Echocardiographic images are inherent with speckle noise which makes visual reading and analysis quite difficult. The multiplicative speckle noise masks finer details, necessary for diagnosis of abnormalities. A novel speckle reduction technique based on integration of geometric, wiener, and fuzzy filters is proposed and analyzed in this paper. The denoising applications of fuzzy filters are studied and analyzed along with 26 denoising techniques. It is observed that geometric filter retains noise and, to address this issue, wiener filter is embedded into the geometric filter during iteration process. The performance of geometric-wiener filter is further enhanced using fuzzy filters and the proposed despeckling techniques are called integrated fuzzy filters. Fuzzy filters based on moving average and median value are employed in the integrated fuzzy filters. The performances of integrated fuzzy filters are tested on echocardiographic images and synthetic images in terms of image quality metrics. It is observed that the performance parameters are highest in case of integrated fuzzy filters in comparison to fuzzy and geometric-fuzzy filters. The clinical validation reveals that the output images obtained using geometric-wiener, integrated fuzzy, nonlocal means, and details preserving anisotropic diffusion filters are acceptable. The necessary finer details are retained in the denoised echocardiographic images.
PurposeBreast cancer is one of the most common malignant tumors in women, which badly have an effect on women's physical and psychological health and even danger to life. Nowadays, mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer. Though, due to the intricate formation of mammogram images, it is reasonably hard for practitioners to spot breast cancer features.Design/methodology/approachBreast cancer is one of the most common malignant tumors in women, which badly have an effect on women's physical and psychological health and even danger to life. Nowadays, mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer. Though, due to the intricate formation of mammogram images, it is reasonably hard for practitioners to spot breast cancer features.FindingsThe performance analysis was done for both segmentation and classification. From the analysis, the accuracy of the proposed IAP-CSA-based fuzzy was 41.9% improved than the fuzzy classifier, 2.80% improved than PSO, WOA, and CSA, and 2.32% improved than GWO-based fuzzy classifiers. Additionally, the accuracy of the developed IAP-CSA-fuzzy was 9.54% better than NN, 35.8% better than SVM, and 41.9% better than the existing fuzzy classifier. Hence, it is concluded that the implemented breast cancer detection model was efficient in determining the normal, benign and malignant images.Originality/valueThis paper adopts the latest Improved Awareness Probability-based Crow Search Algorithm (IAP-CSA)-based Region growing and fuzzy classifier for enhancing the breast cancer detection of mammogram images, and this is the first work that utilizes this method.
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