Journal articles: 'IMAGE SEGMENTATION TECHNIQUES'

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Haralick, Robert M., and Linda G. Shapiro. "Image segmentation techniques." Computer Vision, Graphics, and Image Processing 29, no. 1 (January 1985): 100–132. http://dx.doi.org/10.1016/s0734-189x(85)90153-7.

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Singh, Inderpal, and Dinesh Kumar. "A Review on Different Image Segmentation Techniques." Indian Journal of Applied Research 4, no. 4 (October 1, 2011): 1–3. http://dx.doi.org/10.15373/2249555x/apr2014/200.

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Tongbram, Simon. "Clustering-based Image Segmentation Techniques: A Review." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 701–7. http://dx.doi.org/10.5373/jardcs/v12sp7/20202160.

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Sharma, Dr Kamlesh, and Nidhi Garg. "An Extensive Review on Image Segmentation Techniques." Indian Journal of Image Processing and Recognition 1, no. 2 (June 10, 2021): 1–5. http://dx.doi.org/10.35940/ijipr.b1002.061221.

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Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

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Sharma, Dr Kamlesh, and Nidhi Garg. "An Extensive Review on Image Segmentation Techniques." Indian Journal of Image Processing and Recognition 1, no. 2 (June 10, 2021): 1–5. http://dx.doi.org/10.54105/ijipr.b1002.061221.

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Image processing is the use of algorithms to perform various operations on digital images. The techniques that are explained further are image segmentation and image enhancement. Image Segmentation is a method to partition an image into multiple segments, to change the presentation of an image into something more meaningful and easier to analyze. The current image segmentation techniques include region-based segmentation and edge detection segmentation. Image Enhancement is the process of improving the quality of image. Under this section there are two broad divisions- Spatial Domain Technique and Frequency Domain Technique.

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Patel, Dr Bharat C., and Dr Jagin M. Patel. "Comparative Study on Text Segmentation Techniques." YMER Digital 21, no. 01 (January 19, 2022): 372–80. http://dx.doi.org/10.37896/ymer21.01/35.

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Text segmentation, whether printed, handwritten or cursive, is one of the most complicated phases in any OCR. The accuracy of recognition will be heavily reliant on good segmentation. Image segmentation is a crucial component of image analysis and the field of computer vision. Researchers have developed several techniques for segmentation, each of which is used for different types of segmented objects. At present no any universal method is available for image segmentation. Existing image segmentation techniques are not capable to deal with images of any types. This survey looked at a variety of image segmentation techniques, evaluated them, and discussed the issues that came up as a result of using them

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Gehlot, Shiv, and John Deva Kumar. "The Image Segmentation Techniques." International Journal of Image, Graphics and Signal Processing 9, no. 2 (February 8, 2017): 9–18. http://dx.doi.org/10.5815/ijigsp.2017.02.02.

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Abdul, Wadood. "Region Based Segmentation Techniques for Digital Images." Journal of Computational and Theoretical Nanoscience 16, no. 9 (September 1, 2019): 3792–801. http://dx.doi.org/10.1166/jctn.2019.8252.

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This paper discusses region based segmentation techniques for digital images. For a few applications, such as image compression or recognition, we cannot handle the entire picture straightforwardly as it is unconventional and inefficient. Due to these reasons, many algorithms related to image segmentation are proposed in the literature to divide an image prior to compression or recognition. The segmentation of an image is basically done to arrange or group the image in a few fragments (districts) as specified by the elements of an image, for instance, according to the value of pixel or frequency response. Currently, many image segmentation approaches exist and are widely used in across scientific disciplines and daily human life. The segmentation approaches can be generally categorized to segmentation based on region, segmentation based on edges, and information grouping.

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Tripathi, Rakesh, and Neelesh Gupta. "A Review on Segmentation Techniques in Large-Scale Remote Sensing Images." SMART MOVES JOURNAL IJOSCIENCE 4, no. 4 (April 20, 2018): 7. http://dx.doi.org/10.24113/ijoscience.v4i4.143.

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Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Various algorithms for automating the segmentation process have been proposed, tested and evaluated to find the most ideal algorithm to be used for different types of images. In this paper a review of basic image segmentation techniques of satellite images is presented.

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Chandrakala, M. "Image Analysis of Sauvola and Niblack Thresholding Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 14, 2021): 2353–57. http://dx.doi.org/10.22214/ijraset.2021.34569.

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Image segmentation is a critical problem in computer vision and other image processing applications. Image segmentation has become quite challenging over the years due to its widespread use in a variety of applications. Image thresholding is a popular image segmentation technique. The segmented image quality is determined by the techniques used to determine the threshold value.A locally adaptive thresholding method based on neighborhood processing is presented in this paper. The performance of locally thresholding methods like Niblack and Sauvola was demonstrated using real-world images, printed text, and handwritten text images. Threshold-based segmentation methods were investigated using misclassification error, MSE and PSNR. Experiments have shown that the Sauvola method outperforms real-world images, printed and handwritten text images in terms of misclassification error, PSNR, and MSE.

