Segmentation of heart tissues using gathering and colour analysis techniques (original) (raw)
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Detection of Cardiac Tissues using K-means Analysis Methods in Nuclear Medicine Images
Open Access Macedonian Journal of Medical Sciences, 2021
BACKGROUND: Nuclear cardiology uses to diagnose the cardiac disorders such as ischemic and inflammation disorders. In cardiac scintigraphy, unraveling closely adjacent tissues in the image are challenging issue. AIM: The aim of the study is to detect of cardiac tissues using K-means analysis methods in nuclear medicine images. This study also aimed to reduce the existence of fleck noise that disturbs the contrast and make its analysis more difficult. METHODS: Thus, digital image processing uses to increase the detection rate of myocardium easily using its color-based algorithms. In this study, color-based K-means was used. The scintographs were converted into color space presentation. Then, each pixel in the image was segmented using color analysis algorithms. RESULTS: The segmented scintograph was displayed in distinct fresh image. The proposed technique defines the myocardial tissues and borders precisely. Both exactness rate and recall reckoning were calculated. The results were 97.3 + 8.46 (p > 0.05). CONCLUSION: The proposed technique offered recognition of the heart tissue with high exactness amount.
Distinguishing of different tissue types using K-Means clustering of color segmentation
Eastern-European Journal of Enterprise Technologies
Millions of lives might be saved if stained tissues could be detected quickly. Image classification algorithms may be used to detect the shape of cancerous cells, which is crucial in determining the severity of the disease. With the rapid advancement of digital technology, digital images now play a critical role in the current day, with rapid applications in the medical and visualization fields. Tissue segmentation in whole-slide photographs is a crucial task in digital pathology, as it is necessary for fast and accurate computer-aided diagnoses. When a tissue picture is stained with eosin and hematoxylin, precise tissue segmentation is especially important for a successful diagnosis. This kind of staining aids pathologists in distinguishing between different tissue types. This work offers a clustering-based color segmentation approach for medical images that can successfully find the core points of clusters through penetrating the red-green-blue (RGB) pairings without previous info...
Imaging in Medicine, 2021
Background: Nuclear cardiology can detect both ischemia and inflammation of the heart. It's difficult to distinguish adjacent tissues in a cardiac scintography image. Objectives: The aim of the study is to characterize of myocardium anomalies using watershed-based segmentation approaches in nuclear cardiology. The researchers seek to detect heart tissue in nuclear medicine pictures by using watershed methods. The contrast is blurred, and the presence of fleck noise complicates interpretation. Methods: Thus, color-based image processing can considerably boost the rate of cardiac detection in digital image processing. This study employed color-based k-means clustering. Color space conversion was carried out using scintographs. Following that, using color analysis tools, the image was segmented. Results: On exhibit was an altogether new and crystal-clear rendition of the segmented scintograph. The proposed method precisely defines the cardiac tissues and their borders. We calculated both the accuracy rate and the recall reckoning. 98.9+9.01 (p>0.05) and 0.07+0.004 (p>0.05) were the results. Conclusion: The proposed approach is used to identify cardiac tissue precisely.
Analysis of Biopsy Tissue Images based on Color Moment Technique and Morphology of Cell Nuclei
Korea Multimedia Sociaty, 2019
In recent years, different types of medical imaging techniques are used for the analysis of microscopic biopsy tissue images. The objective of this paper is to perform quantitative analysis and machine learning classification (MLC) based on the biopsy tissue image dataset which is categorized into four Gleason grade groups, namely Benign, Grade 3, Grade 4, and Grade 5. In this paper, an automated classification method has been developed to classify prostate cancer (PCa) from microscopic biopsy images stained with Haematoxylin and Eosin (H&E) compounds. The proposed methodology include ROI (region of interest) segmentation, watershed segmentation, features extraction, and classification. K-means and Watershed algorithms were used for color-based segmentation and separation of overlapping cell nuclei, respectively. In order to classify Gleason grade groups from microscopic biopsy images, color moment and morphological features are proposed and examined. Finally, the support vector machine (SVM) method is used for features classification into benign and malignant groups. Shape and size analysis has been carried out to understand the structure of cell nucleus. In total, 200 biopsy images, divided equally into two groups, were used for machine learning classification. The parameters of the kernel play a vital role in the classification process, and the best combination of í µí±ª and í µí¼¸wereµí¼¸were selected for better classification accuracy. The prediction model yielded an accuracy of 90% for benign vs. malignant.
Research in Medical Imaging Using Image Processing Techniques
Medical imaging is the procedure used to attain images of the body parts for medical uses in order to identify or study diseases. There are millions of imaging procedures done every week worldwide. Medical imaging is developing rapidly due to developments in image processing techniques including image recognition, analysis, and enhancement. Image processing increases the percentage and amount of detected tissues. This chapter presents the application of both simple and sophisticated image analysis techniques in the medical imaging field. This chapter also summarizes how to exemplify image interpretation challenges using different image processing algorithms such as k-means, ROI-based segmentation, and watershed techniques.
