Automatic detection of the tumour on mammogram images based on hidden Markov and active contour with quasi-automatic initialisation Online publication date: Mon, 02-Oct-2017
by Soukaina El Idrissi El Kaitouni; Abdelghafour Abbad; Hamid Tairi
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 9, No. 4, 2017
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Medical Engineering and Informatics (IJMEI):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook