Linear Intensity Change Research Articles

Artificial intelligence scale-invariant feature transform algorithm-based system to improve the calculation accuracy of Ki-67 index in invasive breast cancer: a multicenter retrospective study.

Ki-67 is a key indicator of the proliferation activity of tumors. However, no standardized criterion has been established for Ki-67 index calculation. Scale-invariant feature transform (SIFT) algorithm can identify the robust invariant features to rotation, translation, scaling and linear intensity changes for matching and registration in computer vision. Thus, this study aimed to develop a SIFT-based computer-aided system for Ki-67 calculation in breast cancer. Hematoxylin and eosin (HE)-stained and Ki-67-stained slides were scanned and whole slide images (WSIs) were obtained. The regions of breast cancer (BC) tissues and non-BC tissues were labeled by experienced pathologists. All the labeled WSIs were randomly divided into the training set, verification set, and test set according to a fixed ratio of 7:2:1. The algorithm for identification of cancerous regions was developed by a ResNet network. The registration process between paired consecutive HE-stained WSIs and Ki-67-stained WSIs was based on a pyramid model using the feature matching method of SIFT. After registration, we counted the nuclear-stained Ki-67-positive cells in each identified invasive cancerous region using color deconvolution. To assess the accuracy, the AI-assisted result for each slice was compared with the manual diagnosis result of pathologists. If the difference of the two positive rate values is not greater than 10%, it was a consistent result; otherwise, it was an inconsistent result. The accuracy of the AI-based algorithm in identifying breast cancer tissues in HE-stained slides was 93%, with an area under the curve (AUC) of 0.98. After registration, we succeeded in identifying Ki-67-positive cells among cancerous cells across the entire WSIs and calculated the Ki-67 index, with an accuracy rate of 91.5%, compared to the gold standard pathological reports. Using this system, it took about 1 hour to complete the evaluation of all the tested 771 pairs of HE- and Ki-67-stained slides. Each Ki-67 result took less than 2 seconds. Using a pyramid model and the SIFT feature matching method, we developed an AI-based automatic cancer identification and Ki-67 index calculation system, which could improve the accuracy of Ki-67 index calculation and make the data repeatable among different hospitals and centers.

Feature points for multisensor images

Feature points are effective for wide range of computer vision applications. In the last two decades, a large number of feature point detection and description algorithms have been proposed. All these algorithms are implemented differently but have a common objective to detect and describe feature points invariant to scale, rotation, intensity, and affine variations. Several comparative studies of feature points have been reported in literature. These studies are either application specific or deal with common type of transformations and deformations. Additionally, they primarily focus on evaluation of feature points on gray scale or RGB images. In contrast, this paper presents a comparison of feature points on multisensor images, which possess non linear intensity changes. The objective is to identify robust feature points for image to image matching tasks on multisensor images. Six well known feature point detector and seventeen popular descriptor algorithms are compared. Experimental results obtained on four different image datasets show that the combination of Harris detector with BRIEF descriptor outperforms all other detector-descriptor combinations on multisensor images.

Local image descriptor based on spectral embedding

This study presents a local image descriptor based on spectral embedding. Specifically, the spectra of line graph are used to represent image edges, corners and edge points with big curvature. The authors theoretically analyse and experimentally verify that the spectra of line graph are robust to noise and are invariant to rotation and linear intensity changes. Based on such a fact, some local image descriptors are constructed using the spectra of line graph. Comparative experiments demonstrate the effectiveness of the proposed descriptor and its superiority to some state‐of‐the‐art descriptors under image rotation, image blur, viewpoint change, illumination change, JPEG compression and noise.

