Marius Pedersen

Evaluation of 60 full-reference image quality metrics on the CID:IQ

Image quality metrics have become very popular and new metrics are proposed continuously. They have usually been developed with the goal of correlating with subjective image quality assessment. We perform an extensive evaluation of 60 state-of-the-art image quality metrics, including well-known metrics, such as SSIM, multiscale SSIM, VIF, MSE, S-DEE, CID, MAD, S-CIELAB, SHAME, VSNR, and PSNR. Evaluation is performed on the on the Colourlab Image Database: Image Quality (CID:IQ), a database consisting of 690 images where the subjective data has been collected at two different viewing distances. The performance of the image quality metrics is assessed in terms of correlation to subjective data.

Bayesian framework inspired no-reference region-of-interest quality measure for brain MRI images

Abstract. We describe a postacquisition, attribute-based quality assessment method for brain magnetic resonance imaging (MRI) images. It is based on the application of Bayes theory to the relationship between entropy and image quality attributes. The entropy feature image of a slice is segmented into low- and high-entropy regions. For each entropy region, there are three separate observations of contrast, standard deviation, and sharpness quality attributes. A quality index for a quality attribute is the posterior probability of an entropy region given any corresponding region in a feature image where quality attribute is observed. Prior belief in each entropy region is determined from normalized total clique potential (TCP) energy of the slice. For TCP below the predefined threshold, the prior probability for a region is determined by deviation of its percentage composition in the slice from a standard normal distribution built from 250 MRI volume data provided by Alzheimer’s Disease Neuroimaging Initiative. For TCP above the threshold, the prior is computed using a mathematical model that describes the TCP–noise level relationship in brain MRI images. Our proposed method assesses the image quality of each entropy region and the global image. Experimental results demonstrate good correlation with subjective opinions of radiologists for different types and levels of quality distortions.

The influence of short-term memory in subjective image quality assessment

Aiming at understanding the role of short-term memory in subjective image quality assessment, we report and compare results from two pair-comparison methods: stimuli shown side-by-side versus stimuli shown one after the other. Our results suggest that there is a significant chance that an observer will make different quality assessments in the two setups.

The Influence of Fingerprint Image Degradations on the Performance of Biometric System and Quality Assessment

One of the main challenges facing biometric technologies is system performance decreasing caused by low quality biometric samples. In fingerprint recognition, the system performance may be negatively affected by fingerprint image degradations, which are introduced by subject characteristic, image acquisition, subject behavior, or environment. Therefore, it is necessary to investigate how different fingerprint image degradations influence biometric system performance. In this paper, we will first study different fingerprint image degradations that affect system performance. Then review state-of-the-art fingerprint sample quality assessment methods and their evaluation approaches. Based on the survey, we select corresponding degradations and apply them to fingerprint samples. The system performance comparison between original and degraded fingerprints will be conducted in order to illustrate the impact of each degradation on biometric system performance. Finally, we use NFIQ fingerprint image quality metric to investigate its performance on selected degradations.

An adaptive contrast enhancement method for stereo endoscopic images combining binocular just noticeable difference model and depth information

