Level Set Algorithm Research Articles

Numerical simulation on multi-well fracturing considering multiple thin layers in vertical direction

Multiwell fracturing is a key technology for developing shale gas and shale oil reservoirs. In this study, a multiple planar 3D (PL3D) fracture simulator that can capture multiple thin layers was developed to examine the propagation of multiple fractures during multicluster fracturing in multiple horizontal wells. The simulator considers multiple thin layers in the vertical direction. The results of the model are validated against the analytical solution of a single radial fracture and the implicit level set algorithm (ILSA). Using the simulator, a series of numerical simulations based on the field case are performed to investigate the fracture propagation mechanism of multiwell fracturing. The completion sequence, well placement pattern, well spacing, and cluster spacing are investigated to optimize the treatment parameters. The effective fracture area is used to quantitatively describe the stimulation effect. The adaptability of the completion sequence and well placement pattern is also analysed from the perspective of “frac hits”. The results show that the completion sequence has a critical influence on the stimulation effect and fracture geometry. From the perspective of avoiding “frac-hit” fractures, fracturing the low-stress layer can form an “artificial stress barrier”, which slightly protects the well from interference from other fractures. The staggered well pattern is better than the stacked well pattern. Compared with the stacked pattern, the staggered pattern can reduce the overlap area of fractures by 80 %, which greatly reduces the probability of “frac-hits”. With increasing well spacing from 200 m to 500 m, the fracture area increases by 25 %, and the degree of uneven stimulation between the two pay zones also increases by 6 %. Considering that a small well spacing is prone to “frac hits”, a large well spacing leads to an unstimulated area between two wells, and a 350 m well spacing is optimal. The effective fracture area decreases slightly with increasing perforation cluster spacing, but the fracture geometry becomes much more regular. The results can be helpful for the field design of multiwell fracturing.

An Intelligent Parameter Identification Algorithm of Linear Fuzzy Fractional Differential Equation

Abstract The traditional linear generation fuzzy fractional differential equation parameter identification algorithm lacks the update of parameter identification process, has large amount of calculation, slow convergence speed of parameter identification and strong dependence on initial values. In this paper, a new intelligent recognition algorithm for linearly generated fuzzy fractional differential equations is proposed. The parameters of the equation are re expressed by the constant level set. The piecewise constant level set algorithm based on equation parameter intelligent identification is used to solve the steady-state solution of fractional differential equation, and the non convergence problem caused by too much calculation is solved. A new algorithm scheme for linearly generating fuzzy fractional differential equations is established, the constraints of the level set of the differential equations are calculated, and the updated algorithm for parameter identification of the equation is obtained. The evolutionary algorithm is used to solve the updating algorithm to realize the intelligent identification algorithm of linear fractional fuzzy differential equations. Experimental results show that the algorithm has the advantages of fast convergence speed, high calculation accuracy and low initial value.

Front stability of infinitely steep travelling waves in population biology

Reaction–diffusion models are often used to describe biological invasion, where populations of individuals that undergo random motility and proliferation lead to moving fronts. Many reaction–diffusion models of biological invasion are extensions of the well–known Fisher–Kolmogorov–Petrovskii–Piskunov model that describes the spatiotemporal evolution of a 1D population density, u(x,t) , as a result of linear diffusion with flux J=−∂u/∂x , and logistic growth source term, S=u(1−u) . In 2020 Fadai introduced a new reaction–diffusion model of biological invasion with a nonlinear degenerate diffusive flux, J=−u∂u/∂x , and the model was formulated as a moving boundary problem on 0<x<L(t) , with u(L(t),t)=0 and dL(t)/dt=−κu∂u/∂x at x=L(t) (Fadai and Simpson 2020 J. Phys. A: Math. Theor. 53 095601). Fadai’s model leads to travelling wave solutions with infinitely steep, well–defined fronts at the moving boundary, and the model has the mathematical advantage of being analytically tractable in certain parameter limits. In this work we consider the stability of the travelling wave solutions presented by Fadai. We provide general insight by first presenting two key extensions of Fadai’s model by considering: (i) generalised nonlinear degenerate diffusion with flux J=−um∂u/∂x for some constant m > 0; and, (ii) solutions describing both biological invasion with dL(t)/dt>0 , and biological recession with dL(t)/dt<0 . After establishing the existence of travelling wave solutions for these two extensions, our main contribution is to consider stability of the travelling wave solutions by introducing a lateral perturbation of the travelling wavefront. Full 2D time–dependent level–set numerical solutions indicate that invasive travelling waves are stable to small amplitude lateral perturbations, whereas receding travelling waves are unstable. These preliminary numerical observations are corroborated through a linear stability analysis that gives more formal insight into short time growth/decay of wavefront perturbation amplitude. Julia–based software, including level–set algorithms, is available on Github to replicate all results in this study.

