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Relevant bibliographies by topics / Morphological Operations MRI

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Academic literature on the topic 'Morphological Operations MRI'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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Journal articles on the topic "Morphological Operations MRI"

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Dundar, Mehmet Sait, Kazim Gumus, Semra Icer, and Saliha Ciraci. "Computerized intracranial tumor detection using morphological operations on MRI." Journal of Biotechnology 231 (August 2016): S84. http://dx.doi.org/10.1016/j.jbiotec.2016.05.300.

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Dr., Raman Chadha*, Thakur Shivani, and Kaur Jasmeet. "BRAIN TUMOR DETECTION IN MRI USING MORPHOLOGICAL OPERATIONS BASED IMAGE SEGMENTATION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 4 (2016): 280–85. https://doi.org/10.5281/zenodo.49193.

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Brain is the most important part in the human body.  It is also the most significant element of CNS (Central Nervous System). The brain tumor is the abnormal growth which is caused by cells that grows in uncontrolled manner inside the skull. There are many factors that determine how the brain tumor effects the normal functioning of human body. Among these factors, the location of tumor is one of the most important factors. Thus, the detection of tumor is main objective of the system. It plays a vital role in biomedical system. For the detection of tumor, image processing particularly image segmentation is used. CT (Computed Tomography) scan and MRI (Magnetic Resonance Imaging) are two methods used for imaging of brain tumor. In this paper, MRI brain image is used for the process of tumor detection. This system involves test the brain image process, image filtering, skull stripping, segmentation, morphological operations, and calculation of tumor area and detection of tumor location. The detailed procedures are implemented using MATLAB. MRI gives exact location of tumor and it proves very helpful for doctors for further diagnosis and surgery.  

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Parvathi, K. Sailakshmi, and B. Bindhu Bhavani. "Extraction of Tumor and Cancer Cells of Brain MRI Images by using different Morphological Operations." International Journal of Scientific Research 2, no. 6 (2012): 215–17. http://dx.doi.org/10.15373/22778179/june2013/68.

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Höhne, Karl Heinz, and William A. Hanson. "Interactive 3D Segmentation of MRI and CT Volumes using Morphological Operations." Journal of Computer Assisted Tomography 16, no. 2 (1992): 285–94. http://dx.doi.org/10.1097/00004728-199203000-00019.

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Kulshreshtha, Akanksha, and Arpita Nagpal. "ANALYSIS OF MORPHOLOGICAL OPERATIONS ON IMAGE SEGMENTATION TECHNIQUES." ICTACT Journal on Image and Video Processing 12, no. 1 (2021): 2555–58. http://dx.doi.org/10.21917/ijivp.2021.0362.

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Image segmentation is a process of partitioning an image into different subregions based on edge detection, area based or clustering based methods. Segmentation of brain MRI images is a challenging task. This paper provides a thorough analysis of different segmentation techniques with morphological operators for brain tumor detection. After segmenting the image, morphological operators are used to eliminate and add some pixels from tumor boundaries and to improve the performance of segmentation algorithm. Manual segmentation is used to construct the gold standard for comparing the segmented image. Comparison is performed using performance parameters such as dice, Jaccard coefficient, selectivity, recall and precision. The experimental results show that precision can be improved up to 85% in clustering-based segmentation and full selectivity can be achieved by combining segmentation techniques with morphological operation of erosion. The other performance parameters have also improved by applying erosion than dilation.

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Zhang, Chong, Xuanjing Shen, Hang Cheng, and Qingji Qian. "Brain Tumor Segmentation Based on Hybrid Clustering and Morphological Operations." International Journal of Biomedical Imaging 2019 (April 9, 2019): 1–11. http://dx.doi.org/10.1155/2019/7305832.

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Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method’s sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm’s stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall.

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Faisal Hafeez, Zobia Suhail, and Reyer Zwiggelaar. "Morphological and Marker-based Watershed Method for Detection and Segmentation of Brain Tumor Regions." NUST Journal of Engineering Sciences 16, no. 2 (2023): 108–13. http://dx.doi.org/10.24949/njes.v16i2.759.

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Brain tumor detection is a complex problem in medical image analysis. Brain tumor is an abnormal growth of brain cellsthat is usually detected by Magnetic Resonance Images (MRI). In this paper, we propose an efficient algorithm for detecting brain tumors using MRI without skull removal. After applying basic image preprocessing techniques, morphological operations are used to detect the boundaries and sharpen the regions.Segmentation is performed using Otsu thresholding and then a marker watershed technique is used for final brain tumor segmentation. The proposed approach is evaluated on 3000 images from Brain Tumor Image Segmentation Challenge (BRATS) dataset. Experiments showed that with appropriate preprocessing and appropriate thresholding, good segmentation results can be achieved.

