Key Segmentation Research Articles

Customer Segmentation for Credit Card Customers : A Comprehensive Guide

This comprehensive article explores customer segmentation in the credit card industry, examining its theoretical foundations, methodologies, practical applications, and future trends. It discusses the evolution of segmentation techniques from traditional demographic-based approaches to advanced AI-driven models. The article highlights key segmentation criteria, data collection and preparation methods, and various segmentation techniques, including RFM analysis, clustering algorithms, and machine learning approaches. Case studies of successful implementations by American bankholding companies, multinational financial services corporations, and American bank-holding companies demonstrate the tangible benefits of effective segmentation strategies. The article also addresses common challenges in customer segmentation and provides recommendations for credit card companies to optimize their segmentation efforts. Finally, it explores future trends, including the increasing role of big data and AI in segmentation and the impact of evolving customer expectations on segmentation strategies.

CAPSULE NETWORK FOR BRAIN TUMORS SEGMENTATION IN MRI IMAGES

Glioma is an abnormal, irregularly shaped cell growth in the brain. Diagnosing glioma often relies on magnetic resonance imaging (MRI), which requires precise segmentation of the tumor for effective analysis. Convolutional Neural Networks (CNN), such as U-Net, is widely used in medical image processing, particularly for image segmentation tasks. However, CNNs have the limitation in effectively learning spatial relationships within images. To address this, Capsule Networks (CapsNet) is introduced, utilizing capsule dynamic routing to better capture spatial hierarchies. This paper aims to investigate the performance of SegCaps, a segmentation model based on Capsule Networks, for brain glioma segmentation in MRI images, compared to the CNN-based U-Net model. Both models were tested on the BraTS2018 glioma dataset, which includes 374 MRI images of brain tumors across four modalities (T1, T1c, T2, FLAIR). The performance of SegCaps and U-Net was evaluated using two key segmentation metrics: Dice coefficient and Jaccard index. The results show that SegCaps outperformed U-Net with a Dice coefficient of 87.96% compared to U-Nets 85.56%, demonstrating a 2.4% improvement. Additionally, SegCaps required fewer parameters than the U-Net model, highlighting its efficiency. In conclusion, SegCaps can be considered as a promising alternative for glioma segmentation in MRI images. Future work could focus on refining the SegCaps model to enhance its performance while reducing computational costs.

Lightweight hashing with contrastive self-cross XLNet and combinational similarity matching for content-based video retrieval

ABSTRACT Text-to-video retrieval systems enable simple and natural video searches, but existing methods often employ single queries with noisy annotation and low-quality descriptions. A novel lightweight hashing with Contrastive self-cross XLNet and Combinational similarity matching is proposed to provide high-quality descriptions and clarity in annotation. Issues in existing text and video processing approaches, such as redundancy in keyword generation and extracting frames without considering optical flow estimation of keyframes, are solved using a novel Textual Labelling and Automated key frame segmentation. The 3D Cuboid Net with Lightweight Hashing is proposed, which selects features and converts them into hash codes using Lightweight LSS-Net based hashing, reducing memory utilization. Additionally, a novel Combinational Attentive Text-Video Retrieval is proposed, which eliminates multiple narrow extrema matching error surfaces and increases retrieval validity. The model efficiently solves video retrieval problems with high accuracy, recall, and sensitivity. This novel approach addresses the limitations of existing text-to-video retrieval methods and improves the overall quality of the retrieval process.

Ionograms Trace Extraction Method Based on Multiscale Transformer Network

The echo traces in the ionograms contain key information about the ionosphere. Therefore, the accurate extraction of these traces is crucial for the subsequent work. This paper transforms the original signal processing problem into a semantic segmentation task, combines it with the currently popular deep learning techniques, and proposes a multiscale Transformer network to achieve pixel-level trace extraction. To train the proposed model, we built a dataset by discretizing the original echo data, labeling, and other preprocessing work. A series of advanced semantic segmentation networks are utilized for comparative experiments. The analysis of the results indicates that the proposed network excels in performance, achieving the highest scores on key semantic segmentation evaluation metrics, including mIoU, Kappa, Dice, and AUC-ROC. In addition, this paper also designs a series of ablation experiments to observe the changes in network performance and to evaluate the rationality of the network design. The experimental results demonstrate the effectiveness of the network in the trace extraction task, which plays a positive role in the subsequent electron density reversal work.

