Similarity Index Research Articles

A hybrid of supervised and unsupervised deep learning models for multi-vendor kernel conversion of chest CT images

ObjectiveWhen reconstructing a computed tomography (CT) volume, different filter kernels can be used to highlight different structures depending on the medical purpose. The aim of this study was to perform CT conversion for intra-/inter-vendor kernel conversion while preserving image quality. Materials and methodsThis study used CT scans from 632 patients who underwent contrast-enhanced chest CT on either a GE or Siemens scanner. Raw data from each CT scan was reconstructed with Standard and Chest kernels of GE or B10f, B30f, B50f, and B70f kernels of Siemens. In intra-vendor, all images reconstructed with one kernel are paired with another kernel, so the U-Net based supervised method was applied. In the case of inter-vendor where the input and target kernels have each different vendor, Siemens' B30f and GE's Standard kernel were trained through unsupervised image-to-image translation using contrastive learning. ResultsIn the intra-vendor, quantitative evaluation of the image quality of our model showed reasonable performance on the internal test set (structural similarity index measure (SSIM) > 0.96, peak signal-to-noise ratio (PSNR) > 42.55) compared with the SR-block model (SSIM > 0.93, PSNR > 42.92). In the 6-class classification to evaluate the inter-vendor conversion performance, similar accuracy was shown in the converted image (0.977) compared to the original image (0.998). ConclusionsIn this study, we developed a network that can translate a given CT image into a target kernel among multi-vendors. Our model showed clinically acceptable quality in quantitative and qualitative evaluations, including image quality metrics.

Enhancement Method Based on Multi-Strategy Improved Pelican Optimization Algorithm and Application to Low-Illumination Forest Canopy Images

Enhancement is a crucial step in the field of image processing, as it significantly improves image analysis and understanding. One of the most commonly used methods for image contrast enhancement is the incomplete beta function (IBF). However, the key challenge lies in determining the optimal parameters for the IBF. This paper introduces a multi-strategy improved pelican optimization algorithm (MIPOA) to address the low-illumination color image enhancement problem. The MIPOA algorithm utilizes a nonlinear decreasing coefficient to boost the exploration ability and convergence speed, whereas the Hardy–Weinberg principle compensates for the unsound exploitation mechanism. Additionally, the diversity variation operation improves the ability of the algorithm to escape local optimal solutions. The performance of the proposed MIPOA algorithm was evaluated using a benchmark function and was found to outperform five variant algorithms in extensive comparisons. To further harness the potential of the MIPOA algorithm, the authors propose a low-light forest canopy image enhancement method based on the MIPOA algorithm. The MIPOA algorithm searches for the optimal parameters of the IBF, leading to fast contrast enhancement of the image. The segmented gamma correction function is designed to enhance the brightness of the low-light forest canopy images. In determining the optimal parameters of IBF, the MIPOA algorithm demonstrates superior performance compared to other intelligent algorithms in the feature similarity index (FSIM), entropy, and contrast improvement index (CII) of 75%, 58.33%, and 75%, respectively. The proposed MIPOA-based enhancement method achieves a moderate pixel mean and surpasses the conventional enhancement method with an average gradient of 91.67%. The experimental results indicate that the MIPOA effectively addresses the limitations of low optimization accuracy in IBF parameters, and the enhancement method based on the MIPOA provides a more efficacious approach for enhancing low-light forest canopy images.

Palette‐based colour normalization for histopathology images

AbstractWith the emergence of computer‐aided diagnostic (CAD) systems, the speed of histopathological image analysis and the accuracy of cancer detection have considerably improved. However, the appearance of histopathological slides can vary depending on the consistency of tissue thickness, stain concentrations, and equipment, thus affecting the judgment of CAD systems. This study proposes a palette‐based colour normalization method for histopathology images is proposed to solve the problem of colour differences between histopathology images. The method builds a graphical user interface based on an improved palette generation algorithm and colour transfer definitions, through which the user can complete the corresponding colour modification to achieve colour normalization. The evaluation of the metrics on histopathological images shows that our method is able to achieve higher image quality and better preservation of structural information of the source image compared with four other colour normalization algorithms. The peak signal‐to‐noise ratio values obtained by the proposed method on two publicly available datasets were 24.1914 and 21.3666, and the structural similarity index matrix values were 0.9871 and 0.9760. The proposed method provides new ideas for the development and design of CAD systems.