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Adedoyin, Ajayi Olayinka, Olamide Timothy Tawose, and Olu Sunday Adetolaju. "Image Segmentation Techniques in Bone Structure Psychiatry." International Journal of Engineering and Computer Science 9, no. 07 (June 30, 2020): 25102–12. http://dx.doi.org/10.18535/ijecs/v9i07.4502.

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Today, a large number of x-ray images are interpreted in hospitals and computer-aided system that can perform some intelligent task and analysis is needed in order to raise the accuracy and bring down the miss rate in hospitals, particularly when it comes to diagnosis of hairline fractures and fissures in bone joints. This research considered some segmentation techniques that have been used in the processing and analysis of medical images and a system design was proposed to efficiently compare these techniques. The designed system was tested successfully on a hand X-ray image which led to the proposal of simple techniques to eliminate intrinsic properties of x-ray imaging systems such as noise. The performance and accuracy of image segmentation techniques in bone structures were compared and these eliminated time wasting on the choice of image segmentation algorithms. Although there are several practical applications of image segmentation such as content-based image retrieval, machine vision, medical imaging, object detection, recognition tasks, etc., this study focuses on the performance comparison of several image segmentation techniques for medical X-ray images.

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Dong, Yu Bing, Hai Yan Wang, and Ming Jing Li. "Comparison of Thresholding and Edge Detection Segmentation Techniques." Advanced Materials Research 860-863 (December 2013): 2783–86. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2783.

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Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

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Priya, Aayushi, and Rajeev Tiwari. "A Review on Segmentation Techniques in Medical Images." SMART MOVES JOURNAL IJOSCIENCE 3, no. 2 (February 11, 2017): 6. http://dx.doi.org/10.24113/ijoscience.v3i2.190.

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Image segmentation is an essential but critical component in low level vision image analysis, pattern recognition, and in robotic systems. It is one of the most difficult and challenging tasks in image processing which determines the quality of the final result of the image analysis. Image segmentation is the process of dividing an image into different regions such that each region is homogeneous. A precise segmentation of medical image is an important stage in contouring throughout radiotherapy preparation. Medical images are mostly used as radiographic techniques in diagnosis, clinical studies and treatment planning. This review paper defines the limitation and strength of each methods currently existing for the segmentation of medical images.

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Abdul Saleem, S., and G. Vinitha. "A Study on Image Segmentation Techniques." Asian Journal of Computer Science and Technology 8, S2 (March 5, 2019): 75–78. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2020.

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Image processing is a technique to transform an image into digital form and implement some operations on it; in order to acquire an improved image or to abstract some useful information from it. It is a kind of signal exemption in which input is image, like video frame or photograph and output may be image or characteristics related with that image. Segmentation partitions an image into separate regions comprising each pixel with similar attributes. To be significant and useful for image analysis and clarification, the regions should powerfully relate to depicted objects or features of interest. Meaningful segmentation is the first step from low-level image processing converting a grey scale or color image into one or more other images to high-level image depiction in terms of objects, features, and scenes. The achievement of image analysis depends on reliability of segmentation, but an exact partitioning of an image is mostly a very challenging problem.

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Abdulateef, Salwa, and Mohanad Salman. "A Comprehensive Review of Image Segmentation Techniques." Iraqi Journal for Electrical and Electronic Engineering 17, no. 2 (September 25, 2021): 166–75. http://dx.doi.org/10.37917/ijeee.17.2.18.

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Image segmentation is a wide research topic; a huge amount of research has been performed in this context. Image segmentation is a crucial procedure for most object detection, image recognition, feature extraction, and classification tasks depend on the quality of the segmentation process. Image segmentation is the dividing of a specific image into a numeral of homogeneous segments; therefore, the representation of an image into simple and easy forms increases the effectiveness of pattern recognition. The effectiveness of approaches varies according to the conditions of objects arrangement, lighting, shadow, and other factors. However, there is no generic approach for successfully segmenting all images, where some approaches have been proven to be more effective than others. The major goal of this study is to provide summarize of the disadvantages and the advantages of each of the reviewed approaches of image segmentation.

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Liu, Hui. "Image Segmentation Techniques for Intelligent Monitoring of Putonghua Examinations." Advances in Mathematical Physics 2022 (April 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/4302666.

