Characterization of myocardium anomalies using watershed-based segmentation approaches in nuclear cardiology (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.
Segmentation of heart tissues using gathering and colour analysis techniques
Biomedical Research-tokyo, 2017
Image processing considers as powerful tool in recognition of heart tissues in nuclear medicine investigations that increase the percentage and amount of tissues detected. K-means gathering and colour analysis techniques were used in this study. The images treated using MatLab program. Firstly, the images were changed to colour rich space format. Then each pixel in the image was characterized using color analysis algorithms. Then the segment heart tissues were displayed in colour form in discrete new image. The quantitative analysis had done using both precision and recall computation. The results were 99.3+4.57. This method presented capacities cumulative of recognition of the heart tissue with high precision rate.
Increasing of Edges Recognition in Cardiac Scintography for Ischemic Patients
The detection of ischemia heart disease was usually scored by a trained nuclear medicine Physician by determining the ischemia location and size subjectively (by eyes). This subjective method will add to the 5% tolerance error, which might compromise the whole process of treatment especially in patients with severe conditions. The aim of this study is to increase the edge recognition in cardiac scintography images in patients with ischemic heart disease using L*a*b* color space and K-means clustering. First, we read the nuclear cardiac images. We then to convert the images form RGB color space to L*a*b* color space. Then we classify the colors in 'a*b*' space using K-means clustering. Then we label every pixel in the Image using the results from K-means. We then create images that segment the cardiac image by colour. Finally, we segment the cardiac image into a separate image. The sample of this study was (146 cases) and they showed increase enhancement. This segmentation technique (automatic scoring) and segmented images was adjudicated by three nuclear medicine physician as being comparable to other segmentation techniques created with manual editing (subjective scoring). This technique showed potentials increasing of detection of the myocardial ischemia rather than conventional one.
IJERT-A Study of Preprocessing and Segmentation Techniques on Cardiac Medical Images
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/a-study-of-preprocessing-and-segmentation-techniques-on-cardiac-medical-images https://www.ijert.org/research/a-study-of-preprocessing-and-segmentation-techniques-on-cardiac-medical-images-IJERTV3IS040403.pdf Segmentation is the process of simplifying and/or changing the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. Medical image segmentation plays a crucial role in delineation of regions of interest under study. It is essential in almost any medical imaging applications and is an essential step towards automated disease state detection in diagnostic imaging. Myocardial infraction is the leading cause of death throughout the world. Foreseeing and diagnosing of cardiac diseases usually require quantitative evaluation of the ventricle volume, mass, and ejection fraction. Advantages and disadvantages of the current segmentation methodologies are reviewed in perspective of medical imaging.
Using morphological and clustering analysis for left ventricle detection in MSCT cardiac images
2008 IEEE International Symposium on Signal Processing and Information Technology, 2008
In this paper, an unsupervised approach based on non-linear filtering and region growing techniques to obtain the endocardial surface is proposed. The filtering stage is performed using mathematical morphology operators in order to improve the left ventricle cavity information in multi slice computerized tomography images. A seed point located inside the cardiac cavity is used as inputfor the region growing algorithm. This seed point is propagated along the image sequence to obtain the left ventricle surfaces for all instants of the cardiac cycle. The method is validated by comparing the estimated surface with respect to left ventricle shapes drawn by a cardiologist. The average error obtained was 1.38 mm. Keywords Segmentation, mathematical morphology, unsupervised clustering, cardiac images, human heart, left ventricle.
Segmentation of Medical Image using Clustering and Watershed Algorithms
2011
Problem statement: Segmentation plays an important role in medical imaging. Segmentation of an image is the division or separation of the im age into dissimilar regions of similar attribute. I n this study we proposed a methodology that integrates clustering algorithm and marker controlled watershed segmentation algorithm for medical image segmentation. The use of the conservative watershed algorithm for medical image analysis is pervasive b ecause of its advantages, such as always being able to construct an entire division of the image. On the o ther hand, its disadvantages include over segmentat ion and sensitivity to false edges. Approach: In this study we proposed a methodology that integr ates K- Means clustering with marker controlled watershed segmentation algorithm and integrates Fuzzy C- Means clustering with marker controlled watershed segmentation algorithm separately for medical image segmentation. The Clustering algorithms are unsupervised learning algorithms, while the...
Automatic Segmentation Myocardiac Images Using Maximum Entropy
International Journal of Science and Engineering Applications, 2013
The digital image processing is a widespread applicable technique especially in the area where tools are used for feature extraction and to obtain patterns of studied images. Initially segmentation is used to separate the image into parts that represent an interest object that can be used for further specific study. There are various techniques present which performs such task but a common technique that adapt to all images is required, especially for complex or specific images. Hence our project basically aims to obtain a technique which is convenient for complex and different images. We tend to obtain a more specific result of the input image using histogram quantization, calculating valleys from analysis of histogram slope percentage, calculating threshold using maximum entropy. This approach provides more specific results over the already proposed technique which will be of great importance to the doctors, pathologists and surgeons to detect the potential cell rejection.
In this paper, an approach to the segmentation of microscopic color images is addressed, and applied to medical images. The approach combines a clustering method and a region growing method. Each color plane is segmented independently relying on a watershed based clustering of the plane histogram. The marginal segmentation maps intersect in a label concordance map. The latter map is simplified based on the assumption that the color planes are correlated. This produces a simplified label concordance map containing labeled and unlabeled pixels. The formers are used as an image of seeds for a color watershed. This fast and robust segmentation scheme is applied to several types of medical images.