Enhanced Neonatal Brain Tissue Analysis via Minimum Spanning Tree Segmentation and the Brier Score Coupled Classifier

Automatic assessment of brain regions in an MR image has emerged as a pivotal tool in advancing diagnosis and continual monitoring of neurological disorders through different phases of life. Nevertheless, current solutions often exhibit specificity to particular age groups, thereby constraining their utility in observing brain development from infancy to late adulthood. In our research, we introduce a novel approach for segmenting and classifying neonatal brain images. Our methodology capitalizes on minimum spanning tree (MST) segmentation employing the Manhattan distance, complemented by a shrunken centroid classifier empowered by the Brier score. This fusion enhances the accuracy of tissue classification, effectively addressing the complexities inherent in age-specific segmentation. Moreover, we propose a novel threshold estimation method utilizing the Brier score, further refining the classification process. The proposed approach yields a competitive Dice similarity index of 0.88 and a Jaccard index of 0.95. This approach marks a significant step toward neonatal brain tissue segmentation, showcasing the efficacy of our proposed methodology in comparison to the latest cutting-edge methods.

Biodiversity of Aspergillus Species and Their Mycotoxin Production Potential in Dry Meat.

This study aimed to examine fungi diversity in dried beef meat sold in Ekiti State, characterize the isolated fungi, and determine the aflatoxin-producing ability of the Aspergillus fungi in the samples. Dried beef meat was collected from different markets in Ekiti State and screened for the presence of filamentous fungi using molecular methods. Samples were cultured aseptically on potato dextrose agar (PDA) for fungi isolation, and molecular identification was performed using DNA extraction, Polymerase chain Reaction (PCR), ITS-1/ITS-4 primer pair, and nucleotide sequencing. The results obtained indicated a range of filamentous fungi genera including Aspergillus, Rhizopus, Penicillium, Fusarium, Cladosporium, Alternaria, and other fungi species contaminating the dried meat at (43%), (42%), (3%), (2%), (2%), (1%), and (7%), respectively. High incidences were recorded for Aspergillus flavus, Aspergillus niger, and Aspergillus fumigatus in most of the screened samples. Aspergillus flavus accounted for (24.7%) of all the Aspergillus species isolated with the presence of the gene needed for aflatoxin production. The occurrences of these filamentous fungal species pose a cause for concern, as most of these fungal species are known producers of certain toxic substances. Maximum likelihood phylogenetic analysis showed a high similarity index score, which indicated a good relationship between isolated Aspergillus Species and the closely related strains from GenBank, isolated from different sources and countries. The implication of this study is that consumer health may be at risk through exposure to contaminated dried meat.

Color restoration of mural images based on a reversible neural network: leveraging reversible residual networks for structure and texture preservation

AbstractThe Mogao Grottoes in Dunhuang, a treasure of China's and the world's cultural heritage, contains rich historical and cultural deposits and has left precious relics of the history of human art. Over centuries, the Mogao Caves have been affected by natural and human factors, resulting in irreversible fading and discoloration of many murals. In recent years, deep learning technology has shown great potential in the field of virtual mural color restoration. Therefore, this paper proposes a mural image color restoration method based on a reversible neural network. The method first employs an automatic reference selection module based on structural and texture similarity to choose suitable reference mural images for the faded murals. Then, it utilizes a reversible residual network to extract deep features of the mural images without information loss. Next, a channel refinement module is used to eliminate redundant information in the network channels. Finally, an unbiased color transfer module restores the color of the faded mural images. Compared to other image color restoration methods, the proposed method achieves superior color restoration effects while effectively preserving the original structure and texture details of the mural images. Compared to baseline methods, the Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Perception-based Image Quality Evaluator (PIQE) values are improved by 7.97%, 3.46%, and 13.98%, respectively. The color restoration of the Dunhuang Mural holds significant historical, artistic, cultural, and economic values, and plays a positive role in the preservation and inheritance of Chinese culture, as well as in the promotion of cultural exchange and mutual understanding.