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Mustafa, Zuhaer Nayef AL-Dabagh. "Automated tumor segmentation in MR brain image using fuzzy c-means clustering and seeded region methodology." International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (2021): 284–90. https://doi.org/10.11591/ijai.v10.i2.pp284-290.

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Automated segmentation of a tumor is still a considerably exciting research topic in the medical imaging processing field, and it plays a considerable role in forming a right diagnosis, to aid effective medical treatment. In this work, a fully automated system for segmentation of the brain tumor in MRI images is introduced. The suggested system consists of three parts: Initially, the image is pre-processed to enhance contrast, eliminate noise, and strip the skull from the image using filtering and morphological operations. Secondly, segmentation of the image happens using two techniques, fuzzy c-means clustering (FCM) and with the application of a seeded region growing algorithm (SGR). Thirdly, this method proposes a post-processing step to smooth segmentation region edges using morphological operations. The testing of the proposed system involved 233 patients, which included 287 MRI images. A comparison of the results ensued, with the manual verification of the traces performed by doctors, which ultimately proved an average Dice Coefficient of 90.13% and an average Jaccard Coefficient of 82.60% also, by comparison with traditional segmentation techniques such as FCM method. The segmentation results and quantitative data analysis demonstrates the effectiveness of the suggested system.

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Satish, Chandra. B., K. Satyavathi Smt, and Krishnanaik Vankdoth Dr. "Implementation of Brain Tumor Extraction Application from MRI Image." International Journal of Trend in Scientific Research and Development 2, no. 4 (2018): 2645–53. https://doi.org/10.31142/ijtsrd15701.

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Medical image process is that the most difficult and rising field currently now a day. Process of MRI pictures is one amongst the part of this field. This paper describes the projected strategy to find and extraction of tumour from patient's MRI scan pictures of the brain. This technique incorporates with some noise removal functions, segmentation and morphological operations that area unit the fundamental ideas of image process. Detection and extraction of tumor from MRI scan pictures of the brain is finished by victimization MATLAB software package Satish Chandra. B | Smt K. Satyavathi | Dr. Krishnanaik Vankdoth "Implementation of Brain Tumor Extraction Application from MRI Image" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd15701.pdf

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Srivaishnavi, K. R., T. Pramananda Perumal, and P. Anishiya. "Brain Tumor Prediction and Segmentation with Morphological Region-based Active Contour Model and Refinement using Boltzmann Monte Carlo Method in MRI Images." Indian Journal Of Science And Technology 17, no. 20 (2024): 2088–100. http://dx.doi.org/10.17485/ijst/v17i20.1231.

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Objectives: The primary goal of the research work is to accurately detect the precise location of the brain tumor in the radiological Magnetic Resonance Imaging (MRI) images of human brain using segmentation method. Methods: In this research work, we introduce mainly the Morphological Region-based Active Contour model and Boltzmann Monte Carlo method (MACB model), involving a comprehensive three-step methodology for the segmentation of the brain, MRI images in order to detect brain tumor. The initial step involves pre-processing which includes Gaussian filtering for noise reduction and Contrast Limited Adaptive Histogram Equalization (CLAHE) technique to enhance image features. In the second step, we identify tumor-related clusters using morphological operations and delineate the tumor regions using Active Contour (Snake) model to get a segmented image. In the final step, the Boltzmann Monte Carlo method is used to refine the edges of the segmented image. To evaluate the effectiveness of this approach, the 2D brain tumor datasets, available in the public domain, are used. The first dataset is taken from Kaggle website and has 3064 MRI human brain images and its respective ground truth images which is used for segmentation. The second dataset is used for visualization of segmented tumor, available in the same Kaggle website. Findings: The Performance metrics for finding similarity between the segmented images generated using the proposed MACB model and the ground truth images, available in the first dataset, exhibit higher values. That is, the proposed method has achieved higher values of Dice Similarity Coefficient (DSC): 93.26%, Jaccard Co-efficient: 86.44%, Sensitivity: 97.27%, Specificity: 99.43% and Pixel accuracy: 98.95%. Novelty: In this research work, MACB model is proposed for the detection, segmentation, and refinement process of brain tumor by incorporating Boltzmann Monte Carlo method with Morphological Region-Based Active Contour model. This novel approach has resulted in enhanced precision and efficiency in the brain tumor segmentation process. Keywords: Brain Tumor Segmentation, Morphological Operation, Active Contour, Boltzmann Monte Carlo Method, Magnetic Resonance Imaging

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Book chapters on the topic "Morphological Operations MRI"

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Gezimati, Mavis, Munyaradzi C. Rushambwa, and J. B. Jeeva. "Brain Tumor Detection and Classification of MRI Brain Images Using Morphological Operations." In ICTMI 2017. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1477-3_11.

