Neural classification of abnormal tissue in digital mammography using statistical features of the texture

Christoyianni, Ioanna; Dermatas, Evangelos; Kokkinakis, G.

doi:10.1109/icecs.1999.812237

Cited by 24 publications

(26 citation statements)

References 6 publications

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“…The KNN has 85.7% specificity and 84.6% sensitivity. The accuracy of BNN was determined by area under ROC curve 0.923.Christoyianni et al [9] investigate the use of RBF and MLP Net for the classification masses using 12 texture features. The total classification accuracy achieved for MLP was 84.03%, 4% higher than RBF.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection and Classification of Masses in Digital Mammograms

Thawkar¹,

Ingolikar²

2017

IJIES

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Breast Cancer is still one of the leading cancers in women. Mammography is the best tool for early detection of breast cancer. In this work methods for automatic detection and classification of masses into benign or malignant has been proposed. The suspicious masses are detected automatically by performing image segmentation with Otsu's global thresholding technique, morphological operations and watershed transformation. Twenty-five features based on intensity, texture and shape are extracted from each of the 651 mammograms obtained from Database of Digitized Screen-film Mammograms. The Eight most significant features selected by step-wise Linear Discriminate Analysis are used to classify masses using Fisher's Linear Discriminate Analysis, Support Vector Machine and Multilayer Perceptron with two training algorithms Levenberg-Marquardt and Bayesian Regularization. The performance evaluation of classifiers indicates that MLP is better than both LDA and SVM. MLP-RBF has 98.9% accuracy with area under Receiver Operating Characteristics curve AZ=0.98±0.007, MLP-LM 96.0% accuracy with AZ=0.97±0.007, SVM 91.4% accuracy with AZ=0.956±0.009 and LDA 90.3% accuracy with AZ=0.956±0.009. All the results achieved are promising when compared with some existing work.

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Section: Related Workmentioning

confidence: 99%

Automatic Detection and Classification of Masses in Digital Mammograms

Thawkar¹,

Ingolikar²

2017

IJIES

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“…Multilayer perceptron and radial basis function network have been used for classification of mammograms [9]. Different neural network algorithms are characterised by learning method and architecture of the network.…”

Section: Artificial Neural Network Classifiermentioning

confidence: 99%

“…Linear discriminant analysis is used in [8], where 76% sensitivity and 64% specificity was achieved on the mammographic dataset acquired from Michigan hospitals. Spatial grey level dependence matrix (SGLD) features along with artificial neural network were used to classify the regions of interest from miniMIAS dataset into normal and abnormal categories [9]. In this case 119 regions were used for the training (jack and knife method) and the remaining 119 regions of regions were used for testing purpose.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Technique Based on N-GRAM and Neural Networks for Classification of Mammographic Images

Kulkarni

Stranieri

Kulkarni

et al. 2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Various texture, shape, boundary features have been used previously to classify regions of interest in radiological mammograms into normal and abnormal categories. Although, bag-ofphrases or n-gram model has been effective in text representation for classification or retrieval of text, these approaches have not been widely explored for medical image processing. Our purpose is to represent regions of interest using an n-gram model, then deploy the n-gram features into a back-propagation trained neural network for classifying regions of interest into normal and abnormal categories. Experiments on the benchmark miniMIAS database show that

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“…Texture is generally defined as the group of primitive lines that form an image according to certain arrangement rules. 7 Christoyianni and colleagues 8,9 used gray-level histograms to detect tumors, but the features of such histograms ignore the spatial correlation in pixels. Haralick's group 10 proposed a co-occurrence matrix (also known as a gray-tone spatial-dependence matrix) to convert images to obtain features.…”

Section: Introductionmentioning

confidence: 99%

Combined Image Enhancement, Feature Extraction, and Classification Protocol to Improve Detection and Diagnosis of Rotator-cuff Tears on MR Imaging

Lin

Wang

et al. 2014

MRMS

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Purpose: The diagnosis of most rotator cuff tears (RCTs) relies upon magnetic resonance (MR) imaging, but direct capture of MR images without enhanced image processing leads to poor image contrast and potential misdiagnosis. Therefore, we developed a 2-stage model for the detection and diagnosis of injury of the supraspinatus tendon.Methods: The first stage used coupled weighted histogram separation (WHS) to improve image enhancement, and the second stage extracted suspicious texture, features of both spatial and spectral domains, and sequential floating forward selection (SFFS) selected features conducive to classification of RCTs. We then tested injuries of the supraspinatus tendon using the classifier.Results: The extraction of features by SFFS can increase detection of supraspinatus injury by reducing the input vector by 57.78% from the enhanced input images. The receiver operating characteristic (ROC) curve indicated an azimuth (Az) value of 84.38% when SFFS selected 76 features to construct a support vector machine (SVM) classifier from the enhanced images, compared with 56.94% when all 180 features from the raw input images were used for the construction.Conclusions: The performance of the classifier constructed by SFFS-selected features is superior to that using all features. These findings can serve as references to improve diagnosis and treatment of supraspinatus injuries.

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Neural classification of abnormal tissue in digital mammography using statistical features of the texture

Cited by 24 publications

References 6 publications

Automatic Detection and Classification of Masses in Digital Mammograms

Automatic Detection and Classification of Masses in Digital Mammograms

Hybrid Technique Based on N-GRAM and Neural Networks for Classification of Mammographic Images

Combined Image Enhancement, Feature Extraction, and Classification Protocol to Improve Detection and Diagnosis of Rotator-cuff Tears on MR Imaging

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