US) is an important adjunct to mammography in breast cancer detection as it doubles the rate of detection in dense breasts do a dynamic analysis of moving structures in breast. This paper presents technique to detect spiculations and boundary of spiculated masses in breast ultrasound images. In the proposed method, ultrasound images are preprocessed using Gaussian smoothing to remove additive noise and anisotropic diffusion filters to remove multiplicative noise (speckle noise). Active contour method has been used to extract a closed contour of filtered image which is the boundary of the spiculated mass. Spiculations which make breast mass unstructured or irregular are marked by measuring the angle of curvature of each pixel at the boundary of mass. To classify the breast mass as malignant or benign we have used the structure of mass in accordance with spiculations and elliptical shape. We have used receiver operating characteristic curve (ROC) to evaluate the performance. We have validated the proposed algorithm on 100 sub images(40 spiculated and 60 non spiculated) and results shows 90.5% of sensitivity with 0.87 Area Under Curve. Proposed techniques were compared and contrasted with the existing methods and result demonstrates that proposed algorithm has successfully detected spiculated mass ROI candidates in breast ultrasound images.
Model based approach for Detection of Architectural Distortions and Spiculated Masses in Mammograms
Minavathi,Murali. S.,M. S. Dinesh
International Journal on Computer Science and Engineering , 2011,
Abstract: This paper investigates detection of Architectural Distortions (AD) and spiculated masses in mammograms based on their physical characteristics. We have followed a model based approach which separates the abnormal patterns of AD and spiculated masses from normal breast tissue. The model parameters are retrieved from Gabor filters which characterize the texture features and synthetic patternswere generated using pplanes to retrieve specific patterns of abnormalities in mammographic images. In addition, eight discriminative features are extracted from region of interest (ROI) which describes the patterns representing AD and spiculated masses. Support vector machine (SVM) and Multi-layer Perceptrons (MLP) classifiers are used to classify the discriminative features of AD and spiculated masses from normal breast tissue. This study concentrates on classifying AD and spiculated masses from the oneswhich actually are normal breast parenchyma. Our proposal is based on the texture pattern that represents salient features of AD and spiculated mass. Once the descriptive features are extracted SVM and MLP classifiers are used. We have used receiver operating characteristic curve (ROC) to evaluate the performance and we have compared our method with several other existing methods. Our methodoutperformed other existing methods by achieving 90% of sensitivity, 86% specificity in distinguishing AD from normal breast tissue and 93% sensitivity and 88% specificity in classifying spiculated mass from normal breast parenchyma. In first stage of this study we consider ROI’s that include AD, spiculated masses and normal breast tissue as input. Our method was tested on 190 ROI’s( 19 AD , 19 spiculated mass and 152 normal breast tissue) from Mini-MIAS database and 150 ROI’s( 23 AD , 30 spiculated mass and 97 normal breast tissue ) collected from DDSM database. In the second stage we have applied SVM classification model on whole images and the performance is analyzed by plotting Free Response Operating Characteristic (FROC) curves. SVM classifiers achieved 96% sensitivity with 9.6 false positives per image in detection of spiculated mass and 97% sensitivity with 6.6 false positives per image while detecting AD in digital mammograms.
Dual Modality: Mammogram and Ultrasound Feature Level Fusion for Characterization of Breast Mass
Minavathi,Dr. Murali. S.,,Dr. Dinesh M.S.
International Journal of Innovative Technology and Exploring Engineering , 2013,
Abstract: detection of abnormalities in breast is done in different phases using different modalities and different biomedical techniques. These techniques and modalities are able to furnish morphological, metabolic and functional information of breast. Integrating these information assists in clinical decision making. But it is difficult to retrieve all these information from single modality. Multimodal techniques supply complementary information for improved therapy planning. This work concentrates on early detection of breast cancer which characterises the breast mass as malignant or benign by investigating the features retrieved from dual modalities: mammograms and Ultrasound. Architectural distortion (AD) with Spiculated mass is an important finding for the early detection of breast cancer. Such distortions can be classified as spiculation, retraction, and distortion which can be detected in mammograms. Spiculated masses carry a much higher risk of malignancy than calcifications or other types of masses. The proposed approach is based on the fusion of two modalities at feature extraction level with Z-Score Normalization technique to improve the performance of dual modality. Gabor filters are used to retrieve texture features from region of interest (ROI) of mammograms. Shape and structural features are retrieved from ROI’s of Ultrasound. In addition to that some other discriminative features like denseness texture feature, standard deviation, entropy and homogeneity are also extracted from ROI’s of both modalities. Feature level fusion is then achieved by using a simple concatenation rule. Finally classification is done using Support vector machine (SVM) classifiers to classify breast mass as malignant or benign. Receiver operating characteristic curves (ROC) are used to evaluate the performance. SVM classifiers achieved 95.6% sensitivity in characterising the breast masses using the features retrieved from two modalities.
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