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Image recognition and image processing usually contain the technique of image segmentation. Excellent segmentation results can directly affect the accuracy of image recognition and processing. The essence of image segmentation is to segment each frame of a certain image or a video into multiple specific objects or regions and represent them with different labels. This paper focuses on the segmentation results obtained in image segmentation of images used for intelligent monitoring of Mandarin exams are usually visualized for image analysis. In this paper, we first investigate the performance improvement techniques for semantic segmentation in the image segmentation task for intelligent monitoring of Mandarin exams, improve the pixel classification capability by performing semantic migration, and, for the first time, extend the dataset substantially by style transformation to improve the model’s recognition of advanced features. In addition, to further address the shortcomings of the dataset, this paper improves the performance of image segmentation using synthetic datasets by investigating synthetic dataset image segmentation improvement techniques that reduce the reliance on manually annotated datasets. Image segmentation techniques continue to advance, and there are even thousands of commonly used segmentation methods for image segmentation development to date. Among them, they can be broadly classified as region-based segmentation methods, threshold-based segmentation methods, edge-based segmentation methods, specific theory-based segmentation methods, and deep learning-based segmentation methods. However, the methods used in this paper have all been experimentally demonstrated to improve the effectiveness of the techniques and proved to outperform other existing methods in the same field in the publicly available datasets LSUN, Cityscapes, and GTA5 datasets, respectively.

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Sree, S. Jayanthi, and C. Vasanthanayaki. "Ultrasound Fetal Image Segmentation Techniques: A Review." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 1 (December 7, 2018): 52–60. http://dx.doi.org/10.2174/1573405613666170622115527.

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Background: This paper reviews segmentation techniques for 2D ultrasound fetal images. Fetal anatomy measurements derived from the segmentation results are used to monitor the growth of the fetus. </P><P> Discussion: The segmentation of fetal ultrasound images is a difficult task due to inherent artifacts and degradation of image quality with gestational age. There are segmentation techniques for particular biological structures such as head, stomach, and femur. The whole fetal segmentation algorithms are only very few. Conclusion: This paper presents a review of these segmentation techniques and the metrics used to evaluate them are summarized.

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Thirugnanam, Mythili, and S. Margret Anouncia. "Evaluating the performance of various segmentation techniques in industrial radiographs." Cybernetics and Information Technologies 14, no. 1 (March 1, 2014): 161–71. http://dx.doi.org/10.2478/cait-2014-0013.

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Abstract At present, image processing concepts are widely used in different fields, such as remote sensing, communication, medical imaging, forensics and industrial inspection. Image segmentation is one of the key processes in image processing key stages. Segmentation is a process of extracting various features of the image which can be merged or split to build the object of interest, on which image analysis and interpretation can be performed. Many researchers have proposed various segmentation algorithms to extract the region of interest from an image in various domains. Each segmentation algorithm has its own pros and cons based on the nature of the image and its quality. Especially, extracting a region of interest from a gray scale image is incredibly complex compared to colour images. This paper attempts to perform a study of various widely used segmentation techniques in gray scale images, mostly in industrial radiographic images that would help the process of defects detection in non-destructive testing.

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Basir, Otman, and Kalifa Shantta. "Automatic MRI Brain Tumor Segmentation Techniques: A Survey." IRA-International Journal of Applied Sciences (ISSN 2455-4499) 16, no. 2 (April 20, 2021): 25. http://dx.doi.org/10.21013/jas.v16.n2.p2.

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Image segmentation plays a crucial role in recognizing image signification for checking and mining medical image records. Brain tumor segmentation is a complicated assignment in medical image analysis. It is challenging to identify precisely and extract that a portion of the image has abnormal tissues for further diagnosis and analysis. The method of segmenting a tumor from a brain MRI image is a highly concentrated medical science community field, as MRI is non-invasive. In this survey, brain MRI images' latest brain tumor segmentation techniques are addressed a thoroughgoing literature review. Besides, surveys the several approved techniques regularly applied for brain tumor MRI segmentation. Also, highlighting variances among them and reviews their abilities, pros, and weaknesses. Various approaches to image segmentation are described and explicated with the modern participation of several investigators.

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Fan, Bihan, and Ji Zhang. "A Review of Medical Image Segmentation Techniques." Frontiers in Computing and Intelligent Systems 4, no. 3 (July 20, 2023): 89–91. http://dx.doi.org/10.54097/fcis.v4i3.11149.

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Medical image segmentation is a key task in medical imaging processing. It can segment different tissues, organs or lesions in medical images to provide doctors with more accurate diagnosis and treatment suggestions. With the continuous development of computer science and medical technology, medical image segmentation technology has also undergone rapid development. This article will provide an overview of medical image segmentation technology from concepts, commonly used methods, and applications, and introduce some of the latest research results.

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Amal Abbas Kadhim, Wedad Abdul Khuder Naser, and Safana Hyder Abbas. "Subject review: Image segmentation techniques." Global Journal of Engineering and Technology Advances 13, no. 3 (December 30, 2022): 081–85. http://dx.doi.org/10.30574/gjeta.2022.13.3.0207.

