Automated mammogram classification using a multiresolution pattern recognition approach

Ferreira, Cristiane B. R.; Borges, Díbio Leandro

doi:10.1109/sibgrapi.2001.963040

Cited by 12 publications

(12 citation statements)

References 5 publications

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“…The benign masses have a rounded appearance with a defined boundary, while the inside of the mass is relatively uniform and radiolucent. This has also been noted by other others, see Ferreira & Borges (2001) Rangayyan et al (1997) Mudigonda et al (2000). In contrast, the malignant masses possess ill-defined boundaries, are of higher density (radiopaque) and have an overall nonuniform appearance in comparison to the benign lesions.…”

Section: Biomedical Imagerysupporting

confidence: 85%

“…The only apriori assumption is a very general one: the texture between normal tissue and pathology is different. The first modality, mammography, is an imaging technology which acquires an x-ray image of the breast Ferreira & Borges (2001). They are currently the most effective method for early detection of breast cancers Cheng et al (2006) Wei et al (1995).…”

Section: Biomedical Imagerymentioning

confidence: 99%

“…Incorrectly identifying the lesion type results in negative to positive biopsies ratios as high as 11:1 in some clinics Rangayyan et al (1997). Normal tissue masks the lesions and breast parenchyma is much more prominent than the lesion itself Ferreira & Borges (2001). To test the CAD system with mammography images, a database is used where images contain either a benign or malignant lesion(s).…”

Section: Biomedical Imagerymentioning

confidence: 99%

See 2 more Smart Citations

Shift-Invariant DWT for Medical Image Classification

Khademi¹,

Krishnan²,

Venetsanopoulos³

2011

Discrete Wavelet Transforms - Theory and Applications

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Section: Biomedical Imagerysupporting

confidence: 85%

Section: Biomedical Imagerymentioning

confidence: 99%

Section: Biomedical Imagerymentioning

confidence: 99%

See 1 more Smart Citation

Shift-Invariant DWT for Medical Image Classification

Khademi¹,

Krishnan²,

Venetsanopoulos³

2011

Discrete Wavelet Transforms - Theory and Applications

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“…Ferreira and Borges (2001) cropped the images into 64×64 pixels, while Prathiba and Sadasivam (2010) used an ROI of 32×32 pixels. Lowis et al (2015) and Karahaliou et al (2008) conducted their research by using an ROI of 128×128 pixels.…”

Section: Mammogram Acquisition and Preprocessingmentioning

confidence: 99%

“…Ferreira and Borges (2001) used the biggest wavelet coefficient as the input of the single nearest-neighbor classification method to classify mammogram images as benign or malignant. They obtained the 100 greatest coefficients of the decomposed image using wavelets Haar and Daubechies-4 in the first level of decomposition.…”

Section: Introductionmentioning

confidence: 99%

Classification of Digital Mammogram based on Nearest-Neighbor Method for Breast Cancer Detection

Nusantara¹,

Purwanti²,

Soelistiono³

2016

IJTech

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Breast cancer can be detected using digital mammograms. In this research study, a system is designed to classify digital mammograms into two classes, namely normal and abnormal, using the k-Nearest Neighbor (kNN) method. Prior to classification, the region of interest (ROI) of a mammogram is cropped, and the feature is extracted using the wavelet transformation method. Energy, mean, and standard deviation from wavelet decomposition coefficients are used as input for the classification. Optimal accuracy is obtained when wavelet decomposition level 3 is used with the feature combination of mean and standard deviation. The highest accuracy, sensitivity, and specificity of this method are 96.8%, 100%, and 95%, respectively.

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Associative classification of mammograms using weighted rules

Dua

Singh

Thompson

2009

Expert Systems with Applications

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In this paper, we present a novel method for the classification of mammograms using a unique weighted association rule based classifier. Images are preprocessed to reveal regions of interest. Texture components are extracted from segmented parts of the image and discretized for rule discovery. Association rules are derived between various texture components extracted from segments of images, and employed for classification based on their intra-and inter-class dependencies. These rules are then employed for the classification of a commonly used mammography dataset, and rigorous experimentation is performed to evaluate the rules' efficacy under different classification scenarios. The experimental results show that this method works well for such datasets, incurring accuracies as high as 89%, which surpasses the accuracy rates of other rule based classification techniques.

show abstract

Automated mammogram classification using a multiresolution pattern recognition approach

Cited by 12 publications

References 5 publications

Shift-Invariant DWT for Medical Image Classification

Shift-Invariant DWT for Medical Image Classification

Classification of Digital Mammogram based on Nearest-Neighbor Method for Breast Cancer Detection

Associative classification of mammograms using weighted rules

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