Automatic detection of the tumour on mammogram images based on hidden Markov and active contour with quasi-automatic initialisation

Abstract

The area of tumour's detection and removal is a very active research within the field of medical imaging. In the present work, we present an automatic method for tumour's detection in mammography images. The proposed approach is to construct a detection pattern which starts with the Otsu's method: the thresholding step, followed by estimating the number of classes based on the local binary pattern (LBP) technique. To automate the initialisation task, we proposed to apply the classification by the k-means dynamic improved by Markov's method. The tumour's image is the result of the maximum correlation. A second contribution which is based on active contours gradient vector flow (GVF) with quasi-automatic initialisation applied on the structure that resulted from the structure/texture decomposition of the image to classify. The experimental results show the quality and automation of tumour's detection in medical images in comparison to literature methods.