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21 results for “K-Means Segmentation”
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Research ArticleOpen accessPhrase in the titleSmart Harvesting Decision System for Date Fruit Based on Fruit Detection and Maturity Analysis Using YOLO and K-Means SegmentationMohamed Ouhda, Zarouit Yousra, Brahim AksasseThe date palm (Phoenixdactylifera) is a large palm with exotic fruits measuring up to 30 metersin height. The date palm produces fruits rich in nutrients provides a multitudeof secondary products, and generates income necessary for the survival of alarge population. Losses attributed to manual harvesting encompass bothquantitative and qualitative aspects, with the latter measured throughattributes such as appearance, taste, texture, … …Journal of Computer Science, Vol. 19, Issue 10 · 9 Sep 2023 · 5,813 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSegmentation of Exudates via Color-based K-means Clustering and Statistical-based ThresholdingAtef Zaki Ghalwash, Aliaa Abdel-Halim Youssif, Ali Mohamed Nabil AllamThis paper provides a novel approach for the problem of detecting the yellowish lesions in the eye fundus images, such as hard and soft exudates, in a fully-automated manner. To solve this problem of segmenting exudates automatically, the fundus image was first converted into the L*a*b* color space to decouple the chromaticity information of the image. Next, the fundus image … …Journal of Computer Science, Vol. 13, Issue 10 · 14 Oct 2017 · 7,725 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSEGMENTATION OF ELECTROENCEPHALOGRAM SIGNALS DURING EPILEPTIC SEIZURES BY USING FUZZY C-MEANSMuhammad Abdy, Tahir AhmadElectroencephalogram (EEG) is a recording of electrical activity of the brain. It contains valuable information related to the different physiological states of the brain. A quantitative EEG analysis has been developed over the years that introduce objective measure, reflecting not only the characteristics of the brain activity itself but also giving clues concerning the underlying associated neural dynamics. In this … …American Journal of Applied Sciences, Vol. 11, Issue 10 · 11 Nov 2014 · 6,230 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleHybrid Exponential Particle Swarm Optimization K-Means Algorithm for Efficient Image SegmentationK. M. Murugesan, S. PalaniswamiThe introduction of unsupervised learning techniques like K-means inside the domain of Image Processing plays a vital role in Image Segmentation. The hybridization of this Algorithm by using Swarm Intelligent techniques further more improves the efficiency. Various works on hybridization of Particle Swarm Optimization (PSO) with K-means have been proposed and are found to be efficient in Image Segmentation. However, … …Journal of Computer Science, Vol. 8, Issue 11 · 25 Oct 2012 · 7,141 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleAmeliorate Fuzzy C-Means: An Ameliorate Fuzzy C-Means Clustering Algorithm for CT-Lung Image SegmentationJ. Bridget Nirmala, S. GowriEffective and efficient image segmentation acts as a preliminary stage for the computer-aided diagnosis of medical images. For image segmentation, many FCM-based clustering techniques have been proposed. Regrettably, the existing FCM technique does not generate accurate and standardized segmentation results. This is due to the noise present in the image as well as the random initialization of membership values for … …American Journal of Applied Sciences, Vol. 10, Issue 11 · 1 Oct 2013 · 6,819 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA HYBRID FIREFLY ALGORITHM WITH FUZZY-C MEAN ALGORITHM FOR MRI BRAIN SEGMENTATIONMutasem K. AlsmadiImage processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI). A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is … …American Journal of Applied Sciences, Vol. 11, Issue 9 · 10 Sep 2014 · 8,074 accessesView PDFCite
Research ArticleOpen accessIn the keywordsHeart Localization from Magnetic Resonance Images SequenceAhmed Farag, Mahmoud FakhreldinProblem statement: Heart localization is an important step in cardiac Magnetic Resonance Images (MRI) analysis. This study aims to locate the moving heart region from MRI sequence of images. Approach: The idea is to use the motion detection techniques to isolate the heart region from the background image and then apply morphological operations to construct a moving heart region mask. … …Journal of Computer Science, Vol. 8, Issue 4 · 30 Jan 2012 · 6,852 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Novel Approach to Medical Image SegmentationNandagopalan Shanmugam, Adiga B. Suryanarayana, Sudarshan TSB et al.Problem statement: Segmentation is a vital aspect of medical imaging. It aids in the visualization of medical data and diagnostics of various diseases. Ultrasound image segmentation, in particular echocardiographic image segmentation, is required to identify the regions of interest such as Left Ventricle (LV) and other cardiac cavities. Existing methods do not address the drawbacks of speed and quality of … …Journal of Computer Science, Vol. 7, Issue 5 · 7 May 2011 · 7,214 accessesView PDFCite
Research ArticleOpen accessIn the abstractIncorporation of Modified Region Growing into FCM Clustering for Extraction of Tumors in MR ImagesLovepreet Singh Brar, Jaget Singh, Bhawana Agrawal et al.Due to the tremendous growth of medical Magnetic Resonance (MR) images, it becomes an essential requirement for an automated extraction of diagnostically salient parts of the images to refine the transmission and storage process. Research method: This paper proposes an automated unsupervised machine learning segmentation technique named fuzzy c-means clustering based on modified region growing algorithm (RG-FCM) for the extraction … …Journal of Computer Science, Vol. 21, Issue 12 · 12 Feb 2026 · 1,477 accessesView PDFCite
Research ArticleOpen accessIn the abstractPolar Disentangled Non-Local Convolutional Neural Network for Skin Cancer Classification Using Dermoscopy ImagesUsha Thirugnanam, Nalini JosephSkin cancer classification is the process of identifying and categorizing various types of skin lesions into cancerous and non-cancerous. This process is performed to achieve accurate detection, which minimizes the risk of severe health complications. However, classifying the severity of skin lesion images is a difficult task because of subtle and overlapping visual patterns, which could lead to inaccurate performance. … …Journal of Computer Science, Vol. 22, Issue 6 · 16 Jun 2026 · 1,207 accessesView PDFCite