There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.

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FSIM: A Feature Similarity Index for Image Quality Assessment

The 2010 McDonald criteria for the diagnosis of multiple sclerosis are widely used in research and clinical practice. Scientific advances in the past 7 years suggest that they might no longer provide the most up-to-date guidance for clinicians and researchers. The International Panel on Diagnosis of Multiple Sclerosis reviewed the 2010 McDonald criteria and recommended revisions. The 2017 McDonald criteria continue to apply primarily to patients experiencing a typical clinically isolated syndrome, define what is needed to fulfil dissemination in time and space of lesions in the CNS, and stress the need for no better explanation for the presentation. The following changes were made: in patients with a typical clinically isolated syndrome and clinical or MRI demonstration of dissemination in space, the presence of CSF-specific oligoclonal bands allows a diagnosis of multiple sclerosis; symptomatic lesions can be used to demonstrate dissemination in space or time in patients with supratentorial, infratentorial, or spinal cord syndrome; and cortical lesions can be used to demonstrate dissemination in space. Research to further refine the criteria should focus on optic nerve involvement, validation in diverse populations, and incorporation of advanced imaging, neurophysiological, and body fluid markers.

/pdf/diagnosis-of-multiple-sclerosis-2017-revisions-of-the-55ct02l5o1.pdf

Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? What elements are critical for their success? To answer these questions, we introduce a new dataset of human perceptual similarity judgments. We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics. We find that deep features outperform all previous metrics by large margins on our dataset. More surprisingly, this result is not restricted to ImageNet-trained VGG features, but holds across different deep architectures and levels of supervision (supervised, self-supervised, or even unsupervised). Our results suggest that perceptual similarity is an emergent property shared across deep visual representations.

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric

Cluster analysis is aimed at classifying elements into categories on the basis of their similarity. Its applications range from astronomy to bioinformatics, bibliometrics, and pattern recognition. We propose an approach based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. This idea forms the basis of a clustering procedure in which the number of clusters arises intuitively, outliers are automatically spotted and excluded from the analysis, and clusters are recognized regardless of their shape and of the dimensionality of the space in which they are embedded. We demonstrate the power of the algorithm on several test cases.

Clustering by fast search and find of density peaks

We introduce a new measure of image similarity called the complex wavelet structural similarity (CW-SSIM) index and show its applicability as a general purpose image similarity index. The key idea behind CW-SSIM is that certain image distortions lead to consistent phase changes in the local wavelet coefficients, and that a consistent phase shift of the coefficients does not change the structural content of the image. By conducting four case studies, we have demonstrated the superiority of the CW-SSIM index against other indices (e.g., Dice, Hausdorff distance) commonly used for assessing the similarity of a given pair of images. In addition, we show that the CW-SSIM index has a number of advantages. It is robust to small rotations and translations. It provides useful comparisons even without a preprocessing image registration step, which is essential for other indices. Moreover, it is computationally less expensive.

/pdf/complex-wavelet-structural-similarity-a-new-image-similarity-3jteb27pls.pdf

Complex Wavelet Structural Similarity: A New Image Similarity Index

/pdf/computer-aided-detection-and-diagnosis-in-mammography-4exig6gc0f.pdf

Computer-Aided Detection and Diagnosis in Mammography

Objective: Increasing evidence indicates that the thalamus may be a location of early neurodegeneration in multiple sclerosis (MS). Our objective was to identify the presence of gray matter volume loss and thinning in patients with radiologically isolated syndrome (RIS). Methods: Sixty-three participants were included in this case-control study. Twenty-one patients with RIS were age- and sex-matched to 42 healthy controls in a 1:2 ratio. All participants underwent brain MRIs on a single 3T scanner. After lesion segmentation and inpainting, 1 mm 3 -isometric T1-weighted images were submitted to FreeSurfer (v5.2). Normalized cortical and deep gray matter volumes were compared between patients with RIS and controls using t tests, and thalamic volumes were correlated with white matter lesion volumes using Pearson correlation. Exploratory cortical thickness maps were created. Results: Although traditional normalized total gray and white matter volumes were not statistically different between patients with RIS and controls, normalized left (0.0046 ± 0.0005 vs 0.0049 ± 0.0004, p = 0.006), right (0.0045 ± 0.0005 vs 0.0048 ± 0.0004, p = 0.008), and mean (0.0045 ± 0.0005 vs 0.0049 ± 0.0004, p = 0.004) thalamic volumes were significantly lower in patients with RIS (n = 21, mean age 41.9 ± 12.7 years) than in controls (n = 42, mean age 41.4 ± 11.2 years). Thalamic volumes correlated modestly with white matter lesion volumes (range: r = −0.35 to −0.47). Conclusion: Our data provide novel evidence of thalamic atrophy in RIS and are consistent with previous reports in early MS stages. Thalamic volume loss is present early in CNS demyelinating disease and should be further investigated as a metric associated with neurodegeneration.