A Novel Method for Peanut Seed Plumpness Detection in Soft X-ray Images Based on Level Set and Multi-Threshold OTSU Segmentation

The accurate assessment of peanut seed plumpness is crucial for optimizing peanut production and quality. The current method is mainly manual and visual inspection, which is very time-consuming and causes seed deterioration. A novel imaging technique is used to enhance the detection of peanut seed fullness using a non-destructive soft X-ray, which is suitable for the analysis of the surface or a thin layer of a material. The overall grayscale of the peanut is similar to the background, and the edge of the peanut seed is blurred. The inaccuracy of peanut overall and peanut seed segmentation leads to low accuracy of seed plumpness detection. To improve accuracy in detecting the fullness of peanut seeds, a seed plumpness detection method based on level set and multi-threshold segmentation was proposed for peanut images. Firstly, the level set algorithm is used to extract the overall contour of peanuts. Secondly, the obtained binary image is processed by morphology to obtain the peanut pods (the peanut overall). Then, the multi-threshold OTSU algorithm is used for threshold segmentation. The threshold is selected to extract the peanut seed part. Finally, morphology is used to complete the cavity to achieve the segmentation of the peanut seed. Compared with optimization algorithms, in the segmentation of the peanut pods, average random index (RI), global consistency error (GCE) and variation of information (VI) were increased by 10.12% and decreased by 0.53% and 24.11%, respectively. Compared with existing algorithms, in the segmentation of the peanut seed, the average RI, VI and GCE were increased by 18.32% and decreased by 9.14% and 6.11%, respectively. The proposed method is stable, accurate and can meet the requirements of peanut image plumpness detection. It provides a feasible technical means and reference for scientific experimental breeding and testing grading service pricing.

Evaluation of children's oral diagnosis and treatment using imaging examination using AI based Internet of Things.

Although cone beam computed tomography (CBCT) plays an important role in the diagnosis and treatment of oral diseases, its image segmentation method needs to be further improved, and there are still objections about the clinical application effect of general anesthesia (GA) on children's dental fear (CDF). This study aimed to investigate the application value of CBCT based on intelligent computer segmentation model in oral diagnosis and treatment of children in the context of biomedical signals, and to analyze the alleviating effect of GA on CDF. Based on the regional level set (CV) algorithm, the local binary fitting (LBF) model was introduced to optimize it, and the tooth CBCT image segmentation model CV-LBF was established to compare the segmentation accuracy (SA), maximum symmetric surface distance (MSSD), average symmetric surface distance (ASSD), over segmentation rate (OR), and under segmentation rate (UR) between these model and other algorithms. 82 children with CDF were divided into general anesthesia group (GAG) (n= 38) and controls (n= 44) according to the voluntary principle of their families. Children in GAG were treated with GA and controls with protective fixed intervention. Children's fear survey schedule-dental subscale (CFSS-DS) and Venham scores were counted before intervention in the two groups. CFSS-DS scores were recorded at 2 hours after intervention and after recovery in children in GAG. CFSS-DS and Venham scores were performed in all children 1 week after surgery. The results showed that the S⁢A value of CV-LBF algorithm was higher than that of region growing algorithm (P< 0.05). OR, UR, MSSD, and ASSD values of CV-LBF algorithm were evidently lower than those of other algorithms (P< 0.05). CFSS-DS scores were lower in GAG than in controls 2 hours after intervention and at return visits after 1 week of intervention (P< 0.001), and Venham scores were lower in GAG than in controls after intervention (P< 0.001). After intervention, the proportion of children with Venham grade 0, 1, 2, and 3 was obviously higher in GAG than in controls (P< 0.001), while the proportion of children with Venham grade 4 and 5 was clearly higher in controls than in GAG (P< 0.001). The results revealed that the computer intelligent segmentation model CV-LBF has potential application value in CBCT image segmentation of children's teeth, and GA can effectively alleviate anxiety of children with CDF and can be used as biomedical signals.

