Shape Estimation Research Articles

Effects of diesel pilot-injection strategy on a methanol/diesel dual-direct injection engine

To verify the effectiveness of dual-direct injection technology in enhancing the performance of methanol/diesel dual-fuel engines and the positive impacts of multiple-injection strategy on the in-cylinder mixture formation and combustion process, an experiment was conducted on a modified methanol/diesel dual-direct engine test platform in this paper. The research aimed to explore the effects of the diesel pilot-injection strategy, especially from the two aspects of diesel pilot-injection timing and diesel pilot-injection duration, on the operating range, combustion characteristics, performance and emissions. Results indicate that the pilot-injection strategy reduces the maximum pressure and pressure rise rate of the engine, alleviating the rough combustion in the cylinder and broaden the operating range. As the diesel pilot-injection timing advances, the in-cylinder pressure curve during the combustion process shifts leftward as a whole, and the pressure rise rate curve gradually becomes an approximate single-peak shape. The ignition delay almost increases linearly and CA50 first decreases and then increases with the advance of pilot-injection timing. When the pilot-injection duration increases, the ignition delay shortens and CA50 advances. Additionally, the diesel pilot-injection strategy can optimize engine emissions. As the pilot-injection timing advances and the duration increases, the HC, CO and soot emissions decrease, while the NOx emission increases. Using the pilot-injection strategy, the highest indicated thermal efficiency of 42.94 % is obtained at the diesel ignition injection timing and duration of −28 °CA aTDC and 0.25 ms respectively, which is 7.54 % relatively higher than that under a single injection strategy. The research results show that the pilot-injection strategy better adjusts the distribution of high reactivity and low reactivity fuels in the cylinder, which is beneficial for improving the combustion and performance of the methanol/diesel dual-fuel engine.

Comparison of synergy extrapolation and static optimization for estimating multiple unmeasured muscle activations during walking

BackgroundCalibrated electromyography (EMG)-driven musculoskeletal models can provide insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains unknown.MethodsThis study assessed the accuracy with which SynX can use eight measured EMG signals to estimate muscle activations and forces associated with eight missing EMG signals in the same leg during walking while simultaneously performing EMG-driven model calibration. Experimental gait data collected from two individuals post-stroke, including 16 channels of EMG data per leg, were used to calibrate an EMG-driven musculoskeletal model, providing “gold standard” muscle activations and forces for evaluation purposes. SynX was then used to predict the muscle activations and forces associated with the eight missing EMG signals while simultaneously calibrating EMG-driven model parameter values. Due to its widespread use, static optimization (SO) applied to a scaled generic musculoskeletal model was also utilized to estimate the same muscle activations and forces. Estimation accuracy for SynX and SO was evaluated using root mean square errors (RMSE) to quantify amplitude errors and correlation coefficient r values to quantify shape similarity, each calculated with respect to “gold standard” muscle activations and forces.ResultsOn average, compared to SO, SynX with simultaneous model calibration produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15, r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4 N, r value 0.53 vs. 0.07). SynX yielded calibrated Hill-type muscle–tendon model parameter values for all muscles and activation dynamics model parameter values for measured muscles that were similar to “gold standard” calibrated model parameter values.ConclusionsThese findings suggest that SynX could make it possible to calibrate EMG-driven musculoskeletal models for all important lower-extremity muscles with as few as eight carefully chosen EMG signals and eventually contribute to the design of personalized rehabilitation and surgical interventions for mobility impairments.

Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network.

Estimating canopy volumes of strawberry plants can be useful for predicting yields and establishing advanced management plans. Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera. A preprocessing method based on the You Only Look Once v8 Nano (YOLOv8n) object detection model was applied to correct image distortions influenced by fluctuating flight altitude under a manual maneuver. The CNN model was trained using actual canopy volumes measured using a cylindrical case and small expanded polystyrene (EPS) balls to account for internal plant spaces. Estimated canopy volumes using the CNN with flight altitude compensation closely matched the canopy volumes measured with EPS balls (nearly 1:1 relationship). The model achieved a slope, coefficient of determination (R2), and root mean squared error (RMSE) of 0.98, 0.98, and 74.3 cm3, respectively, corresponding to an 84% improvement over the conventional paraboloid shape approximation. In the application tests, the canopy volume map of the entire strawberry field was generated, highlighting the spatial variability of the plant's canopy volumes, which is crucial for implementing site-specific management of strawberry crops.

