In this paper, we present an efficient fractal method for detection and diagnosis of mass lesion in mammogram which is one of the abnormalities in mammographic images. We used 110 images that were carefully selected by a radiologist, and their abnormalities were also confirmed by biopsy. These images included circumscribed benign, illdefined, and spiculated malignant masses. Firstly, we discriminated lesions automatically using new fractal dimensions. The results which were examined by different types of breast density showed that the proposed method was able to yield quite satisfactory detection results. Secondly, noting that contours of masses playing the most important role in diagnosis of different mass types, we defined new fractal features based on information extraction from the contours. This information is able to identify the roughness in mass contours and determines the extent of spiculation or smoothness of the masses. In this manner, in classification of the spiculated malignant masses from the circumscribed benign tumors, we achieved highly satisfactory results, i.e., 0.98 measured in terms of area under ROC curve (AUC). In this paper, it is also shown that the roughness in contours is a suitable characteristic feature for diagnosis of ill-defined malignant tumors with AUC equal to 0.94 in their classification. The extracted information was also found to be useful in the classification of early malignancies whereas in the classification of spiculated and ill-defined malignant masses in their early stage from those of benign tumors, we achieved high accuracy of 0.99 and 0.90 for AUC, respectively.

In this paper a novel approach for ocular artifact (OA) removal is proposed in which a combination of Independent Component Analysis and wavelet-based noise reduction is utilized for detection and removal of OA. At the first stage, independent basis functions attributed to OA are computed using FastICA algorithm. This is followed by designing a wavelet basis function which is tuned to have sufficient similarity in its waveform to the independent basis functions of OA. We then utilize the designed wavelet for signal decomposition in a standard discrete wavelet transform where by deleting the approximation and summing up the details of signal decomposition, we arrive at a sufficiently artifact-free EEG signal. The approach excludes thresholding challenges of wavelets and works both for eye blinks and eye movements. Applying our algorithm to 420 4-s EEG epochs, the method exhibits high performance for the removal of OA artifacts. Our wavelet design method for noise reduction can be extended to the removal other types of EEG artifacts Keywords-discrete wavelet transform (DWT), independent component analysis (ICA), wavelet design, electroencephalogram (EEG), ocular artifact (OA)

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PurposeThe purpose of this paper is to reduce the destructive effects of existing unavoidable noises contaminating temperature data in inverse heat conduction problems (IHCP) utilizing the wavelets.Design/methodology/approachFor noise reduction, sensor data were treated as input to the filter bank used for signal decomposition and implementation of discrete wavelet transform. This is followed by the application of wavelet denoising algorithm that is applied on the wavelet coefficients of signal components at different resolution levels. Both noisy and de‐noised measurement temperatures are then used as input data to a numerical experiment of IHCP. The inverse problem deals with an estimation of unknown surface heat flux in a 2D slab and is solved by the variable metric method.FindingsComparison of estimated heat fluxes obtained using denoised data with those using original sensor data indicates that noise reduction by wavelet has a potential to be a powerful tool for improvement of IHCP results.Originality/valueNoise reduction using wavelets, while it can be implemented very easily, may also significantly relegate (or even eliminate) conventional regularization schemes commonly used in IHCP.