MRT letter: Quantum noise removal and classification of breast mammogram images.

Because of the limitations of the X-ray hardware systems in mammogram machines, the quality of the breast mammogram images may undergo from poor resolution or low contrast. Quantum noise occurs in the mammogram images during acquisition due to low-count X-ray photons. In this work, an adaptive frost filter has been used to remove quantum noise. Local binary patterns have been extracted to classify breast mammograms into benign and malignant using different classifiers. Results show the superiority of the proposed algorithm in terms of sensitivity, specificity, and accuracy. Mammographic Institute Society Analysis database of mammography has been used for experimentation. Peak signal-to-noise ratio and structural similarity index measure are used to test the validity of adaptive frost filter. Experiment results show that proposed technique produces better results.

Morphological gradient based adapted selective filter for removal of Rician noise from magnetic resonance images.

Feature/edge-preserving noise removal techniques have a strong potential in several application domains including medical image processing. Magnetic resonance (MR) images have a tendency to gain Rician noise during acquisition. In this article, we have presented genetic algorithms based adapted selective non-local means (GASNLM) filter-based scheme for noise suppression of MR images while preserving the image features as much as possible. We have applied GASNLM filter with optimal parameter values for different frequency image regions to remove the noise. Filter parameter values are optimized by genetic algorithm (GA). A change in NLM filter known as selective weight matrix is also proposed to preserve the image features. The results prove soundness of the method. We have compared results with many well known and latest techniques, and the improvements are discussed.

Tumor detection from enhanced magnetic resonance imaging using fuzzy curvelet.

Effective medical image analysis is possible by the use of technique known as segmentation. Segmentation is a very challenging task because there is not any standard segmentation method is available for any medical application. In this article, we have proposed an automatic brain MR image segmentation method. Fast discrete curvelet transform and spatial fuzzy C-mean algorithm is used for noise removal and segmentation of brain MR image. Fuzzy entropy has been used for calculating adaptive and optimal threshold to separate out the image segments. Our proposed system is exclusively based on the information contained by the image itself. No extra information and no human intervention are required in our proposed system. We have tested our proposed system on different T1, T2 and PD brain MR images.

MRT letter: segmentation and texture-based classification of breast mammogram images.

Breast cancer is the most common cancer diagnosed among women. In this article, support vector machine is used to classify digital mammogram images into malignant and benign. Wiener filter is used to handle the possible quantum noise, which is more likely to occur in mammograms. Stack-based connected component method is proposed for background removal, and the image is enhanced using retinax method. Seeded region growing algorithm is used to remove the pectoral muscle part of the mammogram. We have extracted 13 different multidomains' features for classification. Results show the superiority of the proposed algorithm in terms of sensitivity, specificity, and accuracy. We have used MIAS database of mammography for experimentation.