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Image segmentation is the most critical functions in image analysis and processing. Image segmentation is a mechanism used to divide an image into multiple segments. It will make image smooth and easy to evaluate. Segmentation is the most essential and crucial process for facilitating the delineation, characterization, and visualization of regions of interest in a particular image. The main goal is to make image more simple and meaningful. This survey present background reviews of various image segmentation techniques, evaluates them and presents the issues related to those techniques.

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Ortiz, A., J. M. Gorriz, J. Ramirez, and D. Salas-Gonzalez. "Unsupervised Neural Techniques Applied to MR Brain Image Segmentation." Advances in Artificial Neural Systems 2012 (June 7, 2012): 1–7. http://dx.doi.org/10.1155/2012/457590.

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The primary goal of brain image segmentation is to partition a given brain image into different regions representing anatomical structures. Magnetic resonance image (MRI) segmentation is especially interesting, since accurate segmentation in white matter, grey matter and cerebrospinal fluid provides a way to identify many brain disorders such as dementia, schizophrenia or Alzheimer’s disease (AD). Then, image segmentation results in a very interesting tool for neuroanatomical analyses. In this paper we show three alternatives to MR brain image segmentation algorithms, with the Self-Organizing Map (SOM) as the core of the algorithms. The procedures devised do not use any a priori knowledge about voxel class assignment, and results in fully-unsupervised methods for MRI segmentation, making it possible to automatically discover different tissue classes. Our algorithm has been tested using the images from the Internet Brain Image Repository (IBSR) outperforming existing methods, providing values for the average overlap metric of 0.7 for the white and grey matter and 0.45 for the cerebrospinal fluid. Furthermore, it also provides good results for high-resolution MR images provided by the Nuclear Medicine Service of the “Virgen de las Nieves” Hospital (Granada, Spain).

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Sharma, Neeraj, AmitK Ray, KK Shukla, Shiru Sharma, Satyajit Pradhan, Arvind Srivastva, and LalitM Aggarwal. "Automated medical image segmentation techniques." Journal of Medical Physics 35, no. 1 (2010): 3. http://dx.doi.org/10.4103/0971-6203.58777.

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Kaur, Maninderjit, and Er Navdeep Singh. "Image Segmentation Techniques: An Overview." IOSR Journal of Computer Engineering 16, no. 4 (2014): 50–58. http://dx.doi.org/10.9790/0661-16435058.

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Saini, Mehak. "INSIGHT OF IMAGE SEGMENTATION TECHNIQUES." International Journal of Advanced Research in Computer Science 8, no. 7 (August 20, 2017): 682–85. http://dx.doi.org/10.26483/ijarcs.v8i7.4374.

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Zaitoun, Nida M., and Musbah J. Aqel. "Survey on Image Segmentation Techniques." Procedia Computer Science 65 (2015): 797–806. http://dx.doi.org/10.1016/j.procs.2015.09.027.

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panchal, Gayatri. "Analysis of Image Segmentation Techniques." International Journal of Recent Advancement in Engineering & Research 3, no. 3 (March 8, 2017): 4. http://dx.doi.org/10.24128/ijraer.2017.mn89cd.

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Jasim, Wala’a, and Rana Mohammed. "A Survey on Segmentation Techniques for Image Processing." Iraqi Journal for Electrical and Electronic Engineering 17, no. 2 (August 16, 2021): 73–93. http://dx.doi.org/10.37917/ijeee.17.2.10.

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The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image segmentation are splitting the images into a few parts on the basis of image’s features including texture, color, pixel intensity value and so on. With regard to the presented study, many approaches of image segmentation are reviewed and discussed. The techniques of segmentation might be categorized into six classes: First, thresholding segmentation techniques such as global thresholding (iterative thresholding, minimum error thresholding, otsu's, optimal thresholding, histogram concave analysis and entropy based thresholding), local thresholding (Sauvola’s approach, T.R Singh’s approach, Niblack’s approaches, Bernsen’s approach Bruckstein’s and Yanowitz method and Local Adaptive Automatic Binarization) and dynamic thresholding. Second, edge-based segmentation techniques such as gray-histogram technique, gradient based approach (laplacian of gaussian, differential coefficient approach, canny approach, prewitt approach, Roberts approach and sobel approach). Thirdly, region based segmentation approaches including Region growing techniques (seeded region growing (SRG), statistical region growing, unseeded region growing (UsRG)), also merging and region splitting approaches. Fourthly, clustering approaches, including soft clustering (fuzzy C-means clustering (FCM)) and hard clustering (K-means clustering). Fifth, deep neural network techniques such as convolution neural network, recurrent neural networks (RNNs), encoder-decoder and Auto encoder models and support vector machine. Finally, hybrid techniques such as evolutionary approaches, fuzzy logic and swarm intelligent (PSO and ABC techniques) and discusses the pros and cons of each method.