Table image dewarping with key element segmentation

Table image dewarping with key element segmentation

A Prior-Guided Dual Branch Multi-Feature Fusion Network for Building Segmentation in Remote Sensing Images

The domain of remote sensing image processing has witnessed remarkable advancements in recent years, with deep convolutional neural networks (CNNs) establishing themselves as a prominent approach for building segmentation. Despite the progress, traditional CNNs, which rely on convolution and pooling for feature extraction during the encoding phase, often fail to precisely delineate global pixel interactions, potentially leading to the loss of vital semantic details. Moreover, conventional CNN-based segmentation models frequently neglect the nuanced semantic differences between shallow and deep features during the decoding phase, which can result in subpar feature integration through rudimentary addition or concatenation techniques. Additionally, the unique boundary characteristics of buildings in remote sensing images, which offer a rich vein of prior information, have not been fully harnessed by traditional CNNs. This paper introduces an innovative approach to building segmentation in remote sensing images through a prior-guided dual branch multi-feature fusion network (PDBMFN). The network is composed of a prior-guided branch network (PBN) in the encoding process, a parallel dilated convolution module (PDCM) designed to incorporate prior information, and a multi-feature aggregation module (MAM) in the decoding process. The PBN leverages prior region and edge information derived from superpixels and edge maps to enhance edge detection accuracy during the encoding phase. The PDCM integrates features from both branches and applies dilated convolution across various scales to expand the receptive field and capture a more comprehensive semantic context. During the decoding phase, the MAM utilizes deep semantic information to direct the fusion of features, thereby optimizing segmentation efficacy. Through a sequence of aggregations, the MAM gradually merges deep and shallow semantic information, culminating in a more enriched and holistic feature representation. Extensive experiments are conducted across diverse datasets, such as WHU, Inria Aerial, and Massachusetts, revealing that PDBMFN outperforms other sophisticated methods in terms of segmentation accuracy. In the key segmentation metrics, including mIoU, precision, recall, and F1 score, PDBMFN shows a marked superiority over contemporary techniques. The ablation studies further substantiate the performance improvements conferred by the PBN’s prior information guidance and the efficacy of the PDCM and MAM modules.

Development and evaluation of two open-source nnU-Net models for automatic segmentation of lung tumors on PET and CT images with and without respiratory motion compensation

ObjectivesIn lung cancer, one of the main limitations for the optimal integration of the biological and anatomical information derived from Positron Emission Tomography (PET) and Computed Tomography (CT) is the time and expertise required for the evaluation of the different respiratory phases. In this study, we present two open-source models able to automatically segment lung tumors on PET and CT, with and without motion compensation.Materials and methodsThis study involved time-bin gated (4D) and non-gated (3D) PET/CT images from two prospective lung cancer cohorts (Trials 108237 and 108472) and one retrospective. For model construction, the ground truth (GT) was defined by consensus of two experts, and the nnU-Net with 5-fold cross-validation was applied to 560 4D-images for PET and 100 3D-images for CT. The test sets included 270 4D- images and 19 3D-images for PET and 80 4D-images and 27 3D-images for CT, recruited at 10 different centres.ResultsIn the performance evaluation with the multicentre test sets, the Dice Similarity Coefficients (DSC) obtained for our PET model were DSC(4D-PET) = 0.74 ± 0.06, improving 19% relative to the DSC between experts and DSC(3D-PET) = 0.82 ± 0.11. The performance for CT was DSC(4D-CT) = 0.61 ± 0.28 and DSC(3D-CT) = 0.63 ± 0.34, improving 4% and 15% relative to DSC between experts.ConclusionsPerformance evaluation demonstrated that the automatic segmentation models have the potential to achieve accuracy comparable to manual segmentation and thus hold promise for clinical application. The resulting models can be freely downloaded and employed to support the integration of 3D- or 4D- PET/CT and to facilitate the evaluation of its impact on lung cancer clinical practice.Clinical relevance statementWe provide two open-source nnU-Net models for the automatic segmentation of lung tumors on PET/CT to facilitate the optimal integration of biological and anatomical information in clinical practice. The models have superior performance compared to the variability observed in manual segmentations by the different experts for images with and without motion compensation, allowing to take advantage in the clinical practice of the more accurate and robust 4D-quantification.Key PointsLung tumor segmentation on PET/CT imaging is limited by respiratory motion and manual delineation is time consuming and suffer from inter- and intra-variability.Our segmentation models had superior performance compared to the manual segmentations by different experts.Automating PET image segmentation allows for easier clinical implementation of biological information.

