Mach Bands Research Articles

Mach band effect: A source of diagnostic error in intensive care unit

Abstract This is a case of pseudopneumothorax caused by skin fold artifact in chest X-ray. This is important to diagnose as it can lead to inappropriate intervention or radiation exposure as in this case. Human eye’s ability to perceive margin by contrast difference is Mach band effect.

Novel Perceptual Mach Band based Deep Attention Network for Cyclone Intensity Estimation

Novel Perceptual Mach Band based Deep Attention Network for Cyclone Intensity Estimation

Role of visual illusions in the assessment of digital color images of aggressive posterior retinopathy of premature

AIM: To estimate the role of visual illusions in the diagnosis of aggressive posterior retinopathy of prematurity (APROP) using a pediatric retinal camera.
 MATERIAL AND METHODS: A retrospective analysis of images of the fundus structures obtained using a pediatric retinal camera was carried out in 31 children diagnosed with APROP. The children were treated at St. Petersburgs Childrens Hospital N17 of St. Nicholas from January 2011 to December 2018. Based on the analysis of digital colored images of the fundus, the diagnosis of ARN was confirmed in 18 children (58%), and in the remaining 42% of cases (13 children), the diagnosis was revised from APROP to classic ROP (stage 3, with signs of plus disease, zones I or II). Preterm infants with classic ROP were excluded from the study group.
 RESULTS: A total of 14 out of 18 (78%) children with APROP were initially diagnosed with manifestations of classical ROP. Analysis of colored images obtained with a retinal camera showed that all of them manifested Machs bands, which created the effect of a demarcation line or a demarcation ridge at the border of light and dark areas. Although they were manifestations of APROP, these illusions were the reason for the diagnosis of classical ROP along with the designation of stages. The effect of Mach band illusion was neutralized by the study of colored photographs on the monitor screen under high magnification.
 CONCLUSION: Visual illusions create difficulties in the timely diagnosis of APROP during the investigation with a retinal camera. They may influence the treatment of patients and lead to poor outcomes of this most severe form of ROP, including the development of retinal detachment and visual impairment in children.

The Diagnostic Relevance and Interfaces Covered by Mach Band Effect in Dentistry: An Analysis of the Literature.

This work surveyed how the Mach band effect was mentioned in the dental literature and provided a qualitative assessment of diagnostic relevance and interfaces covered. PubMed, Scopus, and Google Scholar were queried in mid-Jan 2022. The search string was (“mach band effect” OR “mach effect”) AND (dental OR oral OR tooth OR teeth OR maxillofacial OR orofacial). All publications returned by the searches were screened. Exclusion criteria included irrelevance (e.g., dealing with “Mach effect” that was non-radiographic or non-dental) and not written in English. Reference lists of returned publications were manually searched to identify potentially missed papers. For each included publication, the following parameters were recorded: any presentation of radiographic images showing a Mach band effect, direct investigation of the effect, relevance to which structural interfaces, diagnostic relevance, and in which parts of the publication Mach band effect was mentioned. Seventy-seven publications were included and analyzed. The majority of the publications mentioned the Mach band effect in the Discussion section about its diagnostic relevance to caries detection at the enamel-dentinal junction and the interface between restorative material and tooth structure. Eight of them presented radiographic images showing a Mach band effect. Three of them investigated the Mach band effect. Dental publications seldom covered the Mach band effect. When they covered it, most of them only mentioned it in the Discussion section without actually investigating it.

Neuromorphic System for Edge Information Encoding: Emulating Retinal Center-Surround Antagonism by Li-Ion-Mediated Highly Interactive Devices.

Center-surround antagonism, a key mechanism in the retina, contributes to the encoding of edge contrast rather than of the overall information on a visual image. Here, a neuromorphic system consisting of multiple ionic devices is built, where each device has a lithium cobalt oxide channel arranged on a common lithium phosphorus oxynitride electrolyte. Because of the migration of Li ions between the channels through the electrolyte, the devices are highly interactive, as is seen with retinal neurons. On the basis of the excitation of single devices and device-to-device inhibition, the system successfully emulates the antagonistic center-surround receptive field and the Mach band effect in which perceived contrast is enhanced at the edges between dark and bright regions. Furthermore, a two-dimensional array system is simulated to implement edge detection for real images. This scheme enables computer vision tasks with simple and effective operations, owing to the intrinsic properties of the materials employed.