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Saeed, Jwan N. "A SURVEY OF ULTRASONOGRAPHY BREAST CANCER IMAGE SEGMENTATION TECHNIQUES." Academic Journal of Nawroz University 9, no. 1 (February 11, 2020): 1. http://dx.doi.org/10.25007/ajnu.v9n1a523.

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The most common cause of death among women globally is breast cancer. One of the key strategies to reduce mortality associated with breast cancer is to develop effective early detection techniques. The segmentation of breast ultrasound (BUS) image in Computer-Aided Diagnosis (CAD) systems is critical and challenging. Image segmentation aims to represent the image in a simplified and more meaningful way while retaining crucial features for easier analysis. However, in the field of image processing, image segmentation is a tough task particularly in ultrasound (US) images due to challenges associated with their nature. This paper presents a survey on several techniques of ultrasonography images segmentation including threshold based, region based, watershed, active contour and learning based techniques, their merits, and demerits. This can provide significant insights for CAD developers or researchers to advance this field.

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R, Asharani, and Naveen Kumar R. "Review on Brain Tumor Image Segmentation in Time-Frequency Domain." Journal of Image Processing and Artificial Intelligence 8, no. 3 (September 20, 2022): 1–6. http://dx.doi.org/10.46610/joipai.2022.v08i03.001.

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The progressive image segmentation is one of the necessary stages in image acquisition and recognition for an effective identification of brain tumor in advanced medical equipment’s, any image segmentation algorithms working effectively in distinguishing impaired and malignant information from tomographic images through various classification techniques. There is an ambiguity in segmentation for effective regeneration of disseminated information during investigation and extraction of features like shape, volume, and motions of organs from medical images is essential. Current research in medical imaging is aimed at developing automated image recognition and diagnostic systems, which require efficient image segmentation and quantification tools. This paper made an effort to realize the Time-frequency method of image segmentation and reviewing the findings of existing Medical segmentation techniques for medical images.

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Mishra, Harshita, and Anuradha Misra. "Techniques for Image Segmentation: A Critical Review." International Journal of Research in Advent Technology 9, no. 3 (April 10, 2021): 1–4. http://dx.doi.org/10.32622/ijrat.93202101.

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In today’s world there is requirement of some techniques or methods that will be helpful for retrieval of the information from the images. Information those are important for finding solution to the problems in the present time are needed. In this review we will study the processing involved in the digitalization of the image. The set or proper array of the pixels that is also called as picture element is known as image. The positioning of these pixels is in matrix which is formed in columns and rows. The image undergoes the process of digitalization by which a digital image is formed. This process of digitalization is called digital image processing of the image (D.I.P). Electronic devices as such computers are used for the processing of the image into digital image. There are various techniques that are used for image segmentation process. In this review we will also try to understand the involvement of data mining for the extraction of the information from the image. The process of the identifying patterns in the large stored data with the help of statistic and mathematical algorithms is data mining. The pixel wise classification of the image segmentation uses data mining technique.

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Bhadoria, Priyanka, Shikha Agrawal, and Rajeev Pandey. "Image Segmentation Techniques for Remote Sensing Satellite Images." IOP Conference Series: Materials Science and Engineering 993 (December 31, 2020): 012050. http://dx.doi.org/10.1088/1757-899x/993/1/012050.

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Jackson, T. R., and M. B. Merickel. "Applications of hierarchical image segmentation techniques: Aorta segmentation." Computerized Medical Imaging and Graphics 16, no. 5 (September 1992): 333–43. http://dx.doi.org/10.1016/0895-6111(92)90146-z.

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Fan, Yuan Yuan, Wei Jiang Li, and Feng Wang. "A Survey on Solar Image Segmentation Techniques." Advanced Materials Research 945-949 (June 2014): 1899–902. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1899.

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Image segmentation is one of the basic problems of image processing, also is the first essential and fundamental issue in the solar image analysis and pattern recognition. This paper summarizes systematically on the image segmentation techniques in the solar image retrieval and the recent applications of image segmentation. Then the merits and demerits of each method are discussed in this paper, in this way we can combine some methods for image segmentation to reach the better effects in astronomy. Finally, according to the characteristics of the solar image itself, the more appropriate image segmentation methods are summed up, and some remarks on the prospects and development of image segmentation are presented.

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Komal, G. K. Sethi, and R. K. Bawa. "A Hybrid Approach of Preprocessing and Segmentation Techniques in Automatic Rice Variety Identification System." Journal of Scientific Research 14, no. 1 (January 1, 2022): 205–13. http://dx.doi.org/10.3329/jsr.v14i1.54811.