Kernel-inspired algorithm to transform transmission electron microscopy images into discrete dipole approximation geometries.

In this work, we present a code that transforms 2D transmission electron microscopy images into 3D geometries for discrete dipole approximation simulations in DDSCAT 7.3.3 based on Python 3.11 and OpenCV 4.8.1. This allows for the extrapolation of experimental sample images into ready-to-use simulation geometries. The advantage is that the geometry reflects complex shapes instead of approximations of basic shapes like spheres, cylinders, or cubes. The underlying algorithm to extrapolate 2D images to 3D structures is inspired by the working principle of kernels used in image processing. To showcase the code, the absorption spectrum of deposited gold nanoparticles was simulated and compared with experimental values. Apart from a small systematic shift of the simulated spectrum, it is in excellent agreement with the experiment.

Zonal shape reconstruction for Shack-Hartmann sensors and deflectometry

Some metrological means, such as Shack-Hartmann, deflectometry sensors or fringe projection profilometry, measure the shape of an optical surface indirectly from slope measurements. Zonal shape reconstruction, a method to reconstruct shape with a high number of degrees of freedom, is used for all of these applications. It has risen in interest with the use of deflectometers for the acquisition of high resolution slope data for optical manufacturing, especially because shape reconstruction is limiting in terms of shape estimation error.Zonal reconstruction methods all rely on the choice of a data formation model, a basis on which the shape will be decomposed, and an estimator. In this paper, we first study the canonical Fried and Southwell models of the literature and analyze their limitations. We show that modeling the slope measurement by a point-wise derivative as they both do can induce a bias on the shape estimation, and that the bases on which the shape is decomposed are imposed because of this assumption.In the second part of this paper, we propose to build an unbiased model of the data formation, without constraints on the choice of the decomposition basis. We then compare these models to the canonical models of Fried and Southwell.Lastly, we perform a regularized MAP reconstruction, and compare the performance in terms of total shape error of this method to the state of the art for the Southwell and Fried models, first by simulation, then on experimental data. We demonstrate that the suggested method outperforms the canonical models in terms of total shape reconstruction error on a deflectometry measurement of the high-frequency content of a freeform mirror.

A Deep Cascade Architecture for Stroke Lesion Segmentation and Synthetic Parametric Map Generation over CT St

Stroke is the second leading cause of mortality worldwide. Immediate attention and diagnosis play a crucial role in patient prognosis. Nowadays, computed tomography (CT) is the most utilized diagnostic imaging for early analysis and lesion stroke detection. Nonetheless, acute lesions are not visible on CT, and it is only possible to use this modality in screening analysis, discarding other neurological affectations. Expert radiologists can observe stroke lesions in advanced stages (subacute and chronic), as hypodense regions, but with limited sensibility and remarked subject variability. Computational strategies have been addressed to support lesion segmentation, following deep autoencoders and multimodal inputs. However, these strategies remain limited due to the high variability in the appearance and geometry of stroke lesions. This work introduces a novel deep representation that uses multimodal inputs from CT studies and parametric maps, computed from perfusion (CTP), to retrieve stroke lesions. The architecture follows an autoencoder deep representation, that forces attention on the geometry of stroke through additive cross-attention modules. Besides, a cascade train is herein proposed to generate synthetic perfusion maps that complement multimodal inputs and help with stroke lesion refinement at each stage of processing. The proposed approach brings saliency maps that support observational expert analysis, about lesion localization, but also lead with automatic shape estimation of the stroke. The proposed approach was validated on the ISLES 2018 public dataset with a total of 92 studies that include the annotation of an expert radiologist. The proposed approach achieves a Dice score of 0.66 and a precision of 0.67, outperforming classical autoencoder approximations.