Endoscopic image enhancement has become a very popular research field due to the success of minimally invasive interventions and the innovation of new technological treatment and diagnosis tools such as stereoscopic laparoscopes and the wireless capsule endoscopy. In spite of the important advances achieved in terms of image processing and enhancement, only a few techniques can be adapted to stereo endoscopic images. This can be explained by the specificities of the stereo endoscopic video acquisition process, the surgical tasks artifacts and the endoscopic domain characteristics (e.g., organ textures,edges, color distribution). In this paper we present a contrast enhancement method for stereo endoscopic images taking into consideration some of these specificities, namely those of the acquired stereo images i.e. the depth information, the binocular vision and the organs boundaries/textures. The idea is to enhance the image quality by a contrast enhancement process that exploits the local image activity, the depth information and the binocular just noticeable difference (BJND) model. The results of the conducted subjective experiment show that the proposed method produces stereo endoscopic images with sharper details of the underlying tissues and organs, without introducing any halo effect or overshooting. The observers reported as well a more depth feeling and less visual fatigue when perceiving the enhanced stereo endoscopic images. Introduction During the last three decades, minimally invasive surgery (MIS) has become a popular diagnostic and treatment tool widely used in the clinical routine. While conventional open surgery relies on making large incisions in the skin and separating the underlying tissues to get a direct access to the surgical target, MIS is performed through small incisions (usually between 0.5 and 1.5 cm) to reduce the surgical trauma and morbidity. The abdomen is insufflated with a specific dose of gas in order to create a working volume through which surgical instruments can be inserted via ports. Since direct viewing of the surgical scene is not possible, an endoscopic camera assists the surgeon’s navigation by providing views of the anatomical structures and the surgical instruments. One of the main challenges facing the surgeons during the laparoscopic chirurgical training is to adapt their tasks to a two dimensional (2D) flat view of the surgical field. This lack of depth perception in addition to the loss of tactile feedback, implies a significant sensory loss for the surgeons and can affect their performance. Therefore 3D laparoscopic visual systems such as the Da Vinci Surgical System [17], the EndoSite 3Di Digital Vision System [18] and stereoscopic laparoscopes have been recently developed to address this need. The convergence to 3D visual endoscopic systems introduced, however, new issues related to image quality. Additionally, applying conventional 2D enhancement techniques on stereo endoscopic images does not give necessarily the best results as it does not account for the inherent dependencies between the perceived stereo image quality and the two views. This difficulty to adapt conventional 2D enhancement methods for stereo images may be explained by two main reasons. First, the human visual system (HVS) does not perceive the left and right images independently. The slightly different views captured by each eye are monocularly processed than fused by the visual cortex taking into account many complex binocular vision features such as the binocular rivalry and suppression depending on how much different the images are. Therefore, a depth sensitive enhancement approach exploiting a cross view processing could be a more appropriate approach to enhancing stereo 3D images. Second, most of the image enhancement methods are not adapted to the particular characteristics of the endoscopic domain (moist homogenous tissues, dynamic illumination conditions, non-rigid deformation due to the patient and surgeon motion, specular reflections), the specificities of the endoscopic video acquisition process and the surgical task artifacts (smoke, lens fogging and blood pools). Endoscopic image enhancement aims either to improve the visual video quality for the surgeons or to ameliorate the input of subsequent post processing tasks such as feature extraction for 3D organ reconstruction and registration. One of the main challenges for MIS is to determine the intra-operative morphology of the surgical field. Such information is prerequisite to the registration of the patient-specific data and to the navigation capacity providing the surgeon an efficient control of robotic-assisted surgical systems. The characteristics of the endoscopic environment including dark areas (up to 40% of the special image resolution in some cases) and different acquisition and surgical artifacts makes feature extraction from stereo endoscopic images a very challenging task, which can influence the accuracy of 3D