Texture based classification of mass abnormalities in mammograms

Baeg, S.; Kehtarnavaz, Nasser

doi:10.1109/cbms.2000.856894

Cited by 18 publications

(6 citation statements)

References 10 publications

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“…The major advantage of this filter compared to earlier approaches [6][7][8][9] is its ability to eliminate noise without affecting the intensity characteristics of mammographic image. One of the most common approaches to segment mammograms is by utilizing the statistical distribution of their intensities.…”

Section: F(d) Is the Fuzzy Function Defined In (1) ( ) Is The Origmentioning

confidence: 99%

“…There are several works on mammographic-image preprocessing. Baeg et.al [6] used gamma correction for mammographic enhancement. Based on their texture-analysis method, classification of 150 biopsy-proven masses into benign and malignant classes resulted in an area under Receiver Operating Characteristic (ROC) curve of 0.91.…”

Section: Related Workmentioning

confidence: 99%

“…Previous works on mammographic-image preprocessing used methods such as Gamma correction [6], adaptive 2D Wiener filtering [7], contrast-limited adaptive histogram equalization (CLAHE) [8], and multi-scale wavelet-based enhancement [9]. It is a difficult task to choose the single best technique for image enhancement, and the enhancement most often is evaluated based on the performance of the subsequent segmentation algorithm [10].…”

Section: Introductionmentioning

confidence: 99%

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A New Preprocessing Filter for Digital Mammograms

Rahmati

Hamarneh

Nussbaum

et al. 2010

Lecture Notes in Computer Science

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Abstract. This paper presents a computer-aided approach to enhancing suspicious lesions in digital mammograms. The developed algorithm improves on a well-known preprocessor filter named contrast-limited adaptive histogram equalization (CLAHE) to remove noise and intensity inhomogeneities. The proposed preprocessing filter, called fuzzy contrast-limited adaptive histogram equalization (FCLAHE), performs non-linear enhancement. The filter eliminates noise and intensity inhomogeneities in the background while retaining the natural gray level variations of mammographic images within suspicious lesions. We applied Catarious segmentation method (CSM) to compare the segmentation accuracy in two scenarios: when there is no preprocessing filter, and when the proposed preprocessing filter is applied to the original image. The proposed filter has been evaluated on 50 real mammographic images and the experimental results show an average increase of segmentation accuracy by 14.16% when the new filter is applied.

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Section: F(d) Is the Fuzzy Function Defined In (1) ( ) Is The Origmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Preprocessing Filter for Digital Mammograms

Rahmati

Hamarneh

Nussbaum

et al. 2010

Lecture Notes in Computer Science

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“…Artificial neural network is a simplified imitation of the central nervous system and thus, inspired by the types of computation performed by the human brain [24][25][26]. ANN is a massively parallel-distributed system made up of highly interconnected processing elements called nodes or neurons, working in unison to solve a specific problem.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Classification of Masses in Digital Mammograms Using Firefly based Optimization

Thawkar¹,

Ingolikar²

2018

IJIGSP

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Breast cancer is one of the leading causes of death in women all over the world. Computer based diagnosis system assists radiologist in the effective treatment of breast cancer. To design an efficient classification system for masses in digital mammograms, we have to use efficient algorithms for feature selection to reduce the feature space of mammogram classification problem. The proposed study explores the use of Firefly algorithm to select a subset of features. Artificial neural network and support vector machine classifiers are employed to evaluate fitness of the selected features. Features selected by Firefly algorithm are used to classify masses into benign and malignant, using artificial neural network and support vector machine classifiers. The proposed method employed over 651 mammograms obtained from the Database of Digitized Screen-film Mammograms. Classification results show that Firefly algorithm with artificial neural network is superior to Firefly algorithm with support vector machine. Artificial neural network achieves accuracy of 95.23% with 94.43% sensitivity, 93.94% specificity and area under curve Az=0.965±0.008. On the other hand, support vector machine classifier achieves an accuracy of 92.47% with 96.14% sensitivity, 88.53% specificity and area under curve Az=0.951±0.009.Results obtained with Firefly algorithm shows that it will be useful for effective treatment of breast cancer.

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“…ANN is a simplified model of biological neural Network [33,34,35]. It is the massively parallel distributed system which consists of large number of processing elements called nodes or neurons.…”

Section: Artificial Neural Networkmentioning

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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Texture based classification of mass abnormalities in mammograms

Cited by 18 publications

References 10 publications

A New Preprocessing Filter for Digital Mammograms

A New Preprocessing Filter for Digital Mammograms

Classification of Masses in Digital Mammograms Using Firefly based Optimization

Automatic Detection and Classification of Masses in Digital Mammograms

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