Image Segmentation Research Articles

Pre‐trained SAM as data augmentation for image segmentation

AbstractData augmentation plays an important role in training deep neural model by expanding the size and diversity of the dataset. Initially, data augmentation mainly involved some simple transformations of images. Later, in order to increase the diversity and complexity of data, more advanced methods appeared and evolved to sophisticated generative models. However, these methods required a mass of computation of training or searching. In this paper, a novel training‐free method that utilises the Pre‐Trained Segment Anything Model (SAM) model as a data augmentation tool (PTSAM‐DA) is proposed to generate the augmented annotations for images. Without the need for training, it obtains prompt boxes from the original annotations and then feeds the boxes to the pre‐trained SAM to generate diverse and improved annotations. In this way, annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation model. Multiple comparative experiments on three datasets are conducted, including an in‐house dataset, ADE20K and COCO2017. On this in‐house dataset, namely Agricultural Plot Segmentation Dataset, maximum improvements of 3.77% and 8.92% are gained in two mainstream metrics, mIoU and mAcc, respectively. Consequently, large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.

Medical Image Segmentation : A Comparative Study and Survey

Image segmentation involves dividing an image into distinct segments. It is important to distinguish this from image enhancement, which focuses on improving the image’s visual attributes, such as brightness, contrast, and texture. In segmentation, specific parts of the image are emphasized based on the problem being addressed. Clinical imaging techniques are continuously evolving, aiming to improve the quality of services in the healthcare industry. Methods such as interpolation, image registration, compression, and diagnosis need to be updated to meet the increasing demands of the field, especially with advancements in mobile and cloud computing technologies. The integration of medical devices with wearable technologies presents a promising area for further exploration. This paper provides valuable insights into the domain of medical imaging systems and aims to outline the future potential of work in this field.

Specular highlight removal by federated generative adversarial network with attention mechanism

Specular highlight removal ensures the acquisition of high-quality images, which finds its important applications in stereo matching, text recognition and image segmentation. In order to prevent the leakage of images containing personal information, such as identification card (ID) photos, clients often train specular highlight removal models using local data resulting in a lack of precision and generalization of the trained model. To address this challenge, this paper introduces a new method to remove highlight in images using federated learning (FL) and attention generative adversarial network (AttGAN). Specifically, the former builds a global model in the central server and updates the global model by aggregating model parameters of clients. This process does not involve the transmission of image data, which enhances the privacy of clients; the later combining attention mechanisms and generative adversarial network aims to improve the quality of highlight removal by focusing on key image regions, resulting in more realistic and visually pleasing results. The proposed FL-AttGAN method is numerically evaluated, using SD1, SD2 amd RD datasets. The results show that the proposed FL-AttGAN outperforms existent methods.

Segmentation of Glacier Area Using U-Net through Landsat Satellite Imagery for Quantification of Glacier Recession and Its Impact on Marine Systems

Glaciers have experienced a global trend of recession within the past century. Quantification of glacier variations using satellite imagery has been of great interest due to the importance of glaciers as freshwater resources and as indicators of climate change. Spatiotemporal glacier dynamics must be monitored to quantify glacier variations. The potential methods to quantify spatiotemporal glacier dynamics with increasing complexity levels include detecting the terminus location, measuring the length of the glacier from the accumulation zone to the terminus, quantifying the glacier surface area, and measuring glacier volume. Although some deep learning methods designed purposefully for glacier boundary segmentation have achieved acceptable results, these models are often localized to the region where their training data were acquired and further rely on the training sets that were often curated manually to highlight glacial regions. Due to the very large number of glaciers, it is practically impossible to perform a worldwide study of glacier dynamics using manual methods. As a result, an automated or semi-automated method is highly desirable. The current study has built upon our previous works moving towards identification methods of the 2D glacier profile for glacier area segmentation. In this study, a deep learning method is proposed for segmentation of temporal Landsat images to quantify the glacial region within the Mount Cook/Aoraki massif located in the Southern Alps/Kā Tiritiri o te Moana of New Zealand/Aotearoa. Segmented glacial regions can be further utilized to determine the relationship of their variations due to climate change. This model has demonstrated promising performance while trained on a relatively small dataset. The permanent ice and snow class was accurately segmented at a 92% rate by the proposed model.