organ reconstruction and registration tasks. Among the image processing methods that can address this problem, a proper contrast enhancement technique can improve the endoscopic image quality and the depth feeling. Indeed, it has been demonstrated in [13] that performing a sharpness enhancement on the stereo image views increases the depth perception. Based on the subjective experiment results, the authors proposed an adaptive sharpness enhancement algorithm taking into account the depth perception of the HVS. In [4], Walid et al. improve the stereo image contrast with an algorithm combining the local edge information and the depth level of each object of the scene obtained by segmenting the disparity map. An unsharp masking technique is used in [8] to enhance images containing depth information by darkening the background objects. The aforementioned methods, however, neglect the inter-view differences between right and left luminance components, which can produce visual fatigue and eyestrain for the observer. This question has been addressed by [6], in which the authors propose a sharpness enhancement technique for stereo images using the binocular just noticeable difference model (BJND) [16]. In this paper, we propose an adaptive contrast enhancement method for stereo endoscopic images combining depth information and BJND visibility thresholds. The contrast is improved combining edginess information, depth data and the local image activity to adapt the enhancement in each region (homogenous or boundary region). The BJND is then used to control the overall inter-view enhancement and avoid any noticeable difference that can trigger eyestrain or visual fatigue. The reminder of this paper is organized as follows. Section 2 describes the contrast enhancement method based on local edge detection [1]. Section 3 presents an overview of the BJND model as derived in [16]. The proposed contrast method for stereo endoscopic images is introduced in Section 4. Section 5 describes the experimental settings and discusses the results. Finally, Section 6 conclude the paper. Edge-based contast enhancement (EBCE) In this section we present an overview of the contrast enhancement based on local edge detection technique [1], which accounts for contour detection perceptual features of the HVS by combining Gordon’s method [3] and the theory of contour detection [9]. Given a pixel P at spatial coordinates (i, j) and its gray-level intensity Ii, j, the local contrast is defined as follows: Ci, j = | Ii, j−Ei, j | Ii, j +Ei, j (1) where Ei, j is an estimate of the mean edge gray-level computed by averaging the weighted gray-level intensities within a window wi, j centered at (i, j) and computed as follows: Ei, j = ∑(m,n)∈wi, j Im,n ·Φ(δm,n) ∑(m,n)∈wi, j Φ(δm,n) (2) where δm,n represents the edge value and Φ is an increasing function. The improved contrast C′ i, j can be generated by simply applying a function f to the local contrast Ci, j, satistying the following conditions: { f : [0,1]→ [0,1] Ci, j 7−→ f (Ci, j) =C′ i, j ≥Ci, j (3) The output intensity is computed as follows: I ′ i, j = Ei, j · 1−C′ i, j 1+C′ i, j i f Ii, j ≤ Ei, j Ei, j · 1+C′ i, j 1−C′ i, j otherwise (4) In [1], the authors demonstrated the efficiency and noiserobustness of this low complexity algorithm in sharpening the edges and the micro-edges (e.g., the veins) of 2D images and discriminating objects according to their boundaries. This wellknown method improves also the gray-level distribution and facilitates the detection and the extraction of relevant information such as feature points. Such data is crucial in performing a 3D organ reconstruction for the navigation and the surgery planning. Overview of the BJND The BJND model measures the minimal noise/distortion in one stereoscopic view evoking noticeable perceptual difference when combined with the other view in the binocular vision process. Based on psychophysical experiments, the authors [16] investigated the visual sensitivity to contrast masking effect, the binocular combination of noise and the luminance masking effect for stereo images. In this section, we give an overview of the BJND model and the derivation of its formula as described in [16]. Given the left and the right images, the BJND map of the left view, (i.e., BJNDl) is defined as follows: BJNDl(i, j,d) = f (bgr(i+d, j),ehr(i+d, j),nar(i+d, j)) = AC(bgr(i+d, j),ehr(i+d, j)) × ( 1− ( nar(i+d, j) AC(bgr(i+d, j),ehr(i+d, j)) )λ) 1 λ (5) where i and j refers to the spatial pixel coordinates, d is the disparity value corresponding to the point (i, j) and na is the noise amplitude 0≤ nar ≤ AC. The parameter λ controls the impact of the right-view noise and it is set experimentally to 1.25. We can notice that the BJND left is dependent on the background luminance intensity (bg), the edge high (eh) and the noise amplitude (na) of the right image. The inter-view pixel correspondence data is crucial for processing stereo content and generating the

