A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation

Eltoukhy, Mohamed Meselhy; Faye, Ibrahima; Belhaouari, Samir Brahim

doi:10.1016/j.compbiomed.2011.10.016

Cited by 130 publications

(28 citation statements)

References 19 publications

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“…Moreover, when combining the two datasets together, the accuracies achieved using the Adaboosting of J48 DT, wrapper k-NN based on a random search and wrapper k-NN based on the best first search were 99.7%, 99.7%, and 100%. These accuracies were greater than 95.84% and 95.98% of the method proposed by El-Toukhy et al [47] using the wavelet and curvelet feature extraction methods.…”

Section: Discussionmentioning

confidence: 56%

“…In the feature extraction step, some statistical features were extracted and Finally, the proposed CAD system has been compared with other papers in the field that have the same conditions to prove the efficiency of the proposed method as shown in Table 11. Regarding the MIAS dataset, it was clear that the results have shown that the proposed CAD system recorded the highest accuracy and AUC compared to El-Toukhy et al [47], Beura et al [15], Pawar and Talbar [16], Phadke and Rege [48], and Mohanty et. al.…”

Section: Discussionmentioning

confidence: 90%

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Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

2019

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Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100 %, which was achieved with k‐NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples.

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Section: Discussionmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 90%

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

2019

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“…The diagnosis of breast cancer using mammogram by radiologist varies from expert to expert as symptoms are misinterpreted or overlooked, due to the tedious task of screening mammograms. Study reveals that 10% to 30% of the visible cancers on mammograms are overlooked, and only 20% to 30% of biopsies are positive [ 6 – 8 ]. Biopsies are traumatic in nature and costly; therefore, computer aided detection and diagnosis (CAD) systems combined with expert radiologists' experience would provide more comprehensive diagnosis [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Pawar¹,

Talbar

Dudhane

2018

Journal of Healthcare Engineering

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Breast Cancer is the most prevalent cancer among women across the globe. Automatic detection of breast cancer using Computer Aided Diagnosis (CAD) system suffers from false positives (FPs). Thus, reduction of FP is one of the challenging tasks to improve the performance of the diagnosis systems. In the present work, new FP reduction technique has been proposed for breast cancer diagnosis. It is based on appropriate integration of preprocessing, Self-organizing map (SOM) clustering, region of interest (ROI) extraction, and FP reduction. In preprocessing, contrast enhancement of mammograms has been achieved using Local Entropy Maximization algorithm. The unsupervised SOM clusters an image into number of segments to identify the cancerous region and extracts tumor regions (i.e., ROIs). However, it also detects some FPs which affects the efficiency of the algorithm. Therefore, to reduce the FPs, the output of the SOM is given to the FP reduction step which is aimed to classify the extracted ROIs into normal and abnormal class. FP reduction consists of feature mining from the ROIs using proposed local sparse curvelet coefficients followed by classification using artificial neural network (ANN). The performance of proposed algorithm has been validated using the local datasets as TMCH (Tata Memorial Cancer Hospital) and publicly available MIAS (Suckling et al., 1994) and DDSM (Heath et al., 2000) database. The proposed technique results in reduction of FPs from 0.85 to 0.02 FP/image for MIAS, 4.81 to 0.16 FP/image for DDSM, and 2.32 to 0.05 FP/image for TMCH reflecting huge improvement in classification of mammograms.

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“…Using a wavelet-based space-scale method (Arneodo et al, 2003 ), we show that the long-range correlations and anti-correlations respectively observed in the roughness fluctuations of the mammograms of dense and fatty normal breasts, strikingly vanish in the malignant tumor region. Note that most existing computer-aided diagnostic (CAD) methods (Karahaliou et al, 2008 ; Ayer et al, 2010 ; Tsai et al, 2011 ; Häberle et al, 2012 ; Meselhy Eltoukhy et al, 2012 ) are designed for texture analysis or feature extraction with the prerequisite that the background roughness fluctuations of normal breast mammograms are homogeneous and uncorrelated. In contrast, we propose characterizing correlations in mammogram roughness fluctuations via the estimate of a density fluctuation index H as an innovative and effective discrimination method that will assist in early breast cancer detection.…”

Section: Introductionmentioning

confidence: 99%

Comparative Multifractal Analysis of Dynamic Infrared Thermograms and X-Ray Mammograms Enlightens Changes in the Environment of Malignant Tumors

et al. 2016

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There is growing evidence that the microenvironment surrounding a tumor plays a special role in cancer development and cancer therapeutic resistance. Tumors arise from the dysregulation and alteration of both the malignant cells and their environment. By providing tumor-repressing signals, the microenvironment can impose and sustain normal tissue architecture. Once tissue homeostasis is lost, the altered microenvironment can create a niche favoring the tumorigenic transformation process. A major challenge in early breast cancer diagnosis is thus to show that these physiological and architectural alterations can be detected with currently used screening techniques. In a recent study, we used a 1D wavelet-based multi-scale method to analyze breast skin temperature temporal fluctuations collected with an IR thermography camera in patients with breast cancer. This study reveals that the multifractal complexity of temperature fluctuations superimposed on cardiogenic and vasomotor perfusion oscillations observed in healthy breasts is lost in malignant tumor foci in cancerous breasts. Here we use a 2D wavelet-based multifractal method to analyze the spatial fluctuations of breast density in the X-ray mammograms of the same panel of patients. As compared to the long-range correlations and anti-correlations in roughness fluctuations, respectively observed in dense and fatty breast areas, some significant change in the nature of breast density fluctuations with some clear loss of correlations is detected in the neighborhood of malignant tumors. This attests to some architectural disorganization that may deeply affect heat transfer and related thermomechanics in breast tissues, corroborating the change to homogeneous monofractal temperature fluctuations recorded in cancerous breasts with the IR camera. These results open new perspectives in computer-aided methods to assist in early breast cancer diagnosis.

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A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation

Cited by 130 publications

References 19 publications

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Comparative Multifractal Analysis of Dynamic Infrared Thermograms and X-Ray Mammograms Enlightens Changes in the Environment of Malignant Tumors

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