No abstract

Breast cancer is the leading deadly cancer and most commonly diagnosed in women. New technologies in supplement to existing imaging modalities improve breast cancer screening. This article contributes to identify the high potential device that suggested high accuracy and reliable tool for breast screening and also to examine new screening modalities. An improved imaging system which ensures early detection, non-invasive and radiation free is expected in diagnosis. Numerous imaging modalities like positron emission tomography/computed tomography (PET/CT) imaging, ultrasound, magnetic resonance imaging (MRI), thermography, electrical impedance tomography and few others with recent developments show great potential for diagnosis. Some of the techniques aim for lesion detection and characterization with increased specificity and accuracy. In this paper, the capabilities of traditional and emerging breast imaging modalities used in breast cancer screening are summarized and their advantages and disadvantages are discussed.

No abstract

No abstract

Abstract-In modern times, video object segmentation has emerged as one of the most imperative and challenging area of research. The principal objective of video object segmentation is to facilitate content-based representation by extracting objects of interest from a series of consecutive video frames. Recently, a number of video object segmentation algorithms have been discussed and unfortunately most existing segmentation algorithms are not adequate and robust enough to process noisy video sequences. Competence of most segmentation techniques is affected by the presence of noise in frames which is a critical issue of edge preservation. This paper presents a novel video object segmentation approach for noisy color video sequences towards effective video retrieval. Initially, the noisy video frames are denoised using a strategy based on an enhanced sparse representation in transform domain. Afterwards, the background is estimated from the denoised frames using the Expectation Maximization (EM) algorithm. Then, the foreground objects i.e.) moving video objects are segmented with the aid of the novel approach presented. The biorthogonal wavelet transform and the L2 norm distance measure are employed in the foreground object segmentation. The experimental results demonstrate the effectiveness of the presented approach in segmenting the video objects from noisy color video sequences.