Continuous loudness response to acoustic intensity dynamics in melodies: Effects of melodic contour, tempo, and tonality

The aim of this work was to investigate perceived loudness change in response to melodies that increase (up-ramp) or decrease (down-ramp) in acoustic intensity, and the interaction with other musical factors such as melodic contour, tempo, and tonality (tonal/atonal). A within-subjects design manipulated direction of linear intensity change (up-ramp, down-ramp), melodic contour (ascending, descending), tempo, and tonality, using single ramp trials and paired ramp trials, where single up-ramps and down-ramps were assembled to create continuous up-ramp/down-ramp or down-ramp/up-ramp pairs. Twenty-nine (Exp 1) and thirty-six (Exp 2) participants rated loudness continuously in response to trials with monophonic 13-note piano melodies lasting either 6.4s or 12s. Linear correlation coefficients >.89 between loudness and time show that time-series loudness responses to dynamic up-ramp and down-ramp melodies are essentially linear across all melodies. Therefore, ‘indirect’ loudness change derived from the difference in loudness at the beginning and end points of the continuous response was calculated. Down-ramps were perceived to change significantly more in loudness than up-ramps in both tonalities and at a relatively slow tempo. Loudness change was also greater for down-ramps presented with a congruent descending melodic contour, relative to an incongruent pairing (down-ramp and ascending melodic contour). No differential effect of intensity ramp/melodic contour congruency was observed for up-ramps. In paired ramp trials assessing the possible impact of ramp context, loudness change in response to up-ramps was significantly greater when preceded by down-ramps, than when not preceded by another ramp. Ramp context did not affect down-ramp perception. The contribution to the fields of music perception and psychoacoustics are discussed in the context of real-time perception of music, principles of music composition, and performance of musical dynamics.

A Robust SIFT Descriptor for Multispectral Images

This letter presents a novel method for the description of multispectral image keypoints. The method proposed is based on a modified SIFT algorithm. It uses normalized gradients as local image features for the description of keypoints in order to achieve robustness against non linear intensity changes between multispectral images. The experimental results show that the method proposed achieves a better matching performance and outperforms the SIFT algorithm.

Generalized Gradient Based Digital Image Correlation Method for Displacement with Subpixel Accuracy

Basic principle of generalized gradient based digital image (DIC) method for subpixel-accuracy displacement measurement is discussed. The proposed method can cope well with lighting illumination variation or grayscale intensity change on the object surface during measurement by adopting a linear intensity change model, which overcomes the limitations in existing linear least squares based spatial gradient method by adopting constant model. To evaluate the proposed methods, both computer simulation and actual experiment have been carried out. The results show that the proposed method can measure displacement accurately even when substantial intensity variations exist in the experimental images.

Generalized Gradient Based Digital Image Correlation Method for Displacement with Subpixel Accuracy

Basic principle of generalized gradient based digital image (DIC) method for subpixel-accuracy displacement measurement is discussed. The proposed method can cope well with lighting illumination variation or grayscale intensity change on the object surface during measurement by adopting a linear intensity change model, which overcomes the limitations in existing linear least squares based spatial gradient method by adopting constant model. To evaluate the proposed methods, both computer simulation and actual experiment have been carried out. The results show that the proposed method can measure displacement accurately even when substantial intensity variations exist in the experimental images.

Performance of iterative gradient-based algorithms with different intensity change models in digital image correlation

Digital image correlation (DIC) is a non-contact and powerful tool for whole-field displacement and strain measurement in modern optical metrology. In the DIC technique, the gradient-based algorithm is one of the most commonly used sub-pixel registration algorithms due to its effectiveness and accuracy. Thus, this algorithm has been further investigated and improved by many researchers to increase its accuracy. In the existing gradient-based algorithms, there are three relation models for the gray level intensity of a point in the undeformed and deformed images: namely the constant intensity model, the linear intensity change model and the non-linear intensity change model. However, little quantitative investigation has been conducted to compare their performance. In this paper, three iterative gradient-based algorithms are given using the corresponding intensity change model and the iterative least squares (ILS) solving method. The accuracy, robustness and computational efficiency of the three algorithms for intensity variation are investigated through numerical simulation experiments. The experimental results reveal that the algorithm generated using the non-linear intensity change model is the most accurate, robust and efficient of the three algorithms and the algorithms generated using the linear and non-linear intensity change models have approximately the same accuracy when the intensity variation is small between undeformed and deformed images.