Differences in phagosome acidification of Manila clam hemocytes in response to two Perkinsus species with contrasting proliferation dynamics in the host: P. olseni and P. mediterraneus

Understanding the factors determining the host ranges of Perkinsus spp., a significant group of pathogenic protozoans affecting shellfish, is essential for preventing their spread and designing effective control measures. Considering that differences in the ability to proliferate within the host may influence the determination of host range, we first injected six Perkinsus spp. into Manila clams Ruditapes philippinarum and monitored the variations of trophozoite numbers. Although all six species were detected in the challenged clams 28 days post infection, the infection intensities varied among species, and particularly two species showed contrasting infection trends: P. mediterraneus showed a decreasing trend of infection, declining to the lowest intensity, whereas that of P. olseni continuously increased, reaching the highest intensity. In vitro exposure to Manila clam hemocytes revealed that the survival of P. mediterraneus trophozoites was suppressed, in contrast to P. olseni, which maintained their viability. Despite similar phagocytic indices for both species, the rate of phagosome acidification was significantly higher for hemocytes phagocytizing P. mediterraneus than those targeting P. olseni. Notably, phagosome acidification was significantly suppressed in hemocytes phagocytizing live P. olseni trophozoites, suggesting that P. olseni may secrete a substance that modulates phagosome acidification, and thereby evades intracellular digestion by the host’s hemocytes. Conversely, P. mediterraneus, with a lower affinity for infecting Manila clams, did not exhibit such modulation. Based on these results, we consider that the ability to modulate phagosome acidification in host hemocytes might be at least one factor in determining the host range of Perkinsus species.

Denoising astronomical images using an enhanced Pix2Pix model

Astronomical images are frequently affected by sensor noise, which can negatively impact the accuracy of subsequent data analysis. To address this issue, this study proposes an enhanced Pix2Pix generative adversarial network model that incorporates Residual Blocks and Self-Attention mechanisms to improve denoising performance. The effectiveness of the proposed model is evaluated by comparing it with traditional denoising methods, standard Pix2Pix, Pix2Pix with Residual Blocks, and Pix2Pix with Self-Attention using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. The findings demonstrate that the Pix2Pix model, when combined with both Residual Blocks and Self-Attention, significantly outperforms other models in noise reduction and detail preservation. This improved approach offers a robust solution for high-quality processing of astronomical images, providing clearer and more reliable data for scientific analysis. The results highlight the potential of advanced deep learning techniques in overcoming the challenges posed by sensor noise in astronomical imaging.

Integrating Kalman filter noise residue into U-Net for robust image denoising: the KU-Net model

In low-level image processing, where the main goal is to reconstruct a clean image from a noise-corrupted version, image denoising continues to be a critical challenge. Although recent developments have led to the introduction of complex architectures to improve denoising performance, these models frequently have more parameters and higher computational demands. Here, we propose a new, simplified architecture called KU-Net, which is intended to achieve better denoising performance while requiring less complexity. KU-Net is an extension of the basic U-Net architecture that incorporates gradient information and noise residue from a Kalman filter. The network’s ability to learn is improved by this deliberate incorporation, which also helps it better preserve minute details in the denoised images. Without using Image augmentation, the proposed model is trained on a limited dataset to show its resilience in restricted training settings. Three essential inputs are processed by the architecture: gradient estimations, the predicted noisy image, and the original noisy grey image. These inputs work together to steer the U-Net’s encoding and decoding stages to generate high-quality denoised outputs. According to our experimental results, KU-Net performs better than traditional models, as demonstrated by its superiority on common metrics like the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). KU-Net notably attains a PSNR of 26.60 dB at a noise level of 50, highlighting its efficacy and potential for more widespread use in image denoising.

Breaking the rain barrier: A novel approach in image processing with AMGR‐Net

AbstractImage quality is significantly impacted by rain, posing challenges in fields like surveillance, autonomous driving, and outdoor robotics. The field of image deraining, particularly for single image, has attracted considerable attention to improve image clarity in inclement weather. To overcome the inherent complexity of the single‐image rain removal task, we proposed a novel architecture of the attention mechanism and gated recurrent network (AMGR‐Net) that combines spatial and channel attention mechanisms with gated recurrent units. AMGR‐Net contains multiple modules, each of which uses convolution kernels and attention mechanisms to enhance feature extraction. AMGR‐Net demonstrates superior performance over state‐of‐the‐art methods in both synthetic and real‐world image datasets, as evidenced by higher peak signal to noise ratio and structural similarity index measurement scores. The integration of spatial attention significantly enhances feature expression, enabling more effective rain streak removal and detail preservation. Furthermore, this method also shows promising results in the application of stripe noise removal from meteorological satellite cloud images.