https://nn.neurology.org/content/nnn/2/3/e102.full.pdf

Early CNS neurodegeneration in radiologically isolated syndrome

Amyloid positron emission tomography (PET) imaging is being investigated as a screening tool to identify amyloid-positive patients as an enrichment strategy for Alzheimer disease (AD) clinical trial enrollment. In a multicenter, phase 1b trial, patients meeting clinical criteria for prodromal or mild AD underwent florbetapir PET scanning at screening. PET, magnetic resonance imaging, and coregistered PET/magnetic resonance imaging scans were reviewed by 2 independent readers and binary visual readings tabulated. Semiquantitative values of cortical to whole cerebellar standard uptake value ratios were computed (threshold 1.10). Of 278 patients with an evaluable PET scan, 170 (61%) and 185 (67%) were amyloid-positive by visual reading and quantitative analysis, respectively; 39% were excluded from the study due to an amyloid-negative scan based on visual readings. More ApoE e4 carriers than noncarriers were amyloid-positive (80% vs. 43%). Comparison of visual readings with quantitative results identified 21 discordant cases (92% agreement). Interreader and intrareader agreements from visual readings were 98% and 100%, respectively. Amyloid PET imaging is an effective and feasible screening tool for enrollment of amyloid-positive patients with early stages of AD into clinical trials.

Amyloid PET Screening for Enrichment of Early-Stage Alzheimer Disease Clinical Trials: Experience in a Phase 1b Clinical Trial.

Objective There is increasing evidence that altered glutamate (Glu) homeostasis is involved in the pathophysiology of multiple sclerosis (MS). The aim of this study was to evaluate the in vivo effects of excess brain Glu on neuroaxonal integrity measured by N-acetylaspartate (NAA), brain volume, and clinical outcomes in a large, prospectively followed cohort of MS subjects. Methods We used multivoxel spectroscopy at 3T to longitudinally estimate Glu and NAA concentrations from large areas of normal-appearing white and gray matter (NAWM and GM) in MS patients (n = 343) with a mean follow-up time of 5 years. Using linear mixed-effects models, Glu was examined as a predictor of NAA decline, annualized percentage brain volume change, and evolution of clinical outcomes (Multiple Sclerosis Functional Composite [MSFC], Paced Auditory Serial Addition Test-3 [PASAT], and Expanded Disability Status Scale). Glu/NAA ratio was tested as a predictor of brain volume loss and clinical outcomes. Results Baseline Glu[NAWM] was predictive of accelerated longitudinal decline in NAA[GM] (-0.06mM change in NAA[GM]/yr for each unit increase in Glu; p = 0.004). The sustained elevation of Glu[NAWM] was predictive of a loss of 0.28mM/yr in NAA[NAWM] (p < 0.001) and 0.15mM/yr in NAA[GM] (p = 0.056). Each 10% increase in Glu/NAA [NAWM] was associated with a loss of 0.33% brain volume/yr (p = 0.001), 0.009 standard deviations/yr in MSFC z-score (p < 0.001), and 0.17 points/yr on the PASAT (p < 0.001). Interpretation These results indicate that higher Glu concentrations increase the rate of NAA decline, and higher Glu/NAA[NAWM] ratio increases the rate of decline of brain volume, MSFC, and PASAT. This provides evidence of a relationship between brain Glu and markers of disease progression in MS. © 2014 American Neurological Association.

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Mehul Sampat

Papers

Complex Wavelet Structural Similarity: A New Image Similarity Index

Computer-Aided Detection and Diagnosis in Mammography

Early CNS neurodegeneration in radiologically isolated syndrome

Amyloid PET Screening for Enrichment of Early-Stage Alzheimer Disease Clinical Trials: Experience in a Phase 1b Clinical Trial.

In vivo evidence of glutamate toxicity in multiple sclerosis