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Image processing techniques play an important role with various images such as rice grain identification, wheat, fruits, medical, vehicle, and digital text images in image acquisition, image preprocessing, clustering, segmentation, and classification techniques. In the application of object detection and classification images, preprocessing and segmentation techniques are used. This paper delves into the specifics of automated segmentation processes, focusing on rice variety identification and classification images. The aim is to talk about the issues that come up when segmenting digital images and the relative merits and drawbacks of the various methods for preprocessing and segmenting images that are currently available. In this paper, we propose a hybrid approach of Preprocessing and Segmentation techniques to develop an automatic rice variety identification system.

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Manoharan, Dr Samuel. "Performance Analysis of Clustering Based Image Segmentation Techniques." Journal of Innovative Image Processing 2, no. 1 (March 11, 2020): 14–24. http://dx.doi.org/10.36548/jiip.2020.1.002.

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As the images are examined using the latest machine learning process, the techniques for computing the images become highly essential. This computation applied over the images allows one to have an assessable information’s or values from the images. Since segmentation plays a vital role in processing of images by enhancing or hypothetically altering the images making the examination of valuable insights easier. Several procedures and the methods for segmenting the images have been developed. However to have an better process it is important to sort out an effective segmentation procedure, so the paper performs the analysis of the clustering based image segmentation techniques applied on the magnetic resonance image of the human brain to detect the white matter hyper intensities part. The evaluation process take place in the MATLAB to evince the accurate valuation procedure. The optimal procedure is sorted out to be used in observing and examining the medical images by implementing over a computer assisted tool.

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Shirly, S., and K. Ramesh. "Review on 2D and 3D MRI Image Segmentation Techniques." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 2 (January 10, 2019): 150–60. http://dx.doi.org/10.2174/1573405613666171123160609.

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Background: Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. </P><P> Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation. Conclusion: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

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Bhosle, Sanket, Dnyaneshwar Kanade, Shourya S. Bhosale, Vaibhavi R. Bhosale, Rutuja S. Bhople, and Sakshi B. Bhegade. "Comparative Analysis Between Different Lung Segmentation Techniques." ITM Web of Conferences 56 (2023): 04003. http://dx.doi.org/10.1051/itmconf/20235604003.

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For the purpose of identifying various lung illnesses, computed tomography (CT) pictures of the lung must be segmented. The most significant aspect of medical imaging is image segmentation. Via an automated process, the ROI (region of interest) is extracted. The process of segmentation separates an image into sections according to a particular interest, such as segmenting human organs or tissue. Several medical disorders can benefit from the segmented image of the lung. We specifically compared and analysed various threshold segmentation algorithms in this paper in an effort to determine which one would be the best to use moving forward with image processing. We have used Computed Tomography (CT) images of Lungs with Tuberculosis (TB) dataset from Kaggle for image processing and compared them with finely masked CT images to infer the best Threshold algorithm. We have decided to do the analysis on Threshold algorithms named as Binary Threshold, Otsu’s Threshold, and Adaptive Threshold. Comparison has been done based on performance parameters such as Accuracy, Precision, Recall Value, f1-score, etc. The results are also represented in Graphical format for better understanding of performed comparison study.

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Qasim, Iqraa. "A Review on Image Segmentation Techniques and Its Recent Applications." International Journal of Computer Science and Mobile Computing 10, no. 9 (September 30, 2021): 98–106. http://dx.doi.org/10.47760/ijcsmc.2021.v10i09.010.

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The technique of attaching a name to each component in a picture so that elements with much the same label have certain visual qualities (pixels, colours, values, patterns, and other features of the image) is known as image segmentation. The output of image segmentation is a series of fragments or patterns taken from the image that collectively cover the full picture. After segmenting an image, classification detects classes in an image on the basic of similarities and differences between detected segments. These two tools together are doing wonders in better object detection in images. Big world has big data, and this big data need robust algorithms for understanding images without a human. This paper summarises the techniques of image segmentation and classification based on their efficiency. Finally, a comparison table shows who performed it better.

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Cruz-Aceves, I., J. G. Avina-Cervantes, J. M. Lopez-Hernandez, M. G. Garcia-Hernandez, M. Torres-Cisneros, H. J. Estrada-Garcia, and A. Hernandez-Aguirre. "Automatic Image Segmentation Using Active Contours with Univariate Marginal Distribution." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/419018.

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This paper presents a novel automatic image segmentation method based on the theory of active contour models and estimation of distribution algorithms. The proposed method uses the univariate marginal distribution model to infer statistical dependencies between the control points on different active contours. These contours have been generated through an alignment process of reference shape priors, in order to increase the exploration and exploitation capabilities regarding different interactive segmentation techniques. This proposed method is applied in the segmentation of the hollow core in microscopic images of photonic crystal fibers and it is also used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, respectively. Moreover, to evaluate the performance of the medical image segmentations compared to regions outlined by experts, a set of similarity measures has been adopted. The experimental results suggest that the proposed image segmentation method outperforms the traditional active contour model and the interactive Tseng method in terms of segmentation accuracy and stability.