A Novel Approach to Image Classification for Detecting Abnormalities in Neuroimages based on the Structural Similarity Index Measure

A Novel Approach to Image Classification for Detecting Abnormalities in Neuroimages based on the Structural Similarity Index Measure

PSNR and SSIM Performance Analysis of Schur Decomposition for Imperceptible Steganography

Purpose: This research examines how well Schur decomposition-based steganography can hide data in digital images without being noticed, while also keeping the image quality high and keeping the hidden information safe. Methods: The study starts by choosing regular test images (Lena, Plane, Peppers, Cameraman, Baboon) to use for hiding messages in. The Schur decomposition is used to hide information within images in a subtle way. To test how well the new method works, we added Gaussian noise and Salt & Pepper noise after embedding. The quality of the image is determined by looking at the Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. Result: The research shows that Schur decomposition results in very good SSIM values (greater than 0.92) and high PSNR scores (as high as 90.27 dB) for various image sizes (64x64, 128x128, 256x256). This means that the quality of the images is not greatly reduced even after steganography is applied. Novelty: This research introduces a unique use of Schur decomposition for hiding data in images without affecting their quality. The study highlights how this method can securely hide information in digital media, which could be really useful for improving steganography techniques in the future. Future studies should concentrate on making improvements to Schur decomposition-based steganography, especially for bigger images. One possibility is to create adaptive methods that can change how images are hidden based on their content. This could make it harder for others to detect and analyze hidden information in the images.

AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate Cancer Detection.

Apparent Diffusion Coefficient (ADC) maps in prostate MRI can reveal tumor characteristics, but their accuracy can be compromised by artifacts related with patient motion or rectal gas associated distortions. To address these challenges, we propose a novel approach that utilizes a Generative Adversarial Network to synthesize ADC maps from T2-weighted magnetic resonance images (T2W MRI). By leveraging contrastive learning, our model accurately maps axial T2W MRI to ADC maps within the cropped region of the prostate organ boundary, capturing subtle variations and intricate structural details by learning similar and dissimilar pairs from two imaging modalities. We trained our model on a comprehensive dataset of unpaired T2-weighted images and ADC maps from 506 patients. In evaluating our model, named AI-ADC, we compared it against three state-of-the-art methods: CycleGAN, CUT, and StyTr2. Our model demonstrated a higher mean Structural Similarity Index (SSIM) of 0.863 on a test dataset of 3240 2D MRI slices from 195 patients, compared to values of 0.855, 0.797, and 0.824 for CycleGAN, CUT, and StyTr2, respectively. Similarly, our model achieved a significantly lower Fréchet Inception Distance (FID) value of 31.992, compared to values of 43.458, 179.983, and 58.784 for the other three models, indicating its superior performance in generating ADC maps. Furthermore, we evaluated our model on 147 patients from the publicly available ProstateX dataset, where it demonstrated a higher SSIM of 0.647 and a lower FID of 113.876 compared to the other three models. These results highlight the efficacy of our proposed model in generating ADC maps from T2W MRI, showcasing its potential for enhancing clinical diagnostics and radiological workflows.

IHBOT: An Intelligent and Hybrid Model for Investigation and Classification of IoT Botnet

The Internet of Things (IoT) is revolutionizing the technological market with exponential growth year wise. This revolution of IoT applications has also brought hackers and malware to gain remote access to IoT devices. The security of IoT systems has become more critical for consumers and businesses because of their inherent heterogenous design and open interfaces. Since the release of Mirai in 2016, IoT malware has gained an exponential growth rate. As IoT system and their infrastructure have become critical resources that triggers IoT malware injected by various shareholders in different settings. The enormous applications cause flooding of insecure packets and commands that fueled threats for IoT applications. IoT botnet is one of the most critical malwares that keeps evolving with the network traffic and may harm the privacy of IoT devices. In this work, we presented several sets of malware analysis mechanisms to understand the behavior of IoT malware. We devise an intelligent and hybrid model (IHBOT) that integrates the malware analysis and distinct machine learning algorithms for the identification and classification of the different IoT malware family based on network traffic. The clustering mechanism is also integrated with the proposed model for the identification of malware families based on similarity index. We have also applied YARA rules for the mitigation of IoT botnet traffic.