Semantic segmentation for multisource remote sensing images incorporating feature slice reconstruction and attention upsampling

ABSTRACT Multisource remote sensing images have rich features and high interpretability and are widely employed in many applications. However, highly unbalanced category distributions and complex backgrounds have created some difficulties in the application of remote sensing image semantic segmentation tasks, such as low accuracy of small target segmentation and inaccurate edge extraction. To solve these problems, in this paper, a feature map segmentation reconstruction module and an attention upsampling module are proposed. In the encoder part, the input feature map is equally segmented, and the segmented feature map is enlarged to effectively improve the small target feature information expression ability in the model. In the decoder part, the key segmentation and location information of shallow features are obtained using the global view. The deep semantic information and shallow spatial location information are fully combined to achieve a more refined upsampling operation. In addition, the attention mechanism of the spatial and channel squeeze and excitation block (scSE) is applied to pay more attention to important features and to suppress irrelevant background and redundant information. To verify the effectiveness of the proposed method, the WHU-OPT-SAR dataset and six state-of-the-art algorithms are utilized in comparative experiments. The experimental results show that our model has demonstrated the best performance and low computational complexity. With only approximately half the floating-point operation count and the number of model parameters of the MCANet model, which is specially designed for the dataset, our model surpasses MCANet by 1.52% and 1.53% in terms of mean intersection over union (mIoU) and F1 score, respectively. In particular, for small object regions such as roads and other categories, compared to the baseline model, the IoU and F1 score of our model are improved by 5.27% and 3.99% and by 5.68% and 5.65%, respectively. These results demonstrate the superior performance of our model in terms of accuracy and efficiency.

A Method for Identifying Sports Behaviors in Sports Adversarial Project Training Based on Image Block Classification under the Internet of Things

Abstract There is a need to devise intelligent methods for motion-based image classification to produce accurate results for correct judgment. Existing methods cannot use the image block classification method to recognize the motion behavior of antagonistic sports training, and it is the need of the hour to devise newer methods. The recognition result is not ideal when using the traditional methods. This paper proposes an image block classification method for antagonistic sports training behavior recognition. It classifies the noise sources, quantifies the influence degree of each noise source effectively, and adjusts the noise factor in the denoising method for different noise sources and the specific weight of different noise sources. The adaptive denoising of different noise types of antagonistic sports training image sequences is realized. By using the combination of key frame template selection method and image segmentation method for behavior representation, the features are extracted well. The feature extraction is done on an image region according to the proportion of the number of foreground pixels in each block of the template to the number of pixels in the block. The feature vector is used to form the template. Aiming at the behavior representation and feature extraction based on the features of skeleton joint points, image block classification is used to extract the coordinates of skeleton joint points during human movement. K-means clustering is used to transform them into symbol sequences to represent behavior features that are used for antagonistic sports training behavior recognition. Simulation results show that the proposed method can obtain ideal recognition results and outperforms the existing methods.

Spacecraft-DS: A Spacecraft Dataset for Key Components Detection and Segmentation via Hardware-in-the-Loop Capture

Spacecraft-DS: A Spacecraft Dataset for Key Components Detection and Segmentation via Hardware-in-the-Loop Capture

PointDMM: A Deep-Learning-Based Semantic Segmentation Method for Point Clouds in Complex Forest Environments

