Title: Abnormality identification of breast mammogram image segmentation with iterative restricted mode algorithm

Authors: V. Nagi Reddy; P. Subba Rao

Addresses: Vignan Foundation for Science Technology and Research (Deemed to be University), Vadlamudi, Guntur, India ' Department of Information Technology, Vignan Foundation for Science Technology and Research (Deemed to be University), Vadlamudi, Guntur, India

Abstract: The breast image division is a regular issue in medicinal picture preparing. For the scientist to remove the data with great determination without loss of points of interest. In this paper, we propose a division strategy by utilising the iterative restricted mode (IRM) calculation and Markov random field (MRF) model to recognise the variation from the norm in mammogram pictures. For all cycles the most reduced vitality name making is permitted by IRM. This strategy takes after the high compacted connection between limit name MRF's. In this model is tried with five pictures and assessment is done utilising target assessment criteria, namely the Jaccard coefficient (JC) and volumetric similarity (VS), variation of information (VOI), global consistency error (GCE) and probability rand index (PRI). By utilising image quality measurements the execution assessment of divided pictures likewise assessed. The reproduced comes about proposed by utilizing T₁ weighted pictures are contrasted and the current model.

Keywords: breast images; iterative restricted mode; IRM; Markov random field; MRF; image segmentation; kernel; quality metrics.

DOI: 10.1504/IJAIP.2024.141526

International Journal of Advanced Intelligence Paradigms, 2024 Vol.29 No.1, pp.72 - 85

Received: 05 Oct 2018
Accepted: 27 Nov 2018
Published online: 23 Sep 2024 *

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