Model-based hybrid variational level set method applied to lung cancer detection

&lt;p&gt;The precise segmentation of lung lesions in computed tomography (CT) scans holds paramount importance for lung cancer research, offering invaluable information for clinical diagnosis and treatment. Nevertheless, achieving efficient detection and segmentation with acceptable accuracy proves to be challenging due to the heterogeneity of lung nodules. This paper presents a novel model-based hybrid variational level set method (VLSM) tailored for lung cancer detection. Initially, the VLSM introduces a scale-adaptive fast level-set image segmentation algorithm to address the inefficiency of low gray scale image segmentation. This algorithm simplifies the (Local Intensity Clustering) LIC model and devises a new energy functional based on the region-based pressure function. The improved multi-scale mean filter approximates the image’s offset field, effectively reducing gray-scale inhomogeneity and eliminating the influence of scale parameter selection on segmentation. Experimental results demonstrate that the proposed VLSM algorithm accurately segments images with both gray-scale inhomogeneity and noise, showcasing robustness against various noise types. This enhanced algorithm proves advantageous for addressing real-world image segmentation problems and nodules detection challenges.&lt;/p&gt;

Medical Image Segmentation Algorithm Based on Markup Improvement Combining the Watershed Algorithm and the Level Set Algorithm

Medical Image Segmentation Algorithm Based on Markup Improvement Combining the Watershed Algorithm and the Level Set Algorithm

Enhancement and Segmentation of Low-Light Images Using Illumination Map Estimation based Level Set (IME-LS) Method

One of the most difficult aspects of image segmentation is that it cannot be successfully segmented if the image is dark or degraded. In this paper, proposes a method for the segmentation of low-quality and degraded images is put forth which is called Illumination Map Estimation based Level Set (IME-LS) Method. This proposed model has been classified into two parts: Firstly, we use an enhancement approach using illumination map approximation to enhance the input image. In this approach, Illumination map is constructed then refined. The refined illumination map undergoes an Augmented Lagrangian algorithm and then we use a sped-up solver for a considerably more efficient outcome. Secondly, Then the segmentation procedure begins once the image is enhanced. The enhanced image is segmented using level set bias method through Fuzzy clustering. In this method we employ the Fuzzy C-Means (FCM) algorithm to categorize data points into clusters. The fuzzy C-Means algorithm separates different entities in the image based on their varied intensities and sorts them into various clusters. The level set bias approach then tracks the variational boundaries of the image. We have designed the integrated algorithm in such a way that the image is classified or grouped into various clusters using a novel fuzzy Clustering algorithm and the variational boundaries of those clusters are tracked by employing the level set algorithm. In this paper, we further perform quantitative and comparative analysis of the suggested technique with respect to other segmentation techniques to illustrate the efficiency and flexibility of the suggested model.

Digital core reconstruction based on discrete element and Markov chain-Monte Carlo methods

The digital core is an essential platform for pore-scale flow simulation in geoenergy exploitation, CO2 storage, and hydrogen storage. However, the existing reconstruction methods, such as the Markov chain-Monte Carlo (MCMC) method, can not obtain the digital core reflecting the influence of complex stress. Moreover, the discrete element method (DEM)-based reconstruction, which can consider the effect of stress, has yet to be reported to consider the actual particle shape. To this end, a hybrid reconstruction method based on DEM and MCMC methods is proposed in this study. Firstly, watershed and level set algorithms are used to segment and extract the contours of rock particles, and clump in PFC2D is used to fill the contours to establish a clump template library. Then, the initial condition (ground condition) digital core slice is established with clump packing based on porosity and particle size distribution. Subsequently, the stress loading simulation is carried out to obtain 2D digital core slices with different stress combinations. Finally, the MCMC method obtains 3D digital cores. This proposed method is used to reconstruct the digital core of Belgian fieldstone with different stresses and effective moduli, and the evolution of pore structure characteristics is analyzed. Results show that the stress compresses the range of the pore radius distribution curve, resulting in the shortening of the throat length distribution; the conductivity of the model is poor. The maximum reduction of porosity and permeability is 6.56% and 24.81%, respectively. The model with a large effective modulus has a stronger resistance to deformation and can protect the pore structure. These results prove that the method proposed can obtain the digital core reflecting the stress effects and provide a reference for accurately describing the in-situ rocks' pore structure and the pore-scale flow simulation.

