Patient oriented graph-based image segmentation

Despite increased image quality including medical imaging, image segmentation continues to represent a major bottleneck in practical applications due to noise and lack of contrast. In this paper, we present a new methodology to segment noisy, low contrast medical images, with a view to developing practical applications. Firstly, the contrast of the image is enhanced and then a modified graph-based method is followed. This paper has mainly two contributions: (1) a contrast enhancement stage performed by suitably utilizing the noise present in the medical data. This step is achieved through stochastic resonance theory applied in the wavelet domain and (2) a new weighting function is proposed for traditional graph-based approaches. Both qualitative (by our clinicians/radiologists) and quantitative evaluation performed on publicly available computed tomography (CT) (MICCAI 2007 Grand Challenge workshop database) and cardiac magnetic resonance (CMR) databases reflect the potential of the proposed method even in the presence of tumors/papillary muscles.

► We present a new methodology to segment low contrast medical images with tumors/papillary muscles. ► This method is tested positive on CT and MR images. ► This paper has mainly two contributions: (1) contrast enhancement for the low contrast input image and (2) a new weighting function for graph-based approaches. ► An extensive validation of this method on publicly available databases has been conducted. ► This method has been compared with some well-known graph based methods.