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Detection of Microcalcifications in Digital Mammogram Using Curvelet Fractal Texture Features

Vimal Kumar M N, Divya M, Ilakkia M, Jayanthi S, Gomathi V.

In this work, an attempt is made to Segment and find features of the segmented mammogram Images and finally mammogram images are classified as normal and abnormal. The mammogram images used for this work are considered from MIAS Database. The database includes 322 digitized films and all the images are of size 1024x1024. It consists of 322 images (208 normal images and 114 abnormal images). Initially, mammogram images are subjected to pre-processing using Discrete Cosine Transform to enhance the edges of the mammograms. Then, sharpened images are cl using the Fuzzy C-means Clustering algorithm. After segmentation, Curvelet coefficient and fractal Dimension values are obtained using Discrete Curvelet Transformand Fractal textures respectively. The average values of obtained curvelet coefficient and fractal dimension values for both normal and abnormal mammogram images are compared. Finally, The mammogram images are classified using an Ensemble Fully Complex-Valued Relaxation Network Classifier. The Classifier is used foe the classification of the mammogram images as normal and abnormal.

Key words: Mammography, DCT, MIAS, SFTA, FCRN, Fuzzy C Means.

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