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Gill, Jasmeen, Akshay Girdhar, and Tejwant Singh. "A Review of Enhancement and Segmentation Techniques for Digital Images." International Journal of Image and Graphics 19, no. 03 (July 2019): 1950013. http://dx.doi.org/10.1142/s021946781950013x.

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Image enhancement and segmentation are the two imperative steps while processing digital images. The goal of enhancement is to improve the quality of images so as to nullify the effect of poor illumination conditions during image acquisition. Afterwards, segmentation is performed to extract region of interest (ROI) from the background details of the image. There is a vast literature available for both the techniques. Therefore, this paper is intended to summarize the basic as well as advanced enhancement and segmentation techniques under a single heading; to provide an insight for future researches in the field of pattern recognition.

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Wu, Yin Chao, Seong Jin Noh, and Suyun Ham. "Identification of Inundation Using Low-Resolution Images from Traffic-Monitoring Cameras: Bayes Shrink and Bayesian Segmentation." Water 12, no. 6 (June 17, 2020): 1725. http://dx.doi.org/10.3390/w12061725.

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This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. For the comparative assessment of de-noising techniques, the Bayes shrink and three conventional methods are compared. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. For the comparative assessment of image segmentation techniques, k-means segmentation, Otsu segmentation, and Bayesian segmentation are compared. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. By using the principle of the image processing presented in this paper, we can estimate the inundation from images and assess flooding risks in the vicinity of local flooding locations. Such information will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.

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Yu, Ying, Chunping Wang, Qiang Fu, Renke Kou, Fuyu Huang, Boxiong Yang, Tingting Yang, and Mingliang Gao. "Techniques and Challenges of Image Segmentation: A Review." Electronics 12, no. 5 (March 2, 2023): 1199. http://dx.doi.org/10.3390/electronics12051199.

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Image segmentation, which has become a research hotspot in the field of image processing and computer vision, refers to the process of dividing an image into meaningful and non-overlapping regions, and it is an essential step in natural scene understanding. Despite decades of effort and many achievements, there are still challenges in feature extraction and model design. In this paper, we review the advancement in image segmentation methods systematically. According to the segmentation principles and image data characteristics, three important stages of image segmentation are mainly reviewed, which are classic segmentation, collaborative segmentation, and semantic segmentation based on deep learning. We elaborate on the main algorithms and key techniques in each stage, compare, and summarize the advantages and defects of different segmentation models, and discuss their applicability. Finally, we analyze the main challenges and development trends of image segmentation techniques.

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Kaur, Amanpreet. "Colour Image Segmentation using Background Subtraction with Global and Local Threshold." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1652–57. http://dx.doi.org/10.22214/ijraset.2021.35340.

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Image segmentation is one of the fundamental and essential steps in all the major applications of digital image processing. In this process the digital image is divided into various regions which are also known as segments. These segmented parts of the digital image could be used for further processing like detection of types of objects present in the segmented region, various tumors present in the digital images or the scene understanding process. Usually segmentation is classified as local segmentation and the global segmentation. Image segmentation is also classified on the basis of digital image properties also. In this case it is of two types. First one is non continuity detection and second one is the continuous detection. Various image segmentation techniques are proposed by researchers which have various limitations. Some techniques do not split the region uniformly and other techniques take enough time and memory for the processing of digital image. In this research work both the local and global thresholding concept is used to get the segmented regions of the image. The proposed technique will be able to extract the segmented objects from the digital image. To check the authenticity and efficiency of the proposed technique, it will be compared with other well known techniques of image segmentation using background subtraction of colored digital images. Time of computation, sensitivity and accuracy are used as objective parameters for the performance evaluation of the techniques. For the subjective evaluation visual quality of the digital image is used for performance evaluation.

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Moncho-Santonja, María, Silvia Aparisi-Navarro, Beatriz Defez, and Guillermo Peris-Fajarnés. "Segmentation of Acne Vulgaris Images Techniques: A Comparative and Technical Study." Applied Sciences 13, no. 10 (May 17, 2023): 6157. http://dx.doi.org/10.3390/app13106157.