Modified LACIF filtering in background disjoint noise

This work deals with pattern recognition methods based on correlations for images in the presence of noise. We propose a modification of the nonlinear Locally Adaptive Contrast Invariant Filter (LACIF) that yields correlation peaks that are invariant to linear intensity changes of the target but that has some limitations in the presence low variance nonoverlapping background noise. The modification of the filter implies a normalization by a global variance of several distributions. The estimation of the variance distributions is done locally by means of correlations. Experimental results as well as comparisons with the classical matched filter and the common LACIF are given.

A Novel Descriptor for Line (Curve) Matching

This paper defines a new image feature called Harris feature vector, which is able to describe the image gradient distribution in an effective way. By computing the mean and the standard deviation of the Harris feature vector in a local image region, novel descriptors are constructed for line (curve) matching which are invariable to image rigid transformation and linear intensity change. Experimental evidence suggests that the novel descriptor for line (curve) matching performs well.

Feature vector field and feature matching

In this paper, we propose a feature vector field for images, which is built by the inner products and exterior products of image gradients. The feature vector field effectively represents image edges and feature points including corners and edge points with big curvature. Using the feature vector field, some novel descriptors are constructed for point matching and curve matching. The descriptors are all invariant to image Euclidean transformations and linear intensity changes. The experiments show that the descriptors also have a good adaptability to small image affine transformation, JPEG compression and nonlinear changes of intensity.

Digital image correlation using iterative least squares and pointwise least squares for displacement field and strain field measurements

Digital image correlation (DIC) method using iterative least squares algorithm (ILS) for displacement field measurement and pointwise least squares algorithm (PLS) for strain field measurement is proposed in this paper. A more general and practical intensity change model is employed with consideration of the linear intensity change of the deformed image, followed by an iterative least squares algorithm for calculating displacement field with sub-pixel accuracy. The concept of correlation function is not used in the ILS method, even though we prove that the algorithm is actually equivalent to the optimization of the sum of squared difference correlation function using improved Newton–Raphson method. Besides, different from the conventional strain estimation approaches based on smoothing the displacement fields first and followed by differentiation of the smoothed displacement fields, a simple yet effective PLS algorithm is proposed for extracting strain fields from the computed displacement fields. The effectiveness and accuracy of the proposed techniques is verified through numerical simulation experiments. A practical application of the algorithms to residual plastic deformation field measurement of GH4169 alloy subjected to tensile fatigue is also presented.

Optical analog signal digitization

A fast optical signal digitization method is proposed based on micrometer ring waveguide cavities; a realization is presented. Simulation has been applied to the digitization of an analog optical signal with linear intensity change. The sampling frequencies can attain 1010 per second over a wide spectral range.

Band analysis of temperature-dependent near-infrared spectra of oleic acid in the pure liquid state by the analytic geometric approach.

The applicability of the band-stripping and complementary matching method has been demonstrated by the analysis of temperature-dependent near-infrared (NIR) absorption spectra in the 7500-6500 cm(-1) region of oleic acid (cis-9-octadecenoic acid) in the pure liquid state. This method is based on first derivative-second derivative pair (D1-D2) plots and a new concept called the complementary band, cBDi, created by subtracting all the rest of the bands, exclusive of the ith estimated band, eBDi, from an experimental spectrum. The degree of coincidence of both band shapes provides a suitable measure for the quality of fit for each individual component band. It has been confirmed from the present analysis of the NIR spectra of oleic acid measured over a temperature range of 16-79 degrees C that the change of the peak intensity of the component band at around 6915 cm(-1) due to the first overtone of an O-H stretching vibration of the monomer has two transition points around 35 and 55 degrees C. Moreover, the present study has provided new insight into the analysis of temperature-dependent spectral variations of oleic acid. Among the three temperature ranges, 16-35 degrees C, 35-55 degrees C, and 55-79 degrees C, in the first range the band near 6915 cm(-1) shows a slight increase and in the second range it has a linear intensity change with a slope of 0.002 a.u./degree C. In the third range, a rapid increase of the peak intensity is observed. This band exists even at 15 degrees C (just below the melting point) and shows a shift from 6910 to 6915 cm(-1) and a band narrowing from 85 to 80 cm(-1) (full width at half-height) over a temperature range of 16 to 79 degrees C. Furthermore, it has been found that there are two broad bands at around 6835 and 6778.