Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

/pdf/image-quality-assessment-from-error-visibility-to-structural-1rlwcqe34t.pdf

Image quality assessment: from error visibility to structural similarity

A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.

/pdf/a-model-of-saliency-based-visual-attention-for-rapid-scene-wv0n3yqxix.pdf

A model of saliency-based visual attention for rapid scene analysis

https://surfer.nmr.mgh.harvard.edu/ftp/articles/dale99-recon1.pdf

Cortical surface-based analysis. I. Segmentation and surface reconstruction

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

Since its release in November 2022, OpenAI's artificial intelligence (AI) chatbot ChatGPT has aroused great interest because of its very impressive ability to provide well-formulated and detailed natural language responses to queries about a huge variety of topics. These responses are based on an immense set of training data, obtained from the Internet in 2021, and on information gained from interactions with its users. However, ChatGPT's users soon found that the answers they received to their queries were not always trustworthy. Indeed, OpenAI itself lists as one of ChatGPT's limitations that it “sometimes writes plausible-sounding but incorrect or nonsensical answers, [i.e.,] confident responses that cannot be grounded in any of its training data.” [4] (The term “hallucination” has come into use by the AI community for such responses, which are not unique to ChatGPT [5].)

ChatGPT's Astonishing Fabrications About Percy Ludgate

Diffusion MRI is a complex technique, where new discoveries and implementations occur at a fast pace. The expertise needed for data analyses and accurate and reproducible results is increasingly demanding and requires multidisciplinary collaborations. In the present work we introduce Reproducible Tract Profiles 2 (RTP2), a set of flexible and automated methods to analyze anatomical MRI and diffusion weighted imaging (DWI) data for reproducible tractography. RTP2 reads structural MRI data and processes them through a succession of serialized containerized analyses. We describe the DWI algorithms used to identify white-matter tracts and their summary metrics, the flexible architecture of the platform, and the tools to programmatically access and control the computations. The combination of these three components provides an easy-to-use automatized tool developed and tested over 20 years, to obtain usable and reliable state-of-the-art diffusion metrics at the individual and group levels for basic research and clinical practice. 

https://www.nature.com/articles/s41598-023-32924-7.pdf

Reproducible Tract Profiles 2 (RTP2) suite, from diffusion MRI acquisition to clinical practice and research

• Preprocessing: (a) diffusion tensors were estimated using mrDiffusion (http://white.stanford.edu/software) (b) b0 images were co-registered to the whole-brain anatomy with SPM2. (c) Templates of adult and child DTI data were created by normalizing and averaging the diffusion data (Dougherty et al, 2005). • Fibers were tracked from a large sphere (30mm radius) centered on pOTS ROI. • Fiber tracking using STT (Mori et al., 1999) with 4th order Runge-Kutta correction, step size=1mm, 8 starting point within each voxel. Tract termination criteria were FA 30°. • Fibers were restricted to those with endpoints within a smaller, 8mm radius sphere centered on pOTS ROI. • The resulting fibers were manually pruned and classified by cortical projection zones.

Dorsal and ventral fiber tracts of the pOTS in adults and children

We describe an end-to-end image systems simulation that models a device capable of measuring fluorescence in the oral cavity. Our software includes a 3D model of the oral cavity and excitation-emission matrices of endogenous fluorophores that predict the spectral radiance of oral mucosal tissue. The predicted radiance is transformed by a model of the optics and image sensor to generate expected sensor image values. We compare simulated and real camera data from tongues in healthy individuals and show that the camera sensor chromaticity values can be used to quantify the fluorescence from porphyrins relative to the bulk fluorescence from multiple fluorophores (elastin, NADH, FAD, and collagen). Validation of the simulations supports the use of soft-prototyping in guiding system design for fluorescence imaging. © 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

/pdf/simulations-of-fluorescence-imaging-in-the-oral-cavity-556j5j92b8.pdf

Simulations of fluorescence imaging in the oral cavity

We describe and experimentally validate an end-to-end simulation of a digital camera. The simulation models the spectral radiance of 3D-scenes, formation of the spectral irradiance by multi-element optics, and conversion of the irradiance to digital values by the image sensor. We quantify the accuracy of the simulation by comparing real and simulated images of a precisely constructed, three-dimensional high dynamic range test scene. Validated end-to-end software simulation of a digital camera can accelerate innovation by reducing many of the time-consuming and expensive steps in designing, building and evaluating image systems.

Brian A. Wandell

Papers

ChatGPT's Astonishing Fabrications About Percy Ludgate

Reproducible Tract Profiles 2 (RTP2) suite, from diffusion MRI acquisition to clinical practice and research

Dorsal and ventral fiber tracts of the pOTS in adults and children

Simulations of fluorescence imaging in the oral cavity

Validation of image systems simulation technology using a Cornell Box.