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Hong Nguyen, Giang, Yen Thi Hoang Hua, and Liet Van Dang. "MRI Brain Tumor Segmentation Using Bidimensional Empirical Mode Decomposition and Morphological Operations." In Intelligent Systems and Networks. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4725-6_1.

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Somasundaram, K., and K. Ezhilarasan. "A Fully Automatic Scheme for Skull Stripping from MRI of Head Scans Using Morphological Neck Breaking Operations." In Lecture Notes in Electrical Engineering. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1000-9_25.

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Halder, Amiya, and Apurba Sarkar. "Automatic Detection of Tumor Cell in Brain MRI Using Rough-Fuzzy Feature Selection with Support Vector Machine and Morphological Operation." In Intelligent Systems Reference Library. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71975-3_8.

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Kumar PK, Mithun, and Mohammad Motiur Rahman. "Calcifications Attenuation in Left Coronary Artery CT Images Using FDA Domain." In Medical Imaging. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0571-6.ch051.

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The calcification plaque is a one kind of artifacts or noises, which is occurred in the Computed Tomography (CT) images as a very high attenuation coefficient. Computed Tomography (CT) images are more helpful than other modalities (e.g. Ultrasonic Imaging, Magnetic Resonance Imaging (MRI) etc.) for disease diagnosis but unfortunately, CT image is an affected sometime by calcification plaque. Medical image segmentation cannot be optimum because of having calcification in the CT images, which is absolutely unexpected. The calcification plaque is the major problem for optimal organ segmentation and detection. This proposed task is a subjective as well as an effective for calcification alleviation from CT images. In this paper, Firstly, we applied the Fisher's Discriminant Analysis (FDA) for optimal threshold value estimation. Secondly, the proposed optimal threshold value is used for the optimal threshold image extraction. After this, the morphological operation is used for heavy calcification erosion and the XOR operation is used for adjusting the optimal threshold image with the input image. Finally, we implemented the Extra-Energy Reduction (EER) Function to smooth the desired image. Therefore, our investigated method is the most significant and articulate in order to attenuate calcification plaque from CT images.

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Das, Poulomi, Rahul Rajak, and Arpita Das. "Application of AI for Computer-Aided Diagnosis System to Detect Brain Tumors." In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2742-9.ch010.

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Early detection and proper treatment of brain tumors are imperative to prevent permanent damage to the brain even patient death. The present study proposed an AI-based computer-aided diagnosis (CAD) system that refers to the process of automated contrast enhancement followed by identifying the region of interest (ROI) and then classify ROI into benign/malignant classes using significant morphological feature selection. This tool automates the detection procedure and also reduces the manual efforts required in widespread screening of brain MRI. Simple power law transformation technique based on different performance metrics is used to automate the contrast enhancement procedure. Finally, benignancy/malignancy of brain tumor is examined by neural network classifier and its performance is assessed by well-known receiver operating characteristic method. The result of the proposed method is enterprising with very low computational time and accuracy of 87.8%. Hence, the proposed method of CAD procedure may encourage the medical practitioners to get alternative opinion.

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Conference papers on the topic "Morphological Operations MRI"

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Chakrapani, D. G., and Ralph A. Hudson. "Environmentally-Assisted Cracking in Welded Deaerator Vessels in the Pulp and Paper Industry." In CORROSION 1986. NACE International, 1986. https://doi.org/10.5006/c1986-86138.

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Abstract The questionable integrity of operating deaerator vessels in the pulp and paper industry were brought to the attention of mill owners and insurance companies by a statement issued in September 1983 by the Technical Association of Pulp and Paper Industry (TAPPI): "Three deaerator storage vessels have ruptured, one of which resulted in fatalities and considerable plant downtime." As recommended by TAPPI, industry-wide inspections of deaerator heater and storage vessels revealed that the problem was widespread. MEI-Charlton, Inc., nondestructive testing engineers and metallurgical consultants, has performed fitness-for-purpose evaluations of over 25 deaerators at pulp and paper mills across the United States. Over 50 percent of the vessels inspected had cracks. Wet fluorescent magnetic particle inspection (WFMPI) following specific surface preparation procedures, was effective in crack detection. The cracks were primarily transverse to the weld and propagated across the heat-affected zone into the base metal. In some cases, the cracks had propagated through the wall thickness. MEI-C's role included inspection of vessels, recommendations for repair welding procedures and supervision of crack removal and repair welding per American Society of Mechanical Engineers Boiler and Pressure Vessel Code. The deaerators were fabricated of SA-285, Grades A and C, SA-455 Grades A and B, and SA-515, 5l6, Grade 70 steels. No correlation between the steel, fabrication and crack susceptibility was found. Metallographic and fractographic examinations of coupons indicated that the cracking was environmentally-assisted, bearing morphological resemblances to stress-corrosion cracking and corrosion-fatigue. Certain operational parameters, such as large fluctuations in feedwater temperature and variations in load cycles, influenced the cracking.