Dense depth alignment for human pose and shape estimation

Dense depth alignment for human pose and shape estimation

An Efficient Algorithm for Gravity Forward Modeling (GFM) with Masses of Arbitrary Shapes and Density Distributions

Summary Currently, gravimetric forward modelling of mass density structures with arbitrary geometries and density distributions typically involves subdividing the mass body into individual geometric elements (such as rectangular prisms), calculating their gravitational contributions that are then summed up to obtain the gravitational attraction of the whole body. To achieve a more accurate approximation of the true geometric shape and density distribution, this rectangular prism model requires fine dividing, which significantly increases computational load and reduces numerical efficiency. To address this issue, we propose the algorithm for gravimetric forward modeling of arbitrary geometric shapes and density distributions in spectral domain that significantly improves numerical efficiency while preserves computational accuracy. The novelty of our proposed algorithm lies in dividing the masses into multiple layers of equal thickness in the vertical direction, providing constant upper and lower bounds. This allows to extended Parker's formulas and apply the Fast Fourier Transform (FFT) to increase numerical efficiency. The algorithm is tested using synthetic models and then used to compute gravitational effects of topography and sediments using real data from Tibet. Results show high accuracy and numerical efficiency than rectangular prism approach.

Monocular Pose and Shape Reconstruction of Vehicles in UAV imagery using a Multi-task CNN

Estimating the pose and shape of vehicles from aerial images is an important, yet challenging task. While there are many existing approaches that use stereo images from street-level perspectives to reconstruct objects in 3D, the majority of aerial configurations used for purposes like traffic surveillance are limited to monocular images. Addressing this challenge, a Convolutional Neural Network-based method is presented in this paper, which jointly performs detection, pose, type and 3D shape estimation for vehicles observed in monocular UAV imagery. For this purpose, a robust 3D object model is used following the concept of an Active Shape Model. In addition, different variants of loss functions for learning 3D shape estimation are presented, focusing on the height component, which is particularly challenging to estimate from monocular near-nadir images. We also introduce a UAV-based dataset to evaluate our model in addition to an augmented version of the publicly available Hessigheim benchmark dataset. Our method yields promising results in pose and shape estimation: utilising images with a ground sampling distance (GSD) of 3 cm, it achieves median errors of up to 4 cm in position and 3° in orientation. Additionally, it achieves root mean square (RMS) errors of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm 6$$\\end{document} cm in planimetry and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm 18$$\\end{document} cm in height for keypoints defining the car shape.

Action-conditioned contrastive learning for 3D human pose and shape estimation in videos

The aim of this research is to estimate 3D human pose and shape in videos, which is a challenging task due to the complex nature of the human body and the wide range of possible pose and shape variations. This problem also poses difficulty in finding a satisfactory solution due to the trade-off between the accuracy and temporal consistency of the estimated 3D pose and shape. Thus previous researches have prioritized one objective over the other. In contrast, we propose a novel approach called the action-conditioned mesh recovery (ACMR) model, which improves accuracy without compromising temporal consistency by leveraging human action information. Our ACMR model outperforms existing methods that prioritize temporal consistency in terms of accuracy, while also achieving comparable temporal consistency with other state-of-the-art methods. Significantly, the action-conditioned learning process occurs only during training, requiring no additional resources at inference time, thereby enhancing performance without increasing computational demands.

Mechanical-electric-magnetic-thermal coupled enriched finite element method for magneto-electro-elastic structures

Abstract Magneto-electro-elastic (MEE) materials possess the ability to convert mechanical, electrical, and magnetic energies, playing a critical role in smart devices. To improve the accuracy and efficiency of solving the mechanical properties of MEE structures in mechanical-electrical-magnetic-thermal (MEMT) environments, an MEMT coupled multiphysics enriched finite element method (MP-EFEM) is proposed. Based on the fundamental equations and boundary conditions of MEE materials, the interpolation coverage function is introduced into the MEMT coupled finite element method (FEM) to construct higher-order approximate interpolation displacement shape functions, electric potential shape functions, and magnetic potential shape functions. Combined with the variational principle, MP-EFEM is proposed, and the governing equations of MP-EFEM are derived. Numerical examples validate the accuracy and high efficiency of MP-EFEM in solving the mechanical properties of MEE structures in MEMT environments. When compared to the MEMT coupled FEM (MEMT-FEM), the results show that this method offers higher accuracy and efficiency. Therefore, MP-EFEM can effectively analyze the mechanical properties of MEE structures under multiphysics coupling, providing a new method for the design and development of smart devices.