Detecting and Localizing Dents on Vehicle Bodies Using Region-Based Convolutional Neural Network

Detection and localization of the dents on a vehicle body that occurs during manufacturing is critical to achieve the appearance quality of a new vehicle. This study proposes a region-based convolutional neural network (R-CNN) to detect and localize dents for a vehicle body inspection. For a better feature extraction, this study employed a lighting system, which can highlight dents on an image by projecting the Mach bands (bright-dark stripes). The R-CNN was trained using the highlighted images by the Mach bands, and heat-maps were prepared with the classification scores estimated from the R-CNN to localize dents. This study applied the proposed R-CNN to the inspection of dents on the surface of a car body and quantitatively analyzed its performances. The detection accuracy of the dents was 98.5% for the testing data set, and mean absolute error between the actual dents and estimated dents were 13.7 pixels, which were close to one another. The proposed R-CNN could be applied to detect and localize surface dents during the manufacture of vehicle bodies in the automobile industry.

Sonification supports perception of brightness contrast

In complex visual representations, there are several possible challenges for the visual perception that might be eased by adding sound as a second modality (i.e. sonification). It was hypothesized that sonification would support visual perception when facing challenges such as simultaneous brightness contrast or the Mach band phenomena. This hypothesis was investigated with an interactive sonification test, yielding objective measures (accuracy and response time) as well as subjective measures of sonification benefit. In the test, the participant’s task was to mark the vertical pixel line having the highest intensity level. This was done in a condition without sonification and in three conditions where the intensity level was mapped to different musical elements. The results showed that there was a benefit of sonification, with higher accuracy when sonification was used compared to no sonification. This result was also supported by the subjective measurement. The results also showed longer response times when sonification was used. This suggests that the use and processing of the additional information took more time, leading to longer response times but also higher accuracy. There were no differences between the three sonification conditions.

79‐2: Hyperrealism in Full Ultra High‐Definition 8K Display

8K displays have four times more resolution than 4K, which means that they will have twice the spatial frequency per screen length. With higher intensive Mach band effect, 8K displays show stronger luminance intensity for the objects and edges within the image. Higher resolution results in higher brightness and contrast and improves color and clarity expressions. In this study, 4K (60cpd) and 8K (120cpd) are quantified in realistic conditions of display 65 inches, viewing distance, and psychophysical images and videos selected through general luminance. As a result, an increase in resolution from 4K to 8K is perceived as increasing the perception contrast and color representation in all images, which have an integrated effect on cognitive resolution according to the image.

Application of blurred circular 3D images on the human vision model

This study was developed based on the lateral inhibition effect and the Mach band effect. In the study, various human vision experiments involving blurred circular 3D images of different shapes were conducted under fixed brightness and contrast, and the results of those experiments are described in detail herein, with each type of display and each shape considered and discussed separately. The results show that although the maximum values of the contrast sensitivity function curves of the various displays were different, they showed minimal variations. With changes in spatial frequency, the degree of diminution of the different lines was also minimal. However, since the shape at the intersection of the contour was modified by the Mach band effect, a greater degree of variation was generated. In addition, as the Gaussian blur was increased at the intersection of the contour lines, its Mach band effect tended to decrease as the corresponding contrast sensitivity function curve changed. Summarizing the results of the various experiments on human visual characteristics, a new visual model was developed to match the model tested with the blurred circular 3D images. This model can be used as a reference for the development of artificial vision systems and technological applications in the future.

A parsimonious model of brightness induction.

We present a parsimonious model of brightness induction which can account for various brightness illusions of both brightness-contrast and brightness-assimilation types. Our model is based on a difference of difference-of-Gaussian filter and a two-pass model of attentive vision based on the parallel channels in the central visual pathway. It overcomes some of the problems that could not be addressed by the well-known oriented difference of Gaussian model like those associated with Mach band and checkerboard illusions. This model attempts to provide insight to the mechanism of attention in brightness perception through the two major complimentary visual channels, viz. the magnocellular and the parvocellular.

Perceptual Adaptation of Image Based on Chevreul–Mach Bands Visual Phenomenon

The perceptual adaptation of the image (PAI) is introduced by inspiration from Chevreul–Mach Bands (CMB) visual phenomenon. By boosting the CMB assisting illusory effect on boundaries of the regions, PAI adapts the image to the perception of the human visual system and thereof increases the quality of the image. PAI is proposed for application to standard images or the output of any image processing technique. For the implementation of the PAI on the image, an algorithm of morphological filters (MFs) is presented, which geometrically adds the model of CMB effect. Numerical evaluation by improvement ratios of four no-reference image quality assessment (NR-IQA) indexes approves PAI performance where it can be noticeably observed in visual comparisons. Furthermore, PAI is applied as a postprocessing block for classical morphological filtering, weighted morphological filtering, and median morphological filtering in cancelation of salt and pepper, Gaussian, and speckle noise from MRI images, where the above specified NR-IQA indexes validate it. PAI effect on image enhancement is benchmarked upon morphological image sharpening and high-boost filtering.

