Objectively measuring signal detectability, contrast, blur and noise in medical images using channelized joint observers

ABSTRACT To improve imaging systems and image processing techniques, objective image quality assessment is essential. Model observers adopting a task-based quality assessment strategy by estimating signal detectability measures, have shown to be quite successful to this end. At the same time, costly and time-consuming human observer experiments can be avoided. However, optimizing images in terms of signal detectability alone, still allows a lot of freedom in terms of the imaging parameters. More specifically, fixing the signal detectability defines a manifold in the imaging parameter space on which different “possible” solutions reside. In this article, we present measures that can be used to distinguish these possible solutions from each other, in terms of image quality factors such as signal blur, noise and signal contrast. Our approach is based on an extended channelized joint observer (CJO) that simultaneously estimates the signal amplitude, scale and detectability. As an application, we use this technique to design k-space trajectories for MRI acquisition. Our technique allows to compare the different spiral trajectories in terms of blur, noise and contrast, even when the signal detectability is estimated to be equal

Subjective and objective quality evaluation of compressed medical video sequences

Existing objective video quality metrics such as VQM from NTIA [1] and MOVIE [2] are known to perform well for assessing compression degradation in natural scene and broadcast television sequences but their suitability for the quality evaluation of compressed medical video has not been studied extensively. In this work we assess the quality of compressed medical video sequences using objective metrics and a subjective evaluation study conducted with non-expert subjects. Test sequences consist of High Definition medical video of laparascopic surgery. Four compression types (Motion JPG and three variants of H.264) at four bit-rates (5, 12, 20, and 45 Mbps) are studied and compared to original uncompressed sequences. One reduced reference metric (VQM) and one full-reference metric (MOVIE) are studied. Subjective video evaluation consists of overall quality scores as well as difference scores between compressed and uncompressed sequences for similarity and five types of artifacts or attributes: blurring, blocking, noise, color fidelity, and motion artifacts. The results of the subjective and objective evaluations exhibit similar trends across the compression types and bit-rates, and may indicate that these objective quality metrics may be valid reflections of subjective quality judgments made by non-expert observers on compressed medical video sequences. In future work we will expand the subjective quality evaluation to include expert laparoscopic surgeons as subjects

Image blur estimation based on the average cone of ratio in the wavelet domain

In this paper, we propose a new algorithm for objective blur estimation using wavelet decomposition. The central idea of our method is to estimate blur as a function of the center of gravity of the average cone ratio (ACR) histogram. The key properties of ACR are twofold: it is powerful in estimating local edge regularity, and it is nearly insensitive to noise. We use these properties to estimate the blurriness of the image, irrespective of the level of noise. In particular, the center of gravity of the ACR histogram is a blur metric. The method is applicable both in case where the reference image is available and when there is no reference. The results demonstrate a consistent performance of the proposed metric for a wide class of natural images and in a wide range of out of focus blurriness. Moreover, the proposed method shows a remarkable insensitivity to noise compared to other wavelet domain methods