Direct Reconstruction of Wet Foam from Sparse‐View, Dynamic X‐Ray CT Scans

X‐ray imaging of wet foam dynamics with a high temporal resolution (e.g., 3D videos with a 10 Hz frame rate) requires fast rotation of either the foam sample or the X‐ray gantry. This, however, strongly limits the number of X‐ray projections per rotation that can be acquired. As a result, conventional computed tomography reconstruction methods generate 3D images with severe undersampling artefacts, complicating subsequent foam analysis. Herein, BubSub, a novel tomographic reconstruction approach that reconstructs a 4D (3D plus time) dynamic image of wet foam bubbles from sparse‐view X‐ray projections by leveraging prior knowledge about the evolving foam structure, is introduced. BubSub adapts a collection of subdivision surfaces with spherical topology to represent liquid–gas interfaces of foam bubbles. Estimation of bubble positions and shapes at each time point is achieved by minimizing the projection distance in relation to the measured projections. BubSub operates efficiently with minimal memory usage, exhibits robustness against noise, and provides accurate reconstructions, even when the available projections are limited, as evidenced by various experiments using both simulated and real wet foam X‐ray data.

Thiel cadaver eye as a training model for sub-Tenon’s blocks: a feasibility study

BackgroundRegional anaesthesia education, especially for ocular procedures, necessitates reliable surgical training models. While cadaveric models offer anatomical fidelity, conventional embalming methods may compromise tissue integrity. We aimed to assess the effectiveness of Thiel cadavers for training in sub-Tenon’s blocks by evaluating ocular tissues and measuring insertion forces.MethodsExperimental design, using twenty eyes from ten Thiel cadaver heads. These cadavers were specifically prepared to test the administration of sub-Tenon’s blocks. The research was conducted in a controlled laboratory setting appropriate for handling cadaveric materials and conducting precise measurements. Each cadaver eye underwent an initial ultrasound examination, and its axial length was noted. An intravitreal injection of heptastarch solution followed, to re-establish the eye’s sphericity. After this volume injection, the axial length and intraocular pressure were measured again. Mock sub-Tenon’s blocks were administered in 2 separate quadrants of the eye, with insertion forces measured using a pressure gauge. These were compared to a data set of insertion forces measured in a series of isolated pig’s eyes on which STBs had been performed. Main outcome measurements were macroscopic assessment of the ocular tissue layers and the insertion forces required for the sub-Tenon’s blocks. In a second set of 10 Thiel cadaver heads, 5 ml of sodium chloride were injected as sub-Tenon’s blocks and the emergence of a periocular “T-sign” ascertained and measured by ultrasound.ResultsFour of twenty eyes (20%) retained near-natural sphericity, with the remaining requiring volume injection to approximate physiological shape and pressure. The conjunctiva and Tenon’s layer were intact, and correct cannula placement was achieved in all cases. In 16 of 20 eyes where T-signs could be measured, the median thickness of the T-sign amounted to 2.72 mm (range 1.34 mm–5.28 mm). The average maximum cannula insertion force was 2.92 Newtons. Insertion forces in intact Thiel cadaver heads were consistently higher than in isolated pig’s eyes (3.6 N vs 2.0 N).ConclusionThese findings suggest that Thiel cadavers are a promising model for training in sub-Tenon’sblocks, despite the challenge of often desiccated and involuted eyes.

Impact of Item Songs in Hindi Films on Psychological Well-Being of Young Women in Pakistan

Movies are a popular form of mass media that helps shape ideas, develop images, and reinforce dominant cultural norms. The structure of the "lyrics and dance moves" of item songs objectifies women in the most humiliating ways. In Pakistan, there is massive viewership of Hindi films. This study focuses on the impact of Hindi item songs on the psychological wellbeing of young women in Pakistan. It also investigates the perception of self among young women after watching Hindi item songs. The study used the survey method to consider 150 female participants (aged 17-30 years). The findings of the study suggest that participants of the survey admire the self-confidence of the “item girl” portrayed in item songs, and they also see her as an “independent woman”. Though they do not prefer to watch item songs with family, they love to use it as a celebration song at weddings, parties and other gatherings and also like to perform on such songs compared to different songs. The study concludes that despite some otherwise conceptions, young women in Pakistan are convinced by the “depiction of confident women” in Hindi item songs. The uses and gratifications hypothesis posits that individuals consume various media types because they anticipate obtaining certain gratifications from such choices.