A Modified Deep Semantic Segmentation Model for Analysis of Whole Slide Skin Images

Automated segmentation of biomedical image has been recognized as an important step in computer-aided diagnosis systems for detection of abnormalities. Despite its importance, the segmentation process remains an open challenge due to variations in color, texture, shape diversity and boundaries. Semantic segmentation often requires deeper neural networks to achieve higher accuracy, making the segmentation model more complex and slower. Due to the need to process a large number of biomedical images, more efficient and cheaper image processing techniques for accurate segmentation are needed. In this article, we present a modified deep semantic segmentation model that utilizes the backbone of EfficientNet-B3 along with UNet for reliable segmentation. We trained our model on Non-melanoma skin cancer segmentation for histopathology dataset to divide the image in 12 different classes for segmentation. Our method outperforms the existing literature with an increase in average class accuracy from 79 to 83%. Our approach also shows an increase in overall accuracy from 85 to 94%.

Accelerating Neural Network Inference in Handwritten Digit Recognition — Comparative Study

In deep learning, one of the popular subclasses of multilayer perceptron is the Convolutional Neural Networks (CNNs), optimized for image and semantic segmentation tasks. With a large number of floating-point operations and relatively less data transfer in the training phase, CNNs are well suited to be handled by parallel architectures. The high accuracy of CNNs comes at the cost of consequential compute and memory demands. In this article, we present a comparative analysis of a pre-trained CNN framework for recognizing a handwritten digit on different processing platforms. The performance of the neural network as the function of the parallel processing platforms offers parametric insights and factors that influence the inference on state-of-the-art Graphics Processing Units (GPUs) and systolic arrays such as Eyeriss and Tensor Processing Unit (TPU). Through inference time analysis, we observed that the systolic arrays can outperform upto 58.7 times better than a Turing architecture powered GPU. We show that while the efficiency with which the available resources are utilized is higher in the existing GPUs (upto 32%) than the TPUs, the efficiency of application-specific systolic arrays can be on par with that of GPUs. We present the results obtained from three different customized systolic array-based platforms which can be adopted by the designers to decide the hardware optimization goal.

Hybrid Deep Learning Framework for Melanoma Diagnosis Using Dermoscopic Medical Images

Background: Melanoma, or skin cancer, is a dangerous form of cancer that is the major cause of the demise of thousands of people around the world. Methods: In recent years, deep learning has become more popular for analyzing and detecting these medical issues. In this paper, a hybrid deep learning approach has been proposed based on U-Net for image segmentation, Inception-ResNet-v2 for feature extraction, and the Vision Transformer model with a self-attention mechanism for refining the features for early and accurate diagnosis and classification of skin cancer. Furthermore, in the proposed approach, hyperparameter tuning helps to obtain more accurate and optimized results for image classification. Results: Dermoscopic shots gathered by the worldwide skin imaging collaboration (ISIC2020) challenge dataset are used in the proposed research work and achieved 98.65% accuracy, 99.20% sensitivity, and 98.03% specificity, which outperforms the other existing approaches for skin cancer classification. Furthermore, the HAM10000 dataset is used for ablation studies to compare and validate the performance of the proposed approach. Conclusions: The achieved outcome suggests that the proposed approach would be able to serve as a valuable tool for assisting dermatologists in the early detection of melanoma.

IC-IE-AKS-O: an automatic recognition method for coastal slope landslide areas

Automatically and accurately identifying the deformation zone of coastal slope landslides is crucial for exploring the mechanism of landslides and predicting landslide disasters. To this end, this study proposes an integrated automatic recognition method combining Image Clipping (IC), Image Information Enhancement (IE), Adaptive K-means Clustering Segmentation (AKS), and Optimization (O): IC-IE-AKS-O, which achieves precise extraction of the deformation area in coastal slope landslide images. Firstly, due to the more complex natural environment of field slopes, to extend the monitoring duration, we introduce a hierarchical operation algorithm based on the HSV color model, which effectively mitigates the impact of sunlight, rain, and foggy weather on image recognition accuracy. Secondly, this study proposes a 2D landslide image segmentation technique that combines K-means clustering with global threshold segmentation for landslide images, enabling the segmentation of small image regions with precision. Finally, we combine image information enhancement technology with image segmentation technology. To verify its effectiveness, we identify a landslide image of a coastal slope in Pingtan. The method displays an average relative error of 5.20% and 5.14% in the X and Y directions, respectively. Its advantages are threefold: (1) The combination of image information enhancement and segmentation techniques can more accurately identify landslide areas that appear blurred in the image; (2) expanding the temporal dimension of coastal slope monitoring; (3) providing excellent boundary conditions and segmentation results. The practical application of this method ensures the stable and accurate operation of the coastal slope monitoring system, providing a safeguard for the sustainable development of marine safety.