Segmentation of Ocular Thermogram Using Level-set Algorithm for Analysis of Contralateral Portions in Healthy Eyes

This work aimed to evaluate the level set segmentation algorithm on ocular surface thermograms. In addition, the vascularity functioning between the contralateral portions of two eyes (right and left) was identified using statistical analysis methods. A total of 25 healthy participants with an average age of 35 years (20 men and 5 women) were selected in April 2022. Thermogram images were captured using a FLIR T series thermal camera. Conventional image processing techniques, such as filtering and edge detection, were used to preprocess thermograms. Next, the level set approach was used with the edge-detected pattern as an input to an automated segmented region of interest (ROI). Five metrics, namely Dice Coefficient, Tanimoto Index, Jaccard Index, Volume Similarity, and Structural Similarity, were used to assess the performance of the segmentation technique compared to ground truth, which showed 97.5%, 92.5%, 94.5%, 96.5%, and 96.5% correlation, respectively, between the segmented and the ground truth images with average values for both the eyes. Statistical analysis demonstrated that the contralateral portions of the ocular thermograms were significantly different in terms of vascular distribution between the left and right eyes (p < 0.005) The level set method efficiently segmented the ROI in ocular thermograms with maximum correlation. According to the segmentation’s results, the model showed the dissimilarity between the contralateral parts of the left and right eyes in healthy cases.

An implicit level set algorithm for hydraulic fracturing with a stress-layer asymptote

The capability to simulate a hydraulic fracturing process is an essential tool that can be used to optimize treatment design and increase the efficiency of field operations. In most practical cases, hydraulic fractures propagate in a multi-layered rock formation. As a result, there is a need to incorporate the effect of such heterogeneities in fracturing models to achieve an accurate prediction. To capture the layered structure of rocks, a hydraulic fracture simulator typically requires a fine mesh, which leads to a drastic reduction in computational performance. An alternative is to use more sophisticated models that are capable of providing reasonably accurate predictions even on a relatively coarse mesh. In the case of fracture growth modeling, the pivotal component of the simulation is a fracture front tracking algorithm that accounts for the layered structure of the formation. Consequently, this paper aims to extend the established Implicit Level Set Algorithm (ILSA) to account for the effect of multiple stress layers within the tip asymptote. The enhanced front tracking algorithm involves the stress-corrected asymptote that incorporates the influence of stress layers within the near-tip region. To further increase the validity region of the stress-corrected asymptote, the stress relaxation factor is introduced, and its accuracy is examined. The numerical algorithm is validated against the reference semi-analytical solutions as well as experimental observations. In addition, we investigate the sensitivity of the fracture geometry to mesh size to demonstrate that the front tracking algorithm based on the stress-corrected asymptote retains its accuracy on a coarse mesh.

Piecewise Acoustic Source Imaging with Unknown Speed of Sound Using a Level-Set Method.

We investigate the following inverse problem: starting from the acoustic wave equation, reconstruct a piecewise constant passive acoustic source from a single boundary temporal measurement without knowing the speed of sound. When the amplitudes of the source are known a priori, we prove a unique determination result of the shape and propose a level set algorithm to reconstruct the singularities. When the singularities of the source are known a priori, we show unique determination of the source amplitudes and propose a least-squares fitting algorithm to recover the source amplitudes. The analysis bridges the low-frequency source inversion problem and the inverse problem of gravimetry. The proposed algorithms are validated and quantitatively evaluated with numerical experiments in 2D and 3D.

On-board MRI image compression using video encoder for MR-guided radiotherapy.

Magnetic resonance imaging (MRI) is currently used for online target monitoring and plan adaptation in modern image-guided radiotherapy. However, storing a large amount of data accumulated during patient treatment becomes an issue. In this study, the feasibility to compress MRI images accumulated in MR-guided radiotherapy using video encoders was investigated. Two sorting algorithms were employed to reorder the slices in multiple MRI sets for the input sequence of video encoder. Three cropping algorithms were used to auto-segment regions of interest for separate data storage. Four video encoders, motion-JPEG (M-JPEG), MPEG-4 (MP4), Advanced Video Coding (AVC or H.264) and High Efficiency Video Coding (HEVC or H.265) were investigated. The compression performance of video encoders was evaluated by compression ratio and time, while the restoration accuracy of video encoders was evaluated by mean square error (MSE), peak signal-to-noise ratio (PSNR), and video quality matrix (VQM). The performances of all combinations of video encoders, sorting methods, and cropping algorithms were investigated and their effects were statistically analyzed. The compression ratios of MP4, H.264 and H.265 with both sorting methods were improved by 26% and 5%, 42% and 27%, 72% and 43%, respectively, comparing to those of M-JPEG. The slice-prioritized sorting method showed a higher compression ratio than that of the location-prioritized sorting method for MP4 (P=0.00000), H.264 (P=0.00012) and H.265 (P=0.00000), respectively. The compression ratios of H.265 were improved significantly with the applications of morphology algorithm (P=0.01890 and P=0.00530), flood-fill algorithm (P=0.00510 and P=0.00020) and level-set algorithm (P=0.02800 and P=0.00830) for both sorting methods. Among the four video encoders, H.265 showed the best compression ratio and restoration accuracy. The compression ratio and restoration accuracy of video encoders using inter-frame coding (MP4, H.264 and H.265) were higher than that of video encoders using intra-frame coding (M-JPEG). It is feasible to implement video encoders using inter-frame coding for high-performance MRI data storage in MR-guided radiotherapy.