Implementation of a Flagship Program in Instilling Religious Values in Students: Case Study at MTs Muhammadiyah

One of the efforts of madrasas to improve the quality of education is through superior programs because this activity has an important role in improving the management of madrasas that are dynamic, innovative, and full of ideas in shaping the development of students’ character who have religious values. This research is (1) to determine the application of superior programs in instilling religious values in students. (2) describe the implications of instilling religious values in students. The focus of this research was carried out at MTs Muhammadiyah 1 Malang, East Java. This research is field research; the type used is a case study with a qualitative approach. Data collection techniques (observation, interviews, documentation) were carried out on 25 informants through “Focus Group Discussion” (FGD). First, the process of implementing this superior program is carried out through various religious activities, forming an implementation schedule; coaching activities through homeroom teachers; exemplary values; and evaluating rewards/punishments as well as coordinating the institution with the homeroom teacher. This program aims to foster an Islamic environment, develop religious character, and integrate Islamic values into students’ daily lives. Second, the implications of this superior program can strengthen character, such as worship habits; provide guidance and motivation to students; strengthening morality, honesty, justice; building cooperation, mutual respect; build discipline, responsibility, and get closer to Allah SWT. Therefore, schools must continue to consider the long term so that the contribution of implementing superior programs in creating students’ religious values can be increased more effectively.

Multi-view human pose and shape estimation via mesh-aligned voxel interpolation

Although multi-view human pose and shape regression methods have information from other views for complementing and correcting, existing ones still have its own drawback of not fully taking advantage of multi-view setup. Thus they are far from efficiently aligning and merging features in different views. In order to tackle these problems, we propose a multi-view framework where features from all views are well aligned and merged through multi-view voxel aggregation with inverse projection. Our framework highlights three major characteristics. Firstly, we use a multi-view volumetric aggregation module for better prediction by exploiting various information in different-scale feature maps. Secondly, in our framework, instead of using all voxels, a mesh-aligned voxel selection module is proposed to make effective prediction by eliminating redundant background voxels. Lastly, the framework further improves the performance of human body parametric modeling by adopting a dual-branch strategy, where one branch for parametric human model prediction and the other for 3D keypoints prediction. Their mutual influence is critical to the improvement for both tasks. Additionally, we find the scarcity of datasets also hinders the development of multi-view methods, so we propose a approach for creating occlusion datasets specifically for multi-view occlusion case. Experimental results verify the effectiveness of the proposed framework on two benchmarks, Human3.6M and MPI-INF-3DHP.

Maneuvering extended target tracking method based on transformer network

This research introduces a new approach to address the challenges related to inaccuracies in shape estimation and motion state tracking during the tracking of maneuvering extended targets. The method combines a novel neural network model with a cubature Kalman filter. Initially, the target’s shape variation is represented using a second-order autoregressive model, and Bayesian filtering is employed for target contour estimation in static environments. Subsequently, the kinematic state tracking of maneuvering targets is enhanced by modifying a neural network model based on Transformer architecture. Through the effective integration of these techniques, precise tracking of maneuvering extended targets is achieved, facilitating accurate estimation of irregular contour information while tracking the targets’ motion state in real-time. The efficacy of the proposed approach is further confirmed through various simulation scenarios, underscoring its suitability for tracking maneuvering extended targets.

Sample size considerations for single-arm clinical trials with time-to-event endpoint using the gamma distribution

BackgroundTime-to-event (TTE) endpoints are evaluated as the primary endpoint in single-arm clinical trials; however, limited options are available in statistical software for sample size calculation. In single-arm trials with TTE endpoints, the non-parametric log-rank test is commonly used. Parametric options for single-arm design assume survival times follow exponential distribution or Weibull distribution. MethodsThe exponential- or Weibull-distributed survival time assumption does not always reflect hazard pattern of real-life diseases. We therefore propose gamma distribution as an alternative parametric option for designing single-arm studies with TTE endpoints. We outline a sample size calculation approach using gamma distribution with a known shape parameter and explain how to extract the gamma shape estimate from previously published resources. In addition, we conduct simulations to assess the accuracy of the extracted gamma shape parameter and to explore the impact on sample size calculation when survival time distribution is misspecified. ResultsOur simulations show that if a previously published study (sample sizes ≥ 60 and censoring proportions ≤ 20 %) reported median and inter-quartile range of survival time, we can obtain a reasonably accurate gamma shape estimate, and use it to design new studies. When true survival time is Weibull-distributed, sample size calculation could be underestimated or overestimated depending on the hazard shape. ConclusionsWe show how to use gamma distribution in designing a single-arm trial, thereby offering more options beyond the exponential and Weibull. We provide a simulation-based assessment to ensure an accurate estimation of the gamma shape and recommend caution to avoid misspecification of the underlying distribution.