A review of deep learning approaches for multimodal image segmentation of liver cancer.

This review examines the recent developments in deep learning (DL) techniques applied to multimodal fusion image segmentation for liver cancer. Hepatocellular carcinoma is a highly dangerous malignant tumor that requires accurate image segmentation for effective treatment and disease monitoring. Multimodal image fusion has the potential to offer more comprehensive information and more precise segmentation, and DL techniques have achieved remarkable progress in this domain. This paper starts with an introduction to liver cancer, then explains the preprocessing and fusion methods for multimodal images, then explores the application of DL methods in this area. Various DL architectures such as convolutional neural networks (CNN) and U-Net are discussed and their benefits in multimodal image fusion segmentation. Furthermore, various evaluation metrics and datasets currently used to measure the performance of segmentation models are reviewed. While reviewing the progress, the challenges of current research, such as data imbalance, model generalization, and model interpretability, are emphasized and future research directions are suggested. The application of DL in multimodal image segmentation for liver cancer is transforming the field of medical imaging and is expected to further enhance the accuracy and efficiency of clinical decision making. This review provides useful insights and guidance for medical practitioners.

Computer aided diagnoses for detecting the severity of Keratoconus

Abstract Problem Corneal topography instruments have limited parameter constraints for calculating precise defect ratios on the basis of the cone base area of the anterior axial curvature map for patients with Keratoconus (KC). Aim The aim of this study is to use thresholding-based segmentation and morphological techniques to calculate the pathological ratio of the keratoconic cornea through cone base area extraction for the detection of KC severity and tracking of disease development. Methods Data were collected from February 2022 to March 2023, comprising 97 cases from private clinics in southern Iraq. Disease severity was categorized into three stages, namely, mild, moderate, and severe, according to the topographic KC classification by a senior ophthalmologist. The Galilei system was used in obtaining the corneal topography images. The study proposed an image analysis method for corneal topography images using MATLAB R2020a. The method had four main steps: preprocessing, image segmentation, morphological processing, and pathological ratio calculation. Moreover, pathological ratio was compared with the KC severity through statistical analysis. A P-value less than 0.05 indicated statistically significant results. Results The majority of the cases in the mild category had a pathological ratio of ≤20%, and the moderate category had a higher prevalence ranging from 21 to 40%. The severe category had the highest distribution (<40%). A P-value of <0.001 indicated significant and clear link between KC stages and pathologic ratio. Conclusion The algorithm used for extracting the cone base area of the keratoconic cornea at different stages was validated by an ophthalmic specialist to ensure that the cone base area was appropriately extracted. The findings may help ophthalmologists to make informed decisions for patients with severe KC and assessments based on the percentage of corneal defects.

Joint segmentation of tumors in 3D PET-CT images with a network fusing multi-view and multi-modal information

Objective. Joint segmentation of tumors in positron emission tomography-computed tomography (PET-CT) images is crucial for precise treatment planning. However, current segmentation methods often use addition or concatenation to fuse PET and CT images, which potentially overlooks the nuanced interplay between these modalities. Additionally, these methods often neglect multi-view information that is helpful for more accurately locating and segmenting the target structure. This study aims to address these disadvantages and develop a deep learning-based algorithm for joint segmentation of tumors in PET-CT images.Approach. To address these limitations, we propose the Multi-view Information Enhancement and Multi-modal Feature Fusion Network (MIEMFF-Net) for joint tumor segmentation in three-dimensional PET-CT images. Our model incorporates a dynamic multi-modal fusion strategy to effectively exploit the metabolic and anatomical information from PET and CT images and a multi-view information enhancement strategy to effectively recover the lost information during upsamping. A Multi-scale Spatial Perception Block is proposed to effectively extract information from different views and reduce redundancy interference in the multi-view feature extraction process.Main results. The proposed MIEMFF-Net achieved a Dice score of 83.93%, a Precision of 81.49%, a Sensitivity of 87.89% and an IOU of 69.27% on the Soft Tissue Sarcomas dataset and a Dice score of 76.83%, a Precision of 86.21%, a Sensitivity of 80.73% and an IOU of 65.15% on the AutoPET dataset.Significance. Experimental results demonstrate that MIEMFF-Net outperforms existing state-of-the-art models which implies potential applications of the proposed method in clinical practice.