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Tan, Yanli, Li Liu, Qingqing Liu, Jian Wang, Xueyun Ma, and Hairi Ni. "Automatic breast DCE-MRI segmentation using compound morphological operations." In 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2011. http://dx.doi.org/10.1109/bmei.2011.6098307.

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Joshi, Nikita, and Sarika Jain. "A Robust Approach for Application of Morphological Operations on MRI." In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2020. http://dx.doi.org/10.1109/icrito48877.2020.9198011.

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Bethel, G. N. Beena, T. V. Rajinikanth, and S. Viswanadha Raju. "An Improved Analysis of Heart MRI Images using the Morphological Operations." In 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC). IEEE, 2017. http://dx.doi.org/10.1109/ctceec.2017.8455050.

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Fallahi, Alireza, Mohammad Pooyan, Hassan Khotanlou, Hassan Hashemi, Kavous Firouznia, and Mohammad Ali Oghabian. "Uterine fibroid segmentation on multiplan MRI using FCM, MPFCM and morphological operations." In 2010 2nd International Conference on Computer Engineering and Technology. IEEE, 2010. http://dx.doi.org/10.1109/iccet.2010.5485920.

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Zulkoffli, Zuliani, and Talha Afzal Shariff. "Detection of Brain Tumor and Extraction of Features in MRI Images Using K-means Clustering and Morphological Operations." In 2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). IEEE, 2019. http://dx.doi.org/10.1109/i2cacis.2019.8825094.

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Somasundaram, K., and K. Ezhilarasan. "Brain portion peeling from T2 axial MRI head scans using clustering and morphological operation." In 2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET). IEEE, 2012. http://dx.doi.org/10.1109/incoset.2012.6513898.

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Halder, Amiya, Anuva Pradhan, Sourjya Kumar Dutta, and Pritam Bhattacharya. "Tumor extraction from MRI images using dynamic genetic algorithm based image segmentation and morphological operation." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754489.

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Alsafy, Mahmoud, Khalid Alzebdeh, and Nasr Al-Hinai. "Characterization and Performance Evaluation of New Nanocomposite Materials Derived From Recycled Polypropylene and Nano Fillers." In ASME 2024 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/imece2024-144433.

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Abstract Recently, a growing interest in investigating alternatives to synthetic fibers has been increased due to environmental concerns such as increased carbon footprints and waste management. Thus, various industries’ principal focus has been on developing Nano-sized filler from biomass material to manufacture products for an abundance application. Furthermore, to develop green composites in the industrial sector, extraction and Nano-sized fibers have proven desirable. From a commercial and environmental standpoint, chemical fragmentation and shrinking of waste lignocellulosic fibers to small particles is a potential alternative. The aim of this research is to assess the mechanical and physical properties of two newly formulated nanocomposite materials. These composites are produced by blending recycled polypropylene (rPP), serving as the primary matrix, with either Nano date palm particles (NDPP) or Nano calcium dioxide particles (NCaP) at varying contents to act as reinforcing fillers. The process involves pulverizing date palm microfibers derived from agricultural waste into nano-sized lignocellulose fillers using a ball milling machine operating at an efficient speed of 12 cycles per hour. Subsequently, the two types of Nano fillers are mixed with rPP using a double screw extruder machine to fabricate the nanocomposites — Nano Date Palm Composite (NDPC) and Nano Calcium Dioxide Composite (NCaC). Compression molding is employed to produce sheets, and test specimens are prepared in compliance with ASTM standards for evaluation using a Universal Testing Machine (UTM). Results indicate that the highest tensile strength among the reinforced polymers was attained with 3% wt. NDPC and 2% wt. NCaC, exhibiting enhancements of 48% and 61%, respectively, compared to neat rPP. Moreover, flexural strength increased by 30% with 3% NDPC and by 38% with 2% NCaC over the neat matrix. While both nanoparticles resulted in a slight decrease in Young’s modulus — 15.6% with 3% NDPC and 2% NCaC — their inclusion enhanced elongation at break and flexural modulus, thereby augmenting the ductility of the neat polymer. Morphological analysis via Scanning Electron Microscopy (SEM) is important to illustrate the crosslinking between the matrix and the nanoparticles. It revealed that 2% NCaC exhibited superior filler dispersion and greater physical interlocking with the polymeric matrix compared to 3% NDPC. In addition, physical properties illustrate the importance of the material density and melting flow rate to the manufacturer as indication of productivity of the composites. Therefore, NDPC displayed a lower density than NCaC, yet exhibited higher viscosity as indicated by Melt Flow Index (MFI) analysis. This is due to the density of the NDPP is higher than the NCaP. Consequently, NDPC, serving as a Nano bio-composite, offers economically viable and sustainable production prospects, showing promise for diverse industrial applications.

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