Assessment of Migratory Bird Diversity in Key Wetlands and Forest Reserves of Lakhimpur District, Assam, India

Lakhimpur district of Assam has a rich biodiversity and is home to many birds and has good breeding, resting and feeding grounds for migratory birds as well. However, there is little information available regarding the diversity of migratory birds in the district's various locales. A study was conducted in different locations to document the diversity of migratory birds visiting the district. To better understand the diversity of migrating birds in the district, six locations were selected- Kakoi RF, Ranga RF, Pabha RF, Satajan wetland, Bordoibam-Bilmukh Bird Sanctuary and Ghagor Ghat. Data were gathered through surveys, conducted for 218 days from January 2022 – May 2024. A total of 80 migratory bird species were recorded under 26 families of which Anatidae was most diverse. Out of the 80 species, 56 were winter visitor, 18 were local migrants and 6 were summer visitors. Among the migratory birds, Palla’s Fish Eagle (Haliaeetus leucoryphus) is listed as endangered species in IUCN Red List. Three species viz. Ferruginous duck (Aythya nyroca), Falcated duck (Mareca falcata) and Greater Adjutant (Leptoptilos dubius) are listed as near threatened species in IUCN Red List. Out of all the recorded species, insectivorous species are found to be highest (39%) as compared to species with other feeding habits. The Shannon diversity index was highest for Satajan wetland (H′ = 3.86) and lowest for Bordoibam-Bilmukh BS (H′ = 2.79). Jaccard similarity index showed highest similarity between Ranga RF and Kakoi RF (0.312) and the lowest value between Ranga RF and Bordoibam-Bilmukh BS (0.069). The Satajan wetland, Ranga RF, Kakoi RF, Pabha RF, Bordoibam-Bilmukh BS, and Ghagor Ghat are vital habitats that require conservation to continue supporting diverse resident and migratory birds, as well as other wildlife.

Deep-Learning-Based Real-Time Passive Non-Line-of-Sight Imaging for Room-Scale Scenes.

Non-line-of-sight imaging is a technique for reconstructing scenes behind obstacles. We report a real-time passive non-line-of-sight (NLOS) imaging method for room-scale hidden scenes, which can be applied to smart home security monitoring sensing systems and indoor fast fuzzy navigation and positioning under the premise of protecting privacy. An unseen scene encoding enhancement network (USEEN) for hidden scene reconstruction is proposed, which is a convolutional neural network designed for NLOS imaging. The network is robust to ambient light interference conditions on diffuse reflective surfaces and maintains a fast reconstruction speed of 12.2 milliseconds per estimation. The consistency of the mean square error (MSE) is verified, and the peak signal-to-noise ratio (PSNR) values of 19.21 dB, 15.86 dB, and 13.62 dB are obtained for the training, validation, and test datasets, respectively. The average values of the structural similarity index (SSIM) are 0.83, 0.68, and 0.59, respectively, and are compared and discussed with the corresponding indicators of the other two models. The sensing system built using this method will show application potential in many fields that require accurate and real-time NLOS imaging, especially smart home security systems in room-scale scenes.

Lensless light-field imaging using LMI

Light-field imaging is widely used in many fields, such as computer vision, graphics, and microscopy imaging, to record high-dimensional light information for abundant visual perception. However, light-field imaging systems generally have high system complexity and limited resolution. Over the last decades, lensless imaging systems have attracted tremendous attention to alleviate the restrictions of lens-based architectures. Despite their advantages, lensless light-field imaging introduces significant errors in light-field reconstruction. This paper introduces a novel, to our knowledge, light field moment imaging-based lensless imaging system (LMI-LIS) aiming to improve the quality of light-field reconstruction. The proposed approach first uses light field moment imaging (LMI) with a sinc angular distribution model of the light field to extract the encoded information of the scene for each sub-aperture area. Meanwhile, the corresponding sub-aperture point spread function is segmented from the system point spread function. Finally, sub-aperture images of the scene are reconstructed separately for each sub-aperture area. To evaluate the light-field reconstruction performance, the imaging quality and angular consistency of different lensless light-filed imaging methods are compared through digital refocusing, epipolar plane image, peak signal-to-noise ratio, and structural similarity index. Furthermore, the effectiveness of the proposed methodology is verified using experimental results and theoretical analysis. It is demonstrated that lensless light-field imaging using LMI and the sinc model of the angular distribution achieves high-quality sub-aperture images.