Can no-reference image quality metrics assess visible wavelength iris sample quality?

The overall performance of iris recognition systems is affected by the quality of acquired iris sample images. Due to the development of imaging technologies, visible wavelength iris recognition gained a lot of attention in the past few years. However, iris sample quality of unconstrained imaging conditions is a more challenging issue compared to the traditional near infrared iris biometrics. Therefore, measuring the quality of such iris images is essential in order to have good quality samples for iris recognition. In this paper, we investigate whether general purpose no-reference image quality metrics can assess visible wavelength iris sample quality.

Sparse Coded Handcrafted and Deep Features for Colon Capsule Video Summarization

Capsule endoscopy, which uses a wireless camera to take images of the digestive track, is emerging as an alternative to traditional wired colonoscopy. A single examination produces a sequence of approximately 50,000 frames. These sequences are manually reviewed, which is time consuming and typically takes about 45–90 minutes and requires the undivided concentration of the reviewer. In this paper, we propose a novel capsule video summarization framework using sparse coding and dictionary learning in feature space. Video frames are clustered into superframes based on power spectral density, and cluster representative frames are used for video summarization. Handcrafted and deep features that are extracted for representative frames are sparse coded using a learned dictionary. Sparse coded features are later used for training SVM classifier. The proposed method was compared with state-of-the-art methods based on sensitivity and specificity. The achieved results show that our proposed framework provides robust capsule video summarization without losing informative segments.

The spatial statistics of structural magnetic resonance images: application to post-acquisition quality assessment of brain MRI images

ABSTRACT This report describes a new quality evaluation method for structural magnetic resonance images (MRI) of the brain. Pixels in MRI images are regarded as regionalized random variables that exhibit distinct and organized geographic patterns. We extract geo-spatial local entropy features and build three separate Gaussian distributed quality models upon them using 250 brain MRI images of different subjects. The MRI images were provided by Alzheimers disease neuroimaging initiative (ADNI). Image quality of a test image is predicted in a three-step process. In the first step, three separate geo-spatial feature vectors are extracted. The second step standardizes each quality model using corresponding geo-spatial feature vector extracted from the test image. The third step computes image quality by transforming the standardized score to probability. The proposed method was evaluated on images without perceived distortion and images degraded by different levels of motion blur and Rician noise as well as images with different configurations of bias fields. Based on the performance evaluation, our proposed method will be suitable for use in the field of clinical research where quality evaluation is required for the brain MRI images acquired from different MRI scanners and different clinical trial sites before they are fed into automated image processing and image analysis systems.

How Different Iterative and Filtered Back Projection Kernels Affect Computed Tomography Numbers and Low Contrast Detectability.

Objective The aim of this study was to evaluate how different iterative and filtered back projection kernels affect the computed tomography (CT) numbers and low contrast detectability. Methods Five different scans were performed at 6 different tube potentials on the same Catphan 600 phantom using approximately the same dose level and otherwise identical settings. The scans were reconstructed using all available filtered back projection body kernels and with iterative reconstruction techniques. Results The CT numbers and the contrast-to-noise ratios were reported and how they are affected by the kernel choice and strength of iterative reconstruction. Conclusions Iterative reconstruction improved contrast-to-noise ratio in most cases, but in certain situations, it decreased it. Variations in CT numbers can be large between kernels with similar sharpness for certain densities.

Advanced Image Enhancement Method for Distant Vessels and Structures in Capsule Endoscopy

This paper proposes an advanced method for contrast enhancement of capsule endoscopic images, with the main objective to obtain sufficient information about the vessels and structures in more distant (or darker) parts of capsule endoscopic images. The proposed method (PM) combines two algorithms for the enhancement of darker and brighter areas of capsule endoscopic images, respectively. The half-unit weighted-bilinear algorithm (HWB) proposed in our previous work is used to enhance darker areas according to the darker map content of its HSVs component V. Enhancement of brighter areas is achieved thanks to the novel threshold weighted-bilinear algorithm (TWB) developed to avoid overexposure and enlargement of specular highlight spots while preserving the hue, in such areas. The TWB performs enhancement operations following a gradual increment of the brightness of the brighter map content of its HSVs component V. In other words, the TWB decreases its averaged weights as the intensity content of the component V increases. Extensive experimental demonstrations were conducted, and, based on evaluation of the reference and PM enhanced images, a gastroenterologist (Ø.H.) concluded that the PM enhanced images were the best ones based on the information about the vessels, contrast in the images, and the view or visibility of the structures in more distant parts of the capsule endoscopy images.