Reducing Lateral Visual Biases in Displays

The human visual system is composed of multiple physiological components that apply multiple mechanisms in order to cope with the rich visual content it encounters. The complexity of this system leads to non-trivial relations between what we see and what we perceive, and in particular, between the raw intensities of an image that we display and the ones we perceive where various visual biases and illusions are introduced. In this paper we describe a method for reducing a large class of biases related to the lateral inhibition mechanism in the human retina where neurons suppress the activity of neighboring receptors. Among these biases are the well-known Mach bands and halos that appear around smooth and sharp image gradients as well as the appearance of false contrasts between identical regions. The new method removes these visual biases by computing an image that contains counter biases such that when this laterally-compensated image is viewed on a display, the inserted biases cancel the ones created in the retina.

Human vision model in relation to characteristics of shapes for the Mach band effect.

For human vision to recognize the contours of objects means that, as the contrast variation at the object's edges increases, so will the Mach band effect of human vision. This paper more deeply investigates the relationship between changes in the contours of an object and the Mach band effect of human vision. Based on lateral inhibition and the Mach band effect, we studied subjects' eyes as they watched images of different shapes under a fixed brightness at 34 cd/m2, with changes of contrast and spatial frequency. Three types of display were used: a television, a computer monitor, and a projector. For each display used, we conducted a separate experiment for each shape. Although the maximum values for the contrast sensitivity function curves of the displays were different, their variations were minimal. As the spatial frequency changed, the diminishing effect of the different lines also was minimal. However, as the shapes at the contour intersections were modified by the Mach band effect, a greater degree of variation occurred. In addition, as the spatial frequency at a contour intersection increased, the Mach band effect became lower, along with changes in the corresponding contrast sensitivity function curve. Our experimental results on the characteristics of human vision have led to what we believe is a new vision model based on tests with different shapes. This new model may be used for future development and implementation of an artificial vision system.

Neural circuitry of brightness induction: modeling and physiology

An important function of the brain is adjusting perception to context, so that estimates of object properties are relatively consistent despite variations in retinal inputs. Brightness induction, including both contrast and assimilation, is a classical example of context effects on surface perception, but the underlying neural circuits are still unknown. We have developed a neural model in which each unit at the cortical site of induction receives inputs from receptive fields (RFs) that are spatially opponent and orientation selective, and also from recently found RFs that are neither spatially opponent nor orientation selective. The synaptic weights for both input types are Gaussian functions of the distance in visual space between the input and recipient RFs. The weights for spatially opponent RFs also depend on orientation preference and contrast polarity. The parameters for the synaptic weight function were adjusted for the model to reproduce contrast-linearity of induction and exponential fall-off in context effects with distance from the test. With these parameters fixed, the model reproduced a large variety of complex contrast and assimilation effects, including Mach bands, grating induction, Helson's line stimuli showing assimilation versus contrast, Todorovic effects, Bullseye effect and 2-D and 3-D variants of White's Effect without explicitly extracting image junctions. By simulating deficits in explanatory power when removing specific types of RFs and connections from the model, we predicted specific functional connectivity that can be tested physiologically. With electrode-array recordings in anesthetized macaques, we found many neurons in area V4 that responded in opposite-phase to brightness modulations of the surround, a classical signature of brightness contrast, whereas almost all neurons in V2 responded in the same phase as the surround modulation. Consequently, our preliminary results suggest that V4 is the site where the two types of RFs converge to generate brightness induction. Meeting abstract presented at VSS 2015. Language: en

Differentiating Pneumothorax from the Common Radiographic Skinfold Artifact.

Pneumothorax can be a critical medical condition. The radiographic curvilinear appearance of pneumothorax can be mimicked by a skinfold artifact. Radiographic differentiation of the two entities is achieved in most cases by careful analysis of the characteristics of the linear shadow and its course. A thin, sharply defined opaque density representing the visceral pleura is the hallmark of pneumothorax. The added density of a skinfold presents as a broad opacity, which is outlined laterally by a sharply defined lucent line as a result of the Mach band effect and adjacent air. Unlike pneumothorax, a skinfold produces a line that does not follow the expected course of visceral pleura. Additional features, such as the absence of increased lucency laterally and the projection of lung markings across the curvilinear shadow, can help in the correct identification of skinfolds. Repeating the chest radiograph or using other imaging modalities can be considered in difficult cases.

Mach bands explained by response normalization.