Background. With the advancement of “digital forestry” and “intelligent forestry”, point cloud data have emerged as a powerful tool for accurately capturing three-dimensional forest scenes. It enables the creation and presentation of digital forest systems, facilitates the monitoring of dynamic changes such as forest growth and logging processes, and facilitates the evaluation of forest resource fluctuations. However, forestry point cloud data are characterized by its large volume and the need for time-consuming and labor-intensive manual processing. Deep learning, with its exceptional learning capabilities, holds tremendous potential for processing forestry environment point cloud data. This potential is attributed to the availability of accurately annotated forestry point cloud data and the development of deep learning models specifically designed for forestry applications. Nonetheless, in practical scenarios, conventional direct annotation methods prove to be inefficient and time-consuming due to the complex terrain, dense foliage occlusion, and uneven sparsity of forestry point clouds. Furthermore, directly applying deep learning frameworks to forestry point clouds results in subpar accuracy and performance due to the large size, occlusion, sparsity, and unstructured nature of these scenes. Therefore, the proposal of accurately annotated forestry point cloud datasets and the establishment of semantic segmentation methods tailored for forestry environments hold paramount importance. Methods. A point cloud data annotation method based on single-tree positioning to enhance annotation efficiency was proposed and challenges such as occlusions and sparse distribution in forestry environments were addressed. This method facilitated the construction of a forestry point cloud semantic segmentation dataset, consisting of 1259 scenes and 214.4 billion points, encompassing four distinct categories. The pointDMM framework was introduced, a semantic segmentation framework specifically designed for forestry point clouds. The proposed method first integrates tree features using the DMM module and constructs key segmentation graphs utilizing energy segmentation functions. Subsequently, the cutpursuit algorithm is employed to solve the graph and achieve the pre-segmentation of semantics. The locally extracted forestry point cloud features from the pre-segmentation are comprehensively inputted into the network. Feature fusion is performed using the MLP method of multi-layer features, and ultimately, the point cloud is segmented using the lightweight PointNet. Result. Remarkable segmentation results are demonstrated on the DMM dataset, achieving an accuracy rate of 93% on a large-scale forest environment point cloud dataset known as DMM-3. Compared to other algorithms, the proposed method improves the accuracy of standing tree recognition by 21%. This method exhibits significant advantages in extracting feature information from artificially planted forest point clouds obtained from TLS. It establishes a solid foundation for the automation, intelligence, and informatization of forestry, thereby possessing substantial scientific significance.

Comparison evaluation of unet-based models with noise augmentation for breast cancer segmentation on ultrasound image

This paper investigates the application of convolutional neural networks (CNNs), particularly the UNet model architecture, to improve the accuracy of breast cancer tumor segmentation in ultrasound images. Accurate identification of breast cancer is essential for effective patient treatment. However, ultrasound images, often contain noise and artifacts, which can complicate the task of tumor segmentation. Therefore, to highlight the most robust architecture, modifications were made to the original set, including the addition of noise and fuzziness. In this study, a comparative study of five different variants of UNet models (UNet, Attention UNet, UNet++, Dense Inception UNet and Residual UNet) was conducted on a diverse set of ultrasound images with different breast tumors. Using consistent training methods and techniques of augmentation and adding noise to the data, an improvement in segmentation accuracy was highlighted when using the Dense Inception UNet architecture. The results have potential practical applications in the field of medical diagnosis and can assist medical professionals in automatic tumor segmentation in breast cancer ultrasound images. This study highlights the improvement of segmentation accuracy by introducing dense induction into the UNet architecture. Importantly, the Dice coefficient, a key segmentation metric, improved markedly, increasing from 0.973 to 0.976 after data augmentation. The results of the study offer promise to the medical community by offering a more accurate and reliable approach to segmenting breast cancer lesions on ultrasound images. The findings can be implemented in clinical practice to assist radiologists in early cancer diagnosis.

Key information-guided networks for medical image segmentation in medical systems

Motivated by the goals of overcoming inherent engineering difficulties on segmentation accuracy arising from low contrast and complex tissue relationships in medical images, this paper investigates how key information can be extracted and analyzed in a targeted manner to discover relationships between tissues and enhance feature differentiation. It proposes the Key Information-guided segmentation algorithm, which consists of two key components: the Key Region-guided Sparse Self-Attention (KRSSA) module and the Hard Pixels Multisubclass Contrast learning (HPMCL) module. The former is applied in the encoder by activating the importance of global features and adaptively selecting attentional regions. Experimental results show that propagating large amounts of complex and redundant information suppresses learning. The latter is applied in the final decision layer, where the feature space of each class is partitioned into several subclasses, and hard pixels are continuously pulled towards the closest subclass of their respective class, away from all subclasses of other classes by contrastive learning. Experimental results show that moving pixels towards subclasses rather than a single class center leads to more effective segmentation results. In summary, we propose a novel visual model and extensively validate it on the Brain Tumor Segmentation 2019 Challenge (BraTS2019) dataset, BraTS2020 dataset, and the Medical Segmentation Decathlon (MSD) dataset, namely in brain tumors and the hippocampus. The Dice similarity coefficients of 0.806, 0.904, and 0.853 for the enhanced tumor, whole tumor, and tumor core, respectively, in the Brats2020 validation set ranked fourth out of 345 participating methods. This method has significant implications for addressing challenges in medical image segmentation, including low attention to key regions, class imbalance, and background interference.