A temperature-robust level-set approach for eigenfrequency optimization

The optimization of target eigenfrequencies is crucial for several engineering applications, including dynamical systems. Micro-electro-mechanical systems (MEMS) used in time-keeping applications, for example, require exceptional frequency stability. Most eigenfrequency structural optimization methods focus on a deterministic approach, often neglecting potential fluctuations in operational conditions. Among these, temperature variations have long been known to have a detrimental effect on the natural frequencies of a structure. In this work, we show how eigenfrequency optimization can be applied to the field of structural dynamics while minimizing the variance of natural frequencies caused by external temperature uncertainties. To accomplish this, we employ a level-set optimization algorithm, known for its computational efficiency and ability to define crisp interfaces.

3D PET/CT Tumor Co-Segmentation Based on Background Subtraction Hybrid Active Contour Model

Accurate tumor segmentation in medical images plays an important role in clinical diagnosis and disease analysis. However, medical images usually have great complexity, such as low contrast of computed tomography (CT) or low spatial resolution of positron emission tomography (PET). In the actual radiotherapy plan, multimodal imaging technology, such as PET/CT, is often used. PET images provide basic metabolic information and CT images provide anatomical details. In this paper, we propose a 3D PET/CT tumor co-segmentation framework based on active contour model. First, a new edge stop function (ESF) based on PET image and CT image is defined, which combines the grayscale standard deviation information of the image and is more effective for blurry medical image edges. Second, we propose a background subtraction model to solve the problem of uneven grayscale level in medical images. Apart from that, the calculation format adopts the level set algorithm based on the additive operator splitting (AOS) format. The solution is unconditionally stable and eliminates the dependence on time step size. Experimental results on a dataset of 50 pairs of PET/CT images of non-small cell lung cancer patients show that the proposed method has a good performance for tumor segmentation.

Convolutional neural network-based sea lion optimization algorithm for the detection and classification of diabetic retinopathy.

Diabetic retinopathy (DR) becomes a complicated type of diabetic that causes damage to the blood vessels of the retina's light-sensitive tissue. DR may initially cause mild symptoms or no symptoms. But prolonged DR results in permanent vision loss, and hence, it is necessary to detect the DR at an early stage. Manual diagnosing of DR retina fundus image is a time-consuming process and sometimes leads to misdiagnosis. The existing DR detection model faces few shortcomings in case of improper detection accuracy, higher loss or error values, high feature dimensionality, not suitable for large datasets, high computational complexity, poor performances, unbalanced and limited number of data points, and so on. As a result, the DR is diagnosed in this paper through four critical phases to tackle the shortcomings. The retinal images are cropped during preprocessing to reduce unwanted noises and redundant data. The images are then segmented using a modified level set algorithm based on pixel characteristics. Here, an Aquila optimizer is employed in extracting the segmented image. Finally, for optimal classification of DR images, the study proposes a convolutional neural network-oriented sea lion optimization (CNN-SLO) algorithm. Here, the CNN-SLO algorithm classifies the retinal images into five classes (healthy, moderate, mild, proliferative and severe). The experimental investigation is performed for Kaggle datasets with respect to diverse evaluation measures to deliberate the performances of the proposed system.

