Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms

Ramos, Renato Moran; Nascimento, Marcelo Zanchetta do; Pereira, Danilo César

doi:10.1016/j.eswa.2012.03.020

Cited by 46 publications

(25 citation statements)

References 34 publications

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“…By comparing the performance among different types of spatial features, it is found that features based on statistics of Log-Gabor responses and the gradients often lead to better results than statistics of locally normalized panchromatic image, as shown in Section 4.4. It is worth noting that some other filters, such as wavelet and ridgelet [30,31], are also effective in texture analysis, extracting quality-sensitive features using these filters may lead to a better result. …”

Section: Discussionmentioning

confidence: 99%

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

et al. 2017

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Abstract:Assessing the quality of a reconstructed hyperspectral image (HSI) is of significance for restoration and super-resolution. Current image quality assessment methods such as peak signal-noise-ratio require the availability of pristine reference image, which is often not available in reality. In this paper, we propose a no-reference hyperspectral image quality assessment method based on quality-sensitive features extraction. Difference of statistical properties between pristine and distorted HSIs is analyzed in both spectral and spatial domains, then multiple statistics features that are sensitive to image quality are extracted. By combining all these statistics features, we learn a multivariate Gaussian (MVG) model as benchmark from the pristine hyperspectral datasets. In order to assess the quality of a reconstructed HSI, we partition it into different local blocks and fit a MVG model on each block. A modified Bhattacharyya distance between the MVG model of each reconstructed HSI block and the benchmark MVG model is computed to measure the quality. The final quality score is obtained by average pooling over all the blocks. We assess five state-of-the-art super-resolution methods on Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Hyperspec-VNIR-C (HyperspecVC) data using our proposed method. It is verified that the proposed quality score is consistent with current reference-based assessment indices, which demonstrates the effectiveness and potential of the proposed no-reference image quality assessment method.

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Section: Discussionmentioning

confidence: 99%

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

et al. 2017

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“…In (Ramos et al 2012) authors deal with an evaluation of texture classification, they present a comparative study for the use of co-occurrence matrices, wavelet and ridgelet transforms in texture analysis of mammographic images. Experiments are performed on a data set of 120 craniocaudal mammograms, half containing a mass and half with no lesions.…”

Section: Textural Features Of Mammogram Images Massesmentioning

confidence: 99%

The power laws: Zipf and inverse Zipf for automated segmentation and classification of masses within mammograms

2014

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Breast cancer is becoming the leading form of cancer among women worldwide, indeed, there are no effective ways to prevent this disease at present, therefore, it's early screening and detection is the key to rise the success of treatment, hence, the reduce of the associated mortality rates. This work aims to improve the performance of the current computer-aided detection and diagnosis approaches (CADe/CADx) of breast cancer which involve the application of the computer technology in mammograms analysis and understanding; for this purpose, we deal with the power laws: Zipf and inverse Zipf. The originality of this research lays in the contribution of the power laws for mammograms analysis; it is the first attempt to use them in the field of mammograms masses segmentation and classification, indeed, these laws characterize the structural complexity of texture within mammograms and provide the curves of Zipf and inverse Zipf which carry significant information that could be used to mammograms masses detection and classification along a new set of textural features extracted from the curves of Zipf and inverse Zipf. According to our experiments conducted on a mammogram database used in the framework of a bilateral project between our university and the hospital CHU at Algeria, we can assert that our approach based Zipf's and inverse Zipf's laws is a powerful and efficient approach for automated mammograms masses detection and classification.

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“…The Euclidean distance is then used to construct a supervised classifier. Ramos et al (2012), evaluated the texture classification using features derived from co-occurrence matrices, wavelet and ridgelet transforms of mammographic images. A false positive reduction in computer-aided detection of masses is also proposed.…”

Section: Ajeasmentioning

confidence: 99%

A Robust Approach to Classify Microcalcification in Digital Mammograms Using Contourlet Transform and Support Vector Machine

Jasmine¹

2013

American Journal of Engineering and Applied Sciences

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Mammogram is the best available radiographic method to detect breast cancer in the early stage. However detecting a microcalcification clusters in the early stage is a tough task for the radiologist. Herein we present a novel approach for classifying microcalcification in digital mammograms using Nonsubsampled Contourlet Transform (NSCT) and Support Vector Machine (SVM). The classification of microcalcification is achieved by extracting the microcalcification features from the Contourlet coefficients of the image and the outcomes are used as an input to the SVM for classification. The system classifies the mammogram images as normal or abnormal and the abnormal severity as benign or malignant. The evaluation of the system is carried on using Mammography Image Analysis Society (MIAS) database. The experimental result shows that the proposed method provides improved classification rate.

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Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms

Cited by 46 publications

References 34 publications

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

The power laws: Zipf and inverse Zipf for automated segmentation and classification of masses within mammograms

A Robust Approach to Classify Microcalcification in Digital Mammograms Using Contourlet Transform and Support Vector Machine

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