Deep spectral improvement for unsupervised image instance segmentation.

Recently, there has been growing interest in deep spectral methods for image localization and segmentation, influenced by traditional spectral segmentation approaches. These methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing the Laplacian of the affinity matrix to obtain eigensegments. However, instance segmentation has received less attention than other tasks within the context of deep spectral methods. This paper addresses that not all channels of the feature map extracted from a self-supervised backbone contain sufficient information for instance segmentation purposes. Some channels are noisy and hinder the accuracy of the task. To overcome this issue, this paper proposes two channel reduction modules, Noise Channel Reduction (NCR) and Deviation-based Channel Reduction (DCR). The NCR retains channels with lower entropy, as they are less likely to be noisy, while DCR prunes channels with low standard deviation, as they lack sufficient information for effective instance segmentation. Furthermore, the paper demonstrates that the dot product, commonly used in deep spectral methods, is not suitable for instance segmentation due to its sensitivity to feature map values, potentially leading to incorrect instance segments. A novel similarity metric called Bray-curtis over Chebyshev (BoC) is proposed to address this issue. This metric considers the distribution of features in addition to their values, providing a more robust similarity measure for instance segmentation. Quantitative and qualitative results on the Youtube-VIS 2019 and OVIS datasets highlight the improvements achieved by the proposed channel reduction methods and using BoC instead of the conventional dot product for creating the affinity matrix. These improvements regarding mean Intersection over Union (mIoU) and extracted instance segments are observed, demonstrating enhanced instance segmentation performance. The code is available on: https://github.com/farnooshar/SpecUnIIS.

An integrated method for detecting lung cancer via CT scanning via optimization, deep learning, and IoT data transmission.

With its increasing global prevalence, lung cancer remains a critical health concern. Despite the advancement of screening programs, patient selection and risk stratification pose significant challenges. This study addresses the pressing need for early detection through a novel diagnostic approach that leverages innovative image processing techniques. The urgency of early lung cancer detection is emphasized by its alarming growth worldwide. While computed tomography (CT) surpasses traditional X-ray methods, a comprehensive diagnosis requires a combination of imaging techniques. This research introduces an advanced diagnostic tool implemented through image processing methodologies. The methodology commences with histogram equalization, a crucial step in artifact removal from CT images sourced from a medical database. Accurate lung CT image segmentation, which is vital for cancer diagnosis, follows. The Otsu thresholding method and optimization, employing Colliding Bodies Optimization (CBO), enhance the precision of the segmentation process. A local binary pattern (LBP) is deployed for feature extraction, enabling the identification of nodule sizes and precise locations. The resulting image underwent classification using the densely connected CNN (DenseNet) deep learning algorithm, which effectively distinguished between benign and malignant tumors. The proposed CBO+DenseNet CNN exhibits remarkable performance improvements over traditional methods. Notable enhancements in accuracy (98.17%), specificity (97.32%), precision (97.46%), and recall (97.89%) are observed, as evidenced by the results from the fractional randomized voting model (FRVM). These findings highlight the potential of the proposed model as an advanced diagnostic tool. Its improved metrics promise heightened accuracy in tumor classification and localization. The proposed model uniquely combines Colliding Bodies Optimization (CBO) with DenseNet CNN, enhancing segmentation and classification accuracy for lung cancer detection, setting it apart from traditional methods with superior performance metrics.

Medical image segmentation method based on confidence learning and collaborative training

Confidence learning plays an important role in the training of low-quality annotated data of medical images. However, the current applications of confidence learning are based on the average teacher model, and the possibility of confidence learning on other networks has not been explored. To address this problem, a segmentation model based on confidence learning and co-training is proposed. The model uses two different networks to encourage the output results of the two networks to be consistent. Then, the output of one of the networks is compared with the original low-quality annotations using confidence learning, and the low-quality annotated data is corrected to provide an effective training reference. The proposed model is compared on three medical image datasets of different modalities. Experimental results show that the segmentation effect of the proposed model is better than that of the existing confidence learning model.