Detection and estimation of 3D shape of waste based on neural networks

Abstract This article considers the possibility of using neural networks to detect and recognize different types of waste and determine its shape. The developed algorithm can be used to automate the waste sorting process for further sorting, recycling or disposal. This article deals with the detection of 28 classes of objects made of iron, plastic, cardboard and glass with varying degrees of deformation and damage. The detection is based on the YOLO8 neural network. The quality of the YOLOv8 model was evaluated on test data using the following metrics: box_loss, cls_loss, dfl_loss, mAP50, mAP50-95, precision, recall. After training the neural network, the YOLOv8 metrics on the test data are: box_loss=1.14, cls_loss=2.21, dfl_loss=1.21, mAP50=0.48, mAP50-95=0.38, precision=0.60, recall=0.44. The 3d shape detection is done after analyzing the image by YOLOv8 model based on GLPN neural network.

Continuum topological derivative - A novel application tool for segmentation of CT and MRI images

IntroductionComputed Tomography (CT) and Magnetic Resonance Imaging (MRI) are essential tools for unraveling anatomical and tissue properties, particularly in the head and brain. CT provides high-contrast images, particularly valuable in cases such as cerebral bleeds, and also aids in estimating cranial deformities and organ shape deviations. MRI, on the other hand, offers excellent imaging of cerebral artery regions, allowing analysis of various cerebral pathologies through different sequences. Beyond detecting common head and brain disorders, these modalities play a crucial role in identifying abnormalities in orbits, middle cerebral artery territories, brain ventricles, soft tissues, and bones. A unique aspect of brain MRI is its ability to produce multiplanar brain assessments. Both head/brain CT and MRI are invaluable for studying haemorrhage cases, with segmentation of affected areas providing detailed images for further analysis. This study explores the application of a novel mathematical technique, continuum topological derivative (CTD), for CT and MR image segmentation. MethodsThe initial stage of Continuum Topological Derivative (CTD) segmentation involves preprocessing CT and MR images due to their susceptibility to inherent noises, such as quantum mottle, and Gaussian and Rayleigh noises, respectively. In this study, we have implemented the CTD denoising algorithm to produce denoised CT/MR images, serving as ground truth for subsequent segmentation steps. Validation of the denoised CTD CT/MR images was conducted through minimal residual value computation across all case studies. Following this, segmentation of the region of interest was performed using the CTD technique, with comparisons made against Discrete Topological Derivatives (DTD), k-mean clustering and Adaptive Threshold methods. Evaluation of the proposed CTD algorithm's effectiveness in segmentation involved calculating performance metrics such as Jaccard and dice indices to assess spatial overlap of segmented images. ResultsThe CTD technique yields excellent segmentation results, not only for the delineated region of interest but also for volume-based cerebral blood areas and anomalies in the middle cerebral artery (MCA) and its territorial areas, which are substantiated through performance metrics and visual inspection by trained radiologist. This aids in determining the severity of stroke in affected patients. Additionally, a unique attempt is made to apply CTD to Electrical Impedance Tomography (EIT) images of the lungs for precise estimation of the breathing cycle. CTD successfully generates standardized images, demonstrating attenuation and density characteristics for cerebral cisterns, arteries, and ventricles. DiscussionThe denoised images obtained through CTD facilitate thorough analysis of both normal and pathological conditions, providing radiologists with enhanced capabilities to identify subtle details, particularly in areas such as abnormal cerebral artery territories, haemorrhage cases, cisterns, ventricles and arteries. Results clearly demonstrate that the combination of CTD denoising and segmentation outperforms the other three established methods in terms of both efficiency and accuracy in delineating diseased or affected areas, as evidenced by the various case studies conducted in this research. In summary, the proposed CTD method aims to delineate boundaries and contours of the region of interest, facilitating precise estimation of size and shape for accurate detection of the extent of diseased or affected areas.