Local energy-based image region segmentation using Legendre polynomials

Segmentation of images is a critical pre-processing step in image processing. The objective of segmentation is to divide an image into two homogeneous segments based on similar features. Recently, energy-based segmentation methods, such as the level set algorithm, have exhibited remarkable performance in segmenting various types of image data. However, such methods fail to produce satisfactory results on textured images, leading to inaccurate foreground and background segmentation. To address this issue, the proposed method utilizes a feature space based on image local energy, which is independent of image intensity. This feature space provides a more robust representation of texture in the image. By taking advantage of a unique feature space, images can be represented with a sum of Legendre polynomials. The reproduced image is injected into the L2S algorithm. The level set function is optimized from the average difference of Legendre polynomial coefficients to provide the best representation of the image. Consequently, the proposed method results in better segmentation accuracy, with an average increase of 19.33 compared to existing state-of-the-art methods.

Efficient Prediction of Blast Disease in Paddy Plant using Optimized Support Vector Machine

Diagnostics of plant diseases are essential to predicting yield loss, detecting infections, studying host–pathogen interactions, and detecting host resistance. Early detection of plant leaf disease can increase yields. This is possible if there are automated systems to help farmers diagnose rice diseases from the pictures of plant leaves. Therefore, automatic leaf detection using machine learning is proposed in this study. The proposed approach has three stages, namely, pre-processing, feature extraction, and classification. Initially, the input image is converted into red, green and blue and the noise in the green band is removed using a median filter. Initially, the input image is converted into red, green and blue and the noise in the green band is removed using a median filter. Then, important features of the green band are extracted. An Optimized Support Vector Machine (OSVM) classifier uses the extracted features to classify an image as normal or pathological. To improve SVM performance, the SVM parameters are chosen optimally using the Adaptive Sunflower Optimization (ASFO) algorithm. Then, the infected region is separated using a level-set segmentation algorithm. The efficiency of our work is analyzed based on accuracy, sensitivity, and specificity, the proposed method reached the maximum accuracy of 97.54% for plant disease prediction.

Investigation of the flow characteristics of spherical harmonic particles using the level set method

Non-spherical granular flow is critical for hopper design and applications. Although particle morphology has an evident influence on the dynamic properties of granular materials, a large number of numerical simulations contribute to the dynamic behaviors of convex particles. In this work, concave particles are represented using the spherical harmonic functions, and the accurate collision forces between spherical harmonics particles are obtained by the level set algorithm. Subsequently, the influences of particle sphericity, shape parameters, and hopper angles on the mass flow rate, flow fluctuation, mass flow index, particle velocity, and inter-particle contact forces of spherical harmonics particles are investigated using the discrete element method. Results indicate that spherical harmonic particles with small sphericity and large shape parameters have dense clusters and strong interlocking, which cause larger normal contact forces between particles. As a result, spherical harmonic particles have lower flow rates and more significant flow fluctuations than spheres. • Concave particles are constructed by the spherical harmonic functions. • Contact forces between concave particles are calculated by the level set method. • The flow properties of spherical and concave particles in the hopper are compared. • Concave particles have lower flow rates and larger flow fluctuations than spheres. • The contact forces between concave particles increase with decreasing sphericity.

Cardiac MRI Segmentation Using Efficient ResNeXT-50-Based IEI Level Set and Anisotropic Sigmoid Diffusion Algorithms

Endocardial and epicardial border identification has been of extensive interest in cardiac Magnetic Resonance Images (MRIs). It is a difficult job to segment the epicardium and endocardium accurately and automatically from cardiac MRI owing to the cardiac tissues’ complexity even though the prevailing Deep Learning (DL) methodologies had attained significant success in medical imaging segmentation. Hence, by employing effectual ResNeXT-50-centric Inverse Edge Indicator Level Set (IEILS) and anisotropic sigmoid diffusion algorithms, this system has proposed cardiac MRI segmentation. The work has endured some function for an effectual partition of epicardium and endocardium. Initially, by employing the Truncated Kernel Function (TK)-Trilateral Filter, the noise removal function is executed on the input cardiac MRI. Next, by wielding the ResNeXT-50 IEILS, the Left and Right Ventricular (LV/RV) regions are segmented. The epicardium and endocardium are segmented by the ASD algorithm once the LV/RV is separated from the Left Ventricle (LV) region. Here, the openly accessible Sunnybrook and the Right Ventricle (RV) datasets are wielded. Then, the prevailing state-of-art algorithms are analogized to the outcomes achieved by the proposed framework. Regarding accuracy, sensitivity, and specificity, the proposed methodology executed the cardiac MRI segmentation process precisely along with the other surpassed state-of-the-art methodologies.