Ischemic Stroke Segmentation by Transformer and Convolutional Neural Network Using Few-Shot Learning

Stroke is a major factor in causing disability and fatalities. Doctors use computerized tomography (CT) and magnetic resonance imaging (MRI) scans to assess the severity of a stroke. Automatic image segmentation can help doctors diagnose strokes more quickly and accurately, but it is challenging due to the variability of stroke lesions and the limited availability of labeled data. Deep learning is the cutting-edge technique of machine learning and artificial intelligence, which needs an extensive labeled dataset for effective training. Unfortunately, in the medical domain, the availability of labeled data is severely limited, posing a challenge for conventional deep learning approaches. In this paper, we introduce a system that utilizes deep learning in the form of fusing transformer-based and convolutional neural network (CNN)-based features and few-shot learning techniques to segment ischemic strokes in multimedia MRIs. To accomplish this, we employ two different methods. The first method involves parallel fusion, where we combine CNN-based and transformer-based features. The second method utilizes serial fusion, combining CNN-based and transformer models using few-shot learning. Through the integration of transformer and CNN models, we can extract both global and local features and enhance the system's performance. Moreover, we tackle the issue of limited labeled data by integrating few-shot learning techniques. Additionally, our system optimizes efficiency by selecting only the slices with lesions, disregarding unlesioned slices. The system under consideration is trained with the BraTS2020 dataset, evaluated on the ISLES 2015 dataset, and contrasted the performance with cutting-edge systems. The suggested system attains a dice coefficient score of 0.76, surpassing the scores of previous cutting-edge systems by a substantial margin.

Detection and isolation of brain tumors in cancer patients using neural network techniques in MRI images

MRI imaging primarily focuses on the soft tissues of the human body, typically performed prior to a patient's transfer to the surgical suite for a medical procedure. However, utilizing MRI images for tumor diagnosis is a time-consuming process. To address these challenges, a new method for automatic brain tumor diagnosis was developed, employing a combination of image segmentation, feature extraction, and classification techniques to isolate the specific region of interest in an MRI image corresponding to a brain tumor. The proposed method in this study comprises five distinct steps. Firstly, image pre-processing is conducted, utilizing various filters to enhance image quality. Subsequently, image thresholding is applied to facilitate segmentation. Following segmentation, feature extraction is performed, analyzing morphological and structural properties of the images. Then, feature selection is carried out using principal component analysis (PCA). Finally, classification is performed using an artificial neural network (ANN). In total, 74 unique features were extracted from each image, resulting in a dataset of 144 observations. Principal component analysis was employed to select the top 8 most effective features. Artificial Neural Networks (ANNs) leverage comprehensive data and selective knowledge. Consequently, the proposed approach was evaluated and compared with alternative methods, resulting in significant improvements in precision, accuracy, and F1 score. The proposed method demonstrated notable increases in accuracy, with improvements of 99.3%, 97.3%, and 98.5% in accuracy, Sensitivity and F1 score. These findings highlight the efficiency of this approach in accurately segmenting and classifying MRI images.

An Automated Clubbed Fingers Detection System Based on YOLOv8 and U-Net: A Tool for Early Prediction of Lung and Cardiovascular Diseases

Background/Objectives: Lung and cardiovascular diseases are leading causes of mortality worldwide, yet early detection remains challenging due to the subtle symptoms. Digital clubbing, characterized by the bulbous enlargement of the fingertips, serves as an early indicator of these diseases. This study aims to develop an automated system for detecting digital clubbing using deep-learning models for real-time monitoring and early intervention. Methods: The proposed system utilizes the YOLOv8 model for object detection and U-Net for image segmentation, integrated with the ESP32-CAM development board to capture and analyze finger images. The severity of digital clubbing is determined using a custom algorithm based on the Lovibond angle theory, categorizing the condition into normal, mild, moderate, and severe. The system was evaluated using 1768 images and achieved cloud-based and real-time processing capabilities. Results: The system demonstrated high accuracy (98.34%) in real-time detection with precision (98.22%), sensitivity (99.48%), and specificity (98.22%). Cloud-based processing achieved slightly lower but robust results, with an accuracy of 96.38%. The average processing time was 0.15 s per image, showcasing its real-time potential. Conclusions: This automated system provides a scalable and cost-effective solution for the early detection of digital clubbing, enabling timely intervention for lung and cardiovascular diseases. Its high accuracy and real-time capabilities make it suitable for both clinical and home-based health monitoring.

