Recent studies on wavelet transform and @entity3628 applied on mammograms for the detection of @entity5 tissues indicate that microcalcifications and masses can be utilized for the study of the morphology and diagnosis of @entity5 cases. It is shown that the use of @entity3628 , as applied to a given image, can clearly discern @entity5 zones from noncancerous areas. In this paper, for @entity3628 , the original image is first segmented into appropriate fractal boxes followed by identifying the fractal dimension of each windowed section using a computationally efficient two-dimensional box-counting algorithm. Furthermore, using appropriate wavelet sub-bands and image Reconstruction based on modified wavelet coefficients, it is shown that it is possible to arrive at enhanced features for detection of @entity5 zones. In this paper, we have attempted to benefit from the advantages of both fractals and wavelets by introducing a new algorithm. By using a new algorithm named F1W2, the original image is first segmented into appropriate fractal boxes, and the fractal dimension of each windowed section is extracted. Following from that, by applying a maximum level threshold on fractal dimensions matrix, the best-segmented boxes are selected. In the next step, the segmented Cancerous zones which are candidates are then decomposed by utilizing standard orthogonal wavelet transform and db2 wavelet in three different resolution levels, and after nullifying wavelet coefficients of the image at the first scale and low frequency band of the third scale, the modified reconstructed image is successfully utilized for detection of @entity0 regions by applying an appropriate threshold. For detection of @entity5 zones, our simulations indicate the accuracy of 90.9% for masses and 88.99% for microcalcifications detection results using the F1W2 method. For classification of detected mictocalcification into benign and malignant cases, eight features are identified and utilized in radial basis function neural network. Our simulation results indicate the accuracy of 92% classification using F1W2 method.

Detection and classification of XXXX in Wavelet Sub-bands of Fractal Segmented Cancerous Zones.

@entity5 :: ('MESH:D009369', 'Disease') :: ['cancerous'] @entity0 :: ('MESH:D001943', 'Disease') :: ['breast cancer'] @entity3628 :: ('MESH:D004195', 'Disease') :: ['fractal modeling']
@entity0 :: (MESH:D001943,Disease) :: ['Breast Cancer']
