Appearance-based Salient Features Extraction in Medical Images Using Sparse Contourlet-based Representation

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Rami Zewail 1,* Ahmed Hag-ElSafi 2

1. University of Alberta, Edmonton, Alberta, Canada

2. Smart Empower Innovation Labs Inc., Edmonton, Alberta, Canada

* Corresponding author.


Received: 12 May 2017 / Revised: 7 Jun. 2017 / Accepted: 5 Jul. 2017 / Published: 8 Sep. 2017

Index Terms

Salient features, multiscale, appearance, sparse, contourlet


Medical experts often examine hundreds of x-ray images searching for salient features that are used to detect pathological abnormalities. Inspired by our understanding of the human visual system, automated salient features detection methods have drawn much attention in the medical imaging research community. However, despite the efforts, detecting robust and stable salient features in medical images continues to constitute a challenging task. This is mainly attributed to the complexity of the anatomical structures of interest which usually undergo a wide range of rigid and non-rigid variations.
In this paper, we present a novel appearance-based salient feature extraction and matching method based on sparse Contourlet-based representation. The multi-scale and directional capabilities of the Contourlets is utilized to extract salient points that are robust to noise, rigid and non-rigid deformations. Moreover, we also include prior knowledge about local appearance of the salient points of the structure of interest. This allows for extraction of robust stable salient points that are most relevant to the anatomical structure of interest. 

Cite This Paper

Rami Zewail, Ahmed Hag-ElSafi,"Appearance-based Salient Features Extraction in Medical Images Using Sparse Contourlet-based Representation", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.9, pp.1-10, 2017. DOI: 10.5815/ijigsp.2017.09.01


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