Mach bands are the illusory dark and bright bars seen at the foot and knee of a luminance trapezoid. First demonstrated by Ernst Mach in the latter part of the 19th century, Mach bands are a test bed not only for models of brightness illusions but of spatial vision in general. Up until 50 years ago the dominant explanation of Mach Bands was that they were caused by lateral inhibition among retinal neurons. More recently, the dominant idea has been that Mach bands are a consequence of a visual process that generates a sparse, binary description of the image in terms of “edges” and “bars”. Another recent explanation is that Mach bands result from learned expectations about the pattern of light typically found on sharply curved surfaces. In keeping with recent multi-scale filtering accounts of brightness illusions as well as current physiology, I show however that Mach bands are most simply explained by response normalization, whereby the gains of early visual channels are adjusted on a local basis to make their responses more equal. I show that a simple one-dimensional model of response normalization explains the range of conditions under which Mach bands occur, and as importantly, the conditions under which they do not occur.

Mach Band Sign: An Optical Illusion

The chest radiograph of a 63-year-old woman who was found unresponsive was concerning for pneumopericardium, but lacked the other corroborative features suggestive of pneumopericardium. None of the follow-up chest radiographs showed evidence of air around the heart. The radiolucent shadow that mimicked pneumopericardium in this case was due to an artifact known as Mach band sign, an illusion created by lateral inhibition in the light receptors in the retina.

Modelling the Mach bands illusion by means of a diffusion model.

First, we criticize the validity of the principle of lateral inhibition. Second, on the basis of illusory phenomena and stabilized retinal images, we point out that the retina does not code the absolute luminance; the retina forwards a relative luminance sketch towards higher levels of the visual system. However, at the level of conscious processing the perceptual counterpart of absolute luminance, brightness, is available. Therefore, it is reasonable to assume that a reconstruction process is carried out by the visual system, which recovers the inner representation that corresponds to the retinal light distribution from the coded relative luminance sketch. We provide an illustrative description of a computational model of this reconstruction process. The basis of the reconstruction is a mathematically provable theorem, according to which if image P is produced from image I by Laplacian filtering, and then P is used as the sources and sinks of a homogeneous linear diffusion process, then the equilibrium of the diffusion will be identical to the original image I. We have illustrated this by a one-dimensional heat diffusion example, and by a series of test tubes connected to each other, also in one dimension. Brightness illusions are considered as a side effect of this diffusion-based reconstruction process. If the diffusion process deviates from the principle of homogeneous linearity, then the result of the reconstruction will deviate from the original image I. We showed a concrete illustration of this with regards to the Mach bands illusion: here we violated the principle of homogeneous linearity by means of inserting a small vertical tube serving as a serial resistance between each test tube and the horizontal connecting tube. This violation resulted in a change of water level in the source and the sink test tubes corresponding to the Mach bands illusion.

Asymmetrical Social Mach Bands

Perceptual processes generally enhance borders, because of their high information value. Mach bands are an example in vision. In the social world, borders are also of special significance; one side of a border is generally more esteemed or valued than the other. We claim that entities (individuals, groups) that are just over the border on the positive side tend to exaggerate their membership on the positive side (asymmetrical social Mach bands). We demonstrate this by showing that (a) master’s-degree universities use the word university to describe themselves more than major graduate universities do, (b) small international airports use the word international to describe themselves more than major airports do, and (c) University of Pennsylvania students, who are affiliated with a “marginal” Ivy League school, use the word Ivy to describe their school more than Harvard students do.

Now You See It, Now You Don’t: Visual Illusions in Radiology

Visual illusions are distortions, alterations, or alternatives in the appearance of reality that result primarily from the sensory and perceptual processing mechanisms of the human visual system. These illusions are common phenomena in radiology, and it is important to understand them because they can create the illusion of disease, leading to incorrect image interpretation. Visual illusions in radiology can be organized according to the point in the visual system at which they emerge. Illusions of sensation, including Mach bands and background effect, are "lower-order" visual phenomena that occur as the eye and brain translate light into neural activity. Illusions of perception, such as ambiguous figures and distortion, occur in "higher-order" brain structures that are responsible for coalescing sensory input into a mental image interpreted by the mind. Illusions of image formation (eg, parallax), as the name implies, result from the way images are generated. Some visual illusions occur with all modalities, whereas others tend to be modality specific. The authors discuss a variety of visual illusions, describing their underlying psychologic and neuroscientific basis and demonstrating their relevance to radiology. A thorough understanding of visual illusions in radiology enhances diagnostic accuracy by minimizing the risk of mistaking illusions for disease.