Evaluation and Selection of Market Segments Based on the TOPSIS Technique Application

Researching and choosing market segmentation is one of the most crucial marketing considerations for every company. For many businesses, choosing a target market is one of the most crucial marketing choices. This choice also has an ef-fect on the market mix, purchasing and distribution plans, as well as other business considerations. A strong target mar-ket is selected based on the results of the market segmentation assessment, a variety of parameters, including the size of the segment, the existence of competitors, risk, and profitability are taken into consideration. This article looks at some key segmentation models in respect to the characteristics of industrial markets. Many marketing decisions, including those involving the market mix selection, purchasing, the supply chain, distribution channels, etc., are centered on the target market. Market sector A few steps must be taken to ensure that each market segment's desirability is assessed be-fore one (or more) is selected for entrance. A market is thought to be segmented through this procedure into many sub-groups. The target market that can be reached can be chosen as customers and a subset. a distinctive marketing mixture (Cutler and Keller, 2005). As a multi-objective decision-making process, the TOPSIS approach can be used to address questions with numerous objectives. This study suggests that the TOPSIS approach, which has had positive outcomes, be used to assess foreign players when they are introduced to CBA teams. Because of the increasingly competitive industry, businesses must choose wisely when it comes to marketing issues. One of the most important difficulties is the evaluation and choice of market segmentation. The results show that different sectors may have varying success potential and that choosing the best might help firms fulfill their commitment to expanding their operations. This study can be used as a model for future research and the identification of market categories.

Evaluation of Business Model of E-Wallets of Nepal

This article is intended to evaluate business model elements adopted by Nepalese e-wallets and to find out weak points in its implementation. This article also suggests ways to reduce any loss through which they are running through proper mathematical interpretation. Grey-TOPSIS method is a mathematical interpretation tool that can be used to analyze raw data. The limitation of this research is that it is based on costumer’s opinion rather than being based on company’s data. Though the output of result may vary from one costumer to another, sample population taken into consideration in research have shown that the reason behind loss of e-wallets is lack of proper identification of revenue stream by entrepreneurs. Besides revenue streams, e-wallets should also prioritize their focus on their key activities, customer segmentation, key resources, value proposition, cost structure, key partners and external relationships respectively. The success of e-wallets is not far away if they prioritize their activities as per these findings.

How to Survive in the Shadow of Sharing Economy Giants: Business Model Innovation for Small and Medium-Sized Platforms

This study aims to explore how small and medium-sized platforms in the sharing economy can gain a competitive advantage in a market monopolized by giants. Taking China’s ride-hailing industry as an example, the k-means clustering method was employed to compare and analyze the characteristics and differences of 207 small and medium-sized platforms based on the framework of the business model canvas. An empirical typology comprising four representative successful business models of small and medium-sized ride-hailing platforms is extracted from the dimensions of key partners, value propositions, customer segmentation, and cost-revenue structure: high-end business platforms, Minibus-hailing platforms, Intercity carpool platforms, and Aggregation mode platforms. The results show that small and medium-sized platforms in the sharing economy can rely on their own characteristics and advantages to seek new potential development directions and gain development space by constructing value propositions and operating models that are different from industry giants. Specifically, it is necessary to further implement refined market identification and segmentation to continuously develop differentiated competitive advantages. In particular, more effective and open business strategies should be explored and practiced to greatly expand the network effects and business boundaries of small and medium-sized platforms, thereby significantly enhancing their competitiveness in the ride-hailing market. This study provides a new perspective on the reconstruction of market exchange in the sharing economy through the prism of small and medium-sized digital platforms and has certain important theoretical significance for supplementing and advancing existing research.

A posture-based measurement adjustment method for improving the accuracy of beef cattle body size measurement based on point cloud data

Beef cattle body size is an important phenotypic trait for breeding and production. The non-contact automatic calculation of beef cattle body sizes via point cloud data exhibits several disadvantages, such as the inaccurate positioning of measurement points and the sensitivity to postures, heavily restricting commercial applications. To address these issues, an automatic method for key region segmentation, body size calculation, and data calibration to accurately measure the body size parameters of beef cattle in different postures is proposed. Firstly, the body sizes were obtained based on the point cloud data. Then the twelve micro-posture feature parameters were defined and extracted from four categories corresponding to the head, back, torso, and leg regions. Based on those parameters, a Posture-based Measurement Adjustment model was established to reduce the influence of postures on calculation results based on the correlation between measurement errors and micro-posture features. The experimental results show that, after the adjustment model calibration, the average errors of body oblique length, wither height, hip height, chest girth, and abdominal girth were 1.14%, 1.84%, 3.47%, 1.56%, and 2.36%, respectively, whereas the corresponding maximum errors were 4.36%, 8.10%, 11.10%, 7.29%, and 9.26%. The measurement errors of body sizes can be significantly minimised by the adjustment model, which can improve the robustness of different postures. The adjustment model improves the accuracy of the non-contact body size measurement, which could provide guidance and inspirations for future research.