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Background: Acne vulgaris is the most common dermatological pathology worldwide. The currently used methodologies for the evaluation and monitoring of acne have been analyzed in several studies, highlighting important limitations that can be concretely addressed using image processing methods by performing segmentation on different acne vulgaris image modalities. These techniques reduce the costs of treatment and acne severity grading, since they improve objectivity and are less time-consuming. That is why, in the last decade, several studies that propose segmentation methodologies on acne patients’ images have been published. The aim of this work is to analyze the segmentation methods developed for acne vulgaris images until now, including an analysis of the processing techniques and image modalities used, as well as the results. Results: Following the PRISMA statement and PICO model, 27 studies were included in the systematic review, and subsequently, they were divided into two groups: those that discuss methods based on classical image processing techniques, such as contrast adjustment and conversion of RGB images to other color spaces, and those discussing methods based on machine learning algorithms. Conclusions: Currently, there is no preference between one group of segmentation methods or the other. Moreover, the lack of uniformity in the evaluation of results for each study makes the comparison of methods difficult. The preferred image modality for segmentation is conventional photography, which shows a research gap in the application of segmentation algorithms to other acne vulgaris image modalities that could be useful, such as fluorescence imaging.

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Phanindra Kumar N.S.R. and Prasad Reddy P.V.G.D. "Evolutionary Image Thresholding for Image Segmentation." International Journal of Computer Vision and Image Processing 9, no. 1 (January 2019): 17–34. http://dx.doi.org/10.4018/ijcvip.2019010102.

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Image segmentation is a method of segregating the image into required segments/regions. Image thresholding being a simple and effective technique, mostly used for image segmentation, these thresholds are optimized by optimization techniques by maximizing the Tsallis entropy. However, as the two level thresholding extends to multi-level thresholding, the computational complexity of the algorithm is further increased. So there is need of evolutionary and swarm optimization techniques. In this article, first time optimal thresholds are obtained by maximizing the Tsallis entropy by using novel hybrid bacteria foraging optimization technique and particle swam optimization (hBFOA-PSO). The proposed hBFOA-PSO algorithm performance in segmenting the image is tested using natural and standard images. Experiments show that the proposed hBFOA-PSO is better than particle swarm optimization (PSO), the cuckoo search (CS) and the adaptive Cuckoo Search (ACS).

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N, Nazeeya Anjum. "A Study on Segmenting Brain Tumor MRI Images." Journal of Computational Science and Intelligent Technologies 2, no. 1 (April 16, 2021): 1–6. http://dx.doi.org/10.53409/mnaa/jcsit/2101.

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Segmentation of image has traditionally been referred to as the initial stage in image processing. A successful segmentation output will make image processing analysis considerably further easier. There are several image segmentation techniques and methodologies available. Clustering is the most widely used segmentation algorithm for image processing. Segmentation of tumor using magnetic resonance imaging (MRI) data is a critical procedure yet time-consuming process manually carried out by medical specialists. Due to the considerable difference in the tumor tissue appearances across patients, as well as their occasionally similar resemblance to normal tissues, automating this procedure is difficult. MRI is a sort of sophisticated medical imaging that offers precise information on the human soft tissues. To identify and segment the brain tumor using MRI images, several brain tumor segmentation and detection approaches are analyzed. The benefits and drawbacks of these approaches for brain tumor identification and segmentation are analyzed, with an emphasis on illuminating the benefits and limitations of these techniques for brain tumor segmentation and detection. The MRI image usage in segmentation and detection on various processes is also covered. This analysis provides an overview of several segmentation methods for identifying brain tumors from MRI images of the brain, as well as the usage of various Clustering Techniques.

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B., Rupali. "Color Image Enhancement with Different Image Segmentation Techniques." International Journal of Computer Applications 178, no. 8 (May 15, 2019): 36–40. http://dx.doi.org/10.5120/ijca2019918790.

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bai, M. Praveena Kiruba. "ANALYSIS OF IMAGE SEGMENTATION TECHNIQUES IN IMAGE PROCESSING." International Journal of Advanced Research in Computer Science 9, no. 3 (June 20, 2018): 233–39. http://dx.doi.org/10.26483/ijarcs.v9i3.6089.

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Cruz-Aceves, I., J. G. Avina-Cervantes, J. M. Lopez-Hernandez, M. G. Garcia-Hernandez, and M. A. Ibarra-Manzano. "Unsupervised Cardiac Image Segmentation via Multiswarm Active Contours with a Shape Prior." Computational and Mathematical Methods in Medicine 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/909625.

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This paper presents a new unsupervised image segmentation method based on particle swarm optimization and scaled active contours with shape prior. The proposed method uses particle swarm optimization over a polar coordinate system to perform the segmentation task, increasing the searching capability on medical images with respect to different interactive segmentation techniques. This method is used to segment the human heart and ventricular areas from datasets of computed tomography and magnetic resonance images, where the shape prior is acquired by cardiologists, and it is utilized as the initial active contour. Moreover, to assess the performance of the cardiac medical image segmentations obtained by the proposed method and by the interactive techniques regarding the regions delineated by experts, a set of validation metrics has been adopted. The experimental results are promising and suggest that the proposed method is capable of segmenting human heart and ventricular areas accurately, which can significantly help cardiologists in clinical decision support.

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Journal articles on the topic 'IMAGE SEGMENTATION TECHNIQUES'

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