Bi-VLGM: Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation

AbstractMedical reports containing specific diagnostic results and additional information not present in medical images can be effectively employed to assist image understanding tasks, and the modality gap between vision and language can be bridged by vision-language matching (VLM). However, current vision-language models distort the intra-model relation and only include class information in reports that is insufficient for segmentation task. In this paper, we introduce a novel Bi-level class-severity-aware Vision-Language Graph Matching (Bi-VLGM) for text guided medical image segmentation, composed of a word-level VLGM module and a sentence-level VLGM module, to exploit the class-severity-aware relation among visual-textual features. In word-level VLGM, to mitigate the distorted intra-modal relation during VLM, we reformulate VLM as graph matching problem and introduce a vision-language graph matching (VLGM) to exploit the high-order relation among visual-textual features. Then, we perform VLGM between the local features for each class region and class-aware prompts to bridge their gap. In sentence-level VLGM, to provide disease severity information for segmentation task, we introduce a severity-aware prompting to quantify the severity level of disease lesion, and perform VLGM between the global features and the severity-aware prompts. By exploiting the relation between the local (global) and class (severity) features, the segmentation model can include the class-aware and severity-aware information to promote segmentation performance. Extensive experiments proved the effectiveness of our method and its superiority to existing methods. The source code will be released.

Using 2D U-Net convolutional neural networks for automatic acetabular and proximal femur segmentation of hip MRI images and morphological quantification: a preliminary study in DDH

BackgroundDevelopmental dysplasia of the hip (DDH) is a common pediatric orthopedic condition characterized by varying degrees of acetabular dysplasia and hip dislocation. Current 2D imaging methods often fail to provide sufficient anatomical detail for effective treatment planning, leading to higher rates of misdiagnosis and missed diagnoses. MRI, with its advantages of being radiation-free, multi-planar, and containing more anatomical information, can provide the crucial morphological and volumetric data needed to evaluate DDH. However, manual techniques for measuring parameters like the center–edge angle (CEA) and acetabular index (AI) are time-consuming. Automating these processes is essential for accurate clinical assessments and personalized treatment strategies.MethodsThis study employed a U-Net-based CNN model to automate the segmentation of hip MRI images in children. The segmentation process was validated using a leave-one-out method during training. Subsequently, the segmented hip joint images were utilized in clinical settings to perform automated measurements of key angles: AI, femoral neck angle (FNA), and CEA. This automated approach aimed to replace manual measurements and provide an objective reference for clinical assessments.ResultsThe U-Net-based network demonstrates high effectiveness in hip segmentation compared to manual radiologist segmentations. In test data, it achieves average DSC values of 0.9109 (acetabulum) and 0.9244 (proximal femur), with a 91.76% segmentation success rate. The average ASD values are 0.3160 mm (acetabulum) and 0.6395 mm (proximal femur) in test data, with Ground Truth (GT) edge points and predicted segmentation maps having a mean distance of less than 1 mm. Using automated segmentation models for clinical hip angle measurements (CEA, AI, FNA) shows no statistical difference compared to manual measurements (p > 0.05).ConclusionUtilizing U-Net-based image segmentation and automated measurement of morphological parameters significantly enhances the accuracy and efficiency of DDH assessment. These methods improve precision in automatic measurements and provide an objective basis for clinical diagnosis and treatment of DDH.

Development of ExG, ExR, ExGR, HSV, CIELAB Images from RGB Images Using Image Segmentation Algorithm in Computer Vision Based Herbicide Spraying Applications

Weed management in agriculture is critical for preventing crop yield losses, with traditional methods often leading to environmental harm and increased production costs. This study explores the development of color indices and models for weed detection in computer vision-based herbicide spraying applications. Among the all sensors, RGB colour cameras offer several advantages, including low cost and wide availability of image processing libraries tailored for RGB image analysis. In present study a Logitech C270 webcam was used for acquiring the RGB images and a specially python algorithm was developed for image segmentation based on color. Excess Green (ExG), Excess Red (ExR), Excess Green minus Red (ExGR), HSV, and CIELAB color model images are developed by using algorithm. The research demonstrates that while ExG and ExGR indices are effective under specific conditions, the CIELAB model offers superior segmentation results across varying lighting environments. Among all these color indices and models, particularly CIELAB, can enhance the precision of weed detection in automated herbicide applications, thereby reducing environmental impact and improving agricultural efficiency.