의사결정나무분석을 활용한 코로나19 이후 농촌관광객의 선호 특성 세분화 연구

Purpose - The purpose of this study was to explore and diagnose the characteristics and behavioural patterns of rural tourists after COVID-19 using decision tree analysis to classify and identify key segmentation groups.
 Design/methodology/approach - The CHAID algorithm was used as the analysis technique for the decision tree. The explanatory variables used in the analysis of each decision tree model were demographic variables and rural tourism usage behaviour and perception variables, and the target variables were the preferences of rural tourists' activities after COVID-19. From the Rural Tourism 2020 survey data, 614 samples with rural tourism experience were extracted and used in the analysis.
 Findings - The variables that significantly explained the preference for each type of rural tourism activity after COVID-19 were rural tourism safety perception, repeated visits to the region, rural tourism priority activity, rural tourism accommodation experience, gender, age group, marital status, occupation, and education level. Among them, rural tourism safety perception was the most important explanatory variable in each analysis model.
 Research implications or Originality - Overall, to promote rural tourism, it is necessary to enhance the safety image of rural tourism, strengthen loyalty programs for repeat visitors, and develop customized products that reflect the preferred trends of rural tourism.

Information Security Protection of Internet of Energy Using Ensemble Public Key Algorithm under Big Data

This work aims to solve the specific problem in the Power Internet of Things (PIoT). PIoT is vulnerable to monitoring, tampering, forgery, and other attacks during frequent data interaction under the background of big data, leading to a severe threat to the power grid’s Information Security (ISEC). Cryptosystems can solve ISEC problems, such as confidentiality, data integrity, authentication, identity recognition, data control, and nonrepudiation. Thereupon, this work expounds on cryptography from public-key encryption and digital signature and puts forward the model of network information attack. Then, the security of the two cryptograms is certified against the two cyberattack modes. On this basis, an Identity-based Combined Encryption and Signature (IBCES) ensemble scheme is proposed by combining public-key encryption with the digital signature. Finally, the security of the proposed IBCES’s encryption and the signature schemes is verified, and the results prove their feasibility. The results show that the proposed IBCEs are effective and feasible, fully meeting the information confidentiality requirements. Additionally, smart grid against Information Security (ISEC) algorithms must comprehensively consider network resources and computing power. This work creatively combines the two cryptosystems. The proposal breaks the traditional key segmentation principle by applying the same key to different cryptosystems and ensures the independent security of the two cryptosystems. The conclusion provides technical support for future research on cryptography.

An automatic segmentation framework for computer-assisted renal scintigraphy procedure.

One of the techniques for achieving unique and reliable information in medicine is renal scintigraphy. A key step for quantitative renal scintigraphy is segmentation of the kidneys. Here, an automatic segmentation framework was proposed for computer-aided renal scintigraphy procedures. To extract kidney boundary in dynamic renal scintigraphic images, a multi-step approach was proposed. This technique is featured with key steps, namely, localization and segmentation. At first, the ROI of each kidney was estimated using Otsu's thresholding, anatomical constraint, and integral projection, which is done in an automatic process. Afterwards, the ROI obtained for the kidneys was used as the initial contours to create the final counter of kidneys using geometric active contours. At this step and for the segmentation, an improved variational level set was utilized through Mumford-Shah formulation. Using e.cam gamma camera system (SIEMENS), 30 data sets were used to assess the proposed method. By comparing the manually outlined borders, the performance of the proposed method was shown. Different measures were used to examine the performance. It was found that the proposed segmentation method managed to extract the kidney boundary in renal scintigraphic images. The proposed technique achieved a sensitivity of 95.15% and a specificity of 95.33%. In addition, the section under the curve in the ROC analysis was equal to 0.974. The proposed technique successfully segmented the renal contour in dynamic renal scintigraphy. Using all the data sets, a correct segmentation of the kidney was performed. In addition, the technique was successful with noisy and low-resolution images and challenging cases with close interfering activities such as liver and spleen activities.