Segmentation is a fundamental process in digital image processing which has found extensive applications in areas such as medical image processing, compression, diagnosis arthritis from joint image, automatic text hand writing analysis, and remote sensing.The clustering methods can be used to segment any image into various clusters based on the similarity criteria like color or texture. In this research we have developed a method to segment color images using K-means clustering algorithm. K-means clustering algorithm divides the image into K clusters based on the similarity between the pixels in that cluster. In this research we have used Euclidean distance formula to define clusters in K-mean clustering. The proposed method has been applied to a variety of images and conclusions have been drawn.
Automatic recognition of fundamental tissues on histology images of the human cardiovascular system
Micron, 2016
Cardiovascular disease is the leading cause of death worldwide. Therefore, techniques for improving diagnosis and treatment in this field have become key areas for research. In particular, approaches for tissue image processing may support education system and medical practice. In this paper, an approach to automatic recognition and classification of fundamental tissues, using morphological information is presented. Taking a 40× or 10× histological image as input, three clusters are created with the k-means algorithm using a structural tensor and the red and the green channels. Loose connective tissue, light regions and cell nuclei are recognised on 40× images. Then, the cell nuclei's features-shape and spatial projection-and light regions are used to recognise and classify epithelial cells and tissue into flat, cubic and cylindrical. In a similar way, light regions, loose connective and muscle tissues are recognised on 10× images. Finally, the tissue's function and composition are used to refine muscle tissue recognition. Experimental validation is then carried out by histologist following expert criteria, along with manually annotated images that are used as a ground-truth. The results revealed that the proposed approach classified the fundamental tissues in a similar way to the conventional method employed by histologists. The proposed automatic recognition approach provides for epithelial tissues a sensitivity of 0.79 for cubic, 0.85 for cylindrical and 0.91 for flat. Furthermore, the experts gave our method an average score of 4.85 out of 5 in the recognition of loose connective tissue and 4.82 out of 5 for muscle tissue recognition.
Detection of Dead Tissues by Medical Image Using CLUSTERING
2014
This paper presents a new approach for image segmentation by applying k-means clustering and fuzzy c-means clustering. In image segmentation, clustering techniques are very important as they are intuitive and are also easy to implement. In This paper proposes a colour based segmentation method that uses K-means clustering and fuzzy c-means clustering technique. The k-means clustering is an instinct technique used to partition an image into k clusters. It produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and colour since no local constraints are applied to impose spatial continuity for medical images. In others side Fuzzy clustering, which defines fuzzy techniques to cluster data and they consider that an object can be classified to more than one clusters. This type of technique leads to clustering schemes that are compatible with everyday life experience as they handle the uncertainty of real data. The most impor...
Analysis of Color Images using Cluster based Segmentation Techniques
Image segmentation divides an image into several constituent components such as color, structure, shape, and texture. It forms a major research topic for many image processing researchers as the applications are endless. Its applications include image enhancement, object detection, image retrieval, image compression, and medical image processing to name a few. The segmentation of color images is necessary for efficient pattern recognition and feature extraction involving various color spaces such as RGB, HSV and CIE L*A*B*, etc. This paper describes the different cluster based segmentation techniques used for segmenting the different color images and the resultant is analyzed with subjective and objective measures. Initially, registered color images are considered as input. Then the cluster based segmentation techniques namely K-Means clustering, Pillar-Kmeans clustering and Fuzzy C-means (FCM) clustering techniques are applied. Further, the segmented image is analyzed with measures such as compactness and execution time. From the experimental results, it has been observed that K-means and Pillar-Kmeans are the most suitable techniques for RGB, HSV and LAB color spaces than the FCM technique.
Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm
American Journal of Neural Networks and Applications, 2019
In this paper we have segmented an image by using a k-clustering algorithm, using the Gaussian Mixture Model cluster to generate the initial centroid. Many types of research have been done in the area of image segmentation using clustering especially medical images, these techniques help medical scientists in the diagnosis of diseases thereby to cure this diseases K-means clustering algorithm one of these techniques, it is an unsupervised algorithm and it is used to segment the interest area from the background. We used also partial contrast stretching to improve the quality of the original image. And the final segmented result is comparing with the k-means clustering algorithm and we can conclude that the proposed clustering algorithm has better segmentation. Finally, MSE and PSNR are checked and discovered that they have small and large value respective, which are the condition for good image segmentation quality. And comparison for MSE and PSNR are done for the proposed method and classical K-means algorithm and it is found that the proposed method has better performance result.