Double Tseng’s Algorithm with Inertial Terms for Inclusion Problems and Applications in Image Deblurring

In this research paper, we present a novel theoretical technique, referred to as the double Tseng’s algorithm with inertial terms, for finding a common solution to two monotone inclusion problems. Developing the double Tseng’s algorithm in this manner not only comprehensively expands theoretical knowledge in this field but also provides advantages in terms of step-size parameters, which are beneficial for tuning applications and positively impact the numerical results. This new technique can be effectively applied to solve the problem of image deblurring and offers numerical advantages compared to some previously related results. By utilizing certain properties of a Lipschitz monotone operator and a maximally monotone operator, along with the identity associated with the convexity of the quadratic norm in the framework of Hilbert spaces, and by imposing some constraints on the scalar control conditions, we can achieve weak convergence to a common zero point of the sum of two monotone operators. To demonstrate the benefits and advantages of this newly proposed algorithm, we performed numerical experiments to measure the improvement in the signal–to–noise ratio (ISNR) and the structural similarity index measure (SSIM). The results of both numerical experiments (ISNR and SSIM) demonstrate that our new algorithm is more efficient and has a significant advantage over the relevant preceding algorithms.

Conditional probabilistic diffusion model driven synthetic radiogenomic applications in breast cancer.

This study addresses the heterogeneity of Breast Cancer (BC) by employing a Conditional Probabilistic Diffusion Model (CPDM) to synthesize Magnetic Resonance Images (MRIs) based on multi-omic data, including gene expression, copy number variation, and DNA methylation. The lack of paired medical images and genomics data in previous studies presented a challenge, which the CPDM aims to overcome. The well-trained CPDM successfully generated synthetic MRIs for 726 TCGA-BRCA patients, who lacked actual MRIs, using their multi-omic profiles. Evaluation metrics such as Frechet's Inception Distance (FID), Mean Square Error (MSE), and Structural Similarity Index Measure (SSIM) demonstrated the CPDM's effectiveness, with an FID of 2.02, an MSE of 0.02, and an SSIM of 0.59 based on the 15-fold cross-validation. The synthetic MRIs were used to predict clinical attributes, achieving an Area Under the Receiver-Operating-Characteristic curve (AUROC) of 0.82 and an Area Under the Precision-Recall Curve (AUPRC) of 0.84 for predicting ER+/HER2+ subtypes. Additionally, the MRIs served to accurately predicted BC patient survival with a Concordance-index (C-index) score of 0.88, outperforming other baseline models. This research demonstrates the potential of CPDMs in generating MRIs based on BC patients' genomic profiles, offering valuable insights for radiogenomic research and advancements in precision medicine. The study provides a novel approach to understanding BC heterogeneity for early detection and personalized treatment.

Investigating the antioxidant properties and GC-MS profile of Indian native medicinal flower Pandanus odorifer and assessing its cytotoxic effects on HT-29 cells

This study aimed to investigate the antioxidant potential and bioactive compounds of Pandanus odorifer flower. The phytochemical investigation includes both qualitative and quantitative experiments. In vitro antioxidant studies were conducted using DPPH, ABTS and hydroxyl methods. The bioactive compounds present in the ethanol extract were identified using GC-MS analysis. The MTT assay was carried out on HT-29 colon cancer cells to investigate the cytotoxic ability of the ethanolic extract. The qualitative phytochemical analysis demonstrated the presence of flavonoids, phenolic compounds, tannins, saponins, steroids, carboxylic acids and cardiac glycosides. The determination of flavonoid showed 400 ± 0.110 mg/g and 250±0.090 mg/g of quercetin equivalent in ethanol and water extracts. Similarly, the determination of phenol exhibited 108±0.210 mg/g and 87±0.173 mg/g gallic acid equivalent in ethanol and water extracts. The total antioxidant capacity revealed 1.6 ± 0.093 mg/g and 1.3± 0.056 mg/g equivalent of ascorbic acid in ethanol and water extracts. The DPPH assay showed IC50 values of 35.06 µg/mL in ethanol and 44.82 µg/mL in water extract. Likewise, the ABTS assay revealed an IC50 value of 42.20 µg/mL and 47.45 µg/mL in ethanol and water extract. The hydroxyl radical assay showed IC50 value of 23.95 µg/mL in ethanol and 36.56 µg/mL in water extract. 22 bioactive compounds were identified using GC-MS analysis based on their similarity index. The ethanolic extract showed greater cytotoxic activity with the IC50 value of 28.08 µg/mL on HT-29 colon cancer cells. This study concludes that the Pandanus odorifer flower extracts exhibited significant antioxidant and cytotoxic property.