scholarly journals Distinguishing of different tissue types using K-Means clustering of color segmentation

2021 ◽  
Vol 5 (2 (113)) ◽  
pp. 22-28
Author(s):  
Zinah R. Hussein ◽  
Ans Ibrahim Mahameed ◽  
Jawaher Abdulwahab Fadhil
Keyword(s):  
Clustering Algorithms ◽  
Critical Role ◽  
Color Space ◽  
Digital Pathology ◽  
Color Segmentation ◽  
Blue Color ◽  
Tissue Segmentation ◽  
Segmentation Approach ◽  
Severity Of The Disease ◽  
Dark Blue

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 information. Here, the number of RGB pairs functions as a clusters’ number to increase the accuracy of current algorithms by establishing the automated initialization settings for conventional K-Means clustering algorithms. On a picture of tissue stained with eosin and hematoxylin, the developed K-Means clustering technique is used in this study (H&E). The blue items are found in Cluster 3. There are things in both light and dark blue. The results showed that the proposed technique can differentiate light blue from dark blue employing the 'L*' layer in L*a*b* Color Space (L*a*b* CS). The work recognized the cells' nuclei with a dark blue color successfully. As a result, this approach may aid in precisely diagnosing the stage of tumor invasion and guiding clinical therapies

scholarly journals A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

2021 ◽  
Vol 13 (6) ◽  
pp. 1211
Author(s):  
Pan Fan ◽  
Guodong Lang ◽  
Bin Yan ◽  
Xiaoyan Lei ◽  
Pengju Guo ◽  
...  
Keyword(s):  
Vision System ◽  
False Positive Rate ◽  
Clustering Algorithms ◽  
False Negative ◽  
Color Space ◽  
False Negative Rate ◽  
Feature Selection Method ◽  
Image Features ◽  
Accurate Identification ◽  
Rgb Color Space

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

scholarly journals Integrated Pathology Informatics Enables High-Quality Personalized and Precision Medicine: Digital Pathology and Beyond

2017 ◽  
Vol 142 (3) ◽  
pp. 369-382 ◽  
Author(s):  
Zoya Volynskaya ◽  
Hung Chow ◽  
Andrew Evans ◽  
Alan Wolff ◽  
Cecilia Lagmay-Traya; ◽  
...  
Keyword(s):  
Data Collection ◽  
Precision Medicine ◽  
Critical Role ◽  
Digital Pathology ◽  
Geographic Area ◽  
Whole Slide Imaging ◽  
High Quality ◽  
Pathology Informatics ◽  
Prognosis And Prediction ◽  
The Right

Context.— The critical role of pathology in diagnosis, prognosis, and prediction demands high-quality subspecialty diagnostics that integrates information from multiple laboratories. Objective.— To identify key requirements and to establish a systematic approach to providing high-quality pathology in a health care system that is responsible for services across a large geographic area. Design.— This report focuses on the development of a multisite pathology informatics platform to support high-quality surgical pathology and hematopathology using a sophisticated laboratory information system and whole slide imaging for histology and immunohistochemistry, integrated with ancillary tools, including electron microscopy, flow cytometry, cytogenetics, and molecular diagnostics. Results.— These tools enable patients in numerous geographic locations access to a model of subspecialty pathology that allows reporting of every specimen by the right pathologist at the right time. The use of whole slide imaging for multidisciplinary case conferences enables better communication among members of patient care teams. The system encourages data collection using a discrete data synoptic reporting module, has implemented documentation of quality assurance activities, and allows workload measurement, providing examples of additional benefits that can be gained by this electronic approach to pathology. Conclusion.— This approach builds the foundation for accurate big data collection and high-quality personalized and precision medicine.

scholarly journals Influence of Luminance L* in the L*a*b* Color Space during Color Segmentation in Highly Saturated Color Images

2015 ◽  
Vol 102 (1) ◽  
pp. 21-31
Author(s):  
Rodolfo Alvarado-Cervantes ◽  
Edgardo M. Felipe-Riveron ◽  
Vladislav Khartchenko ◽  
Oleksiy Pogrebnyak
Keyword(s):  
Color Space ◽  
Color Images ◽  
Color Segmentation ◽  
Saturated Color

Hybrid Image Segmentation Within a Computer Vision Hierarchy

1993 ◽  
Vol 30 (1) ◽  
pp. 51-64
Author(s):  
Ray Thomas ◽  
Fariborz Zahedi
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Vision System ◽  
Critical Role ◽  
Generic Model ◽  
Test Results ◽  
Segmentation Approach ◽  
Edge Based

Hybrid image segmentation within a computer vision hierarchy A generic model of a computer vision system is presented which highlights the critical role of image segmentation. A hybrid segmentation approach, utilising both edge-based and region-based techniques, is proposed for improved quality of segmentation. An image segmentation architecture is outlined and test results are presented and discussed.

scholarly journals Developing an Automatic Color Determination Procedure for the Quality Assessment of Mangos (Mangifera indica) Using a CCD Camera and Color Standards

Foods ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1709
Author(s):  
Khanitta Ratprakhon ◽  
Werner Neubauer ◽  
Katharina Riehn ◽  
Jan Fritsche ◽  
Sascha Rohn
Keyword(s):  
Vision System ◽  
Mangifera Indica ◽  
Color Space ◽  
Evaluation Process ◽  
Ccd Camera ◽  
Optimal Level ◽  
Blue Color ◽  
Color Code ◽  
Color Determination

Color is one of the key sensory characteristics in the evaluation of the quality of mangos (Mangifera indica) especially with regard to determining the optimal level of ripeness. However, an objective color determination of entire fruits can be a challenging task. Conventional evaluation methods such as colorimetric or spectrophotometric procedures are primarily limited to a homogenous distribution of the color. Accordingly, a direct assessment of the mango quality with regard to color requires more pronounced color determination procedures. In this study, the color of the peel and the pulp of the mango cultivars “Nam Dokmai”, “Mahachanok”, and “Kent” was evaluated and categorized into various levels of ripeness using a charge-coupled device (CCD) camera in combination with a computer vision system and color standards. The color evaluation process is based on a transformation of the RGB (red, green, and blue) color space values into the HSI (hue, saturation, and intensity) color system and the Natural Color Standard (NCS). The results showed that for pulp color codes, 0560-Y20R and 0560-Y40R can be used as appropriate indicators for the ripeness of the cultivars “Nam Dokmai” and “Mahachanok”. The peels of these two mango cultivars present two distinct colors (1050-Y40R and 1060-Y40R), which can be used to determine the fruit maturity during the post-ripening process. However, in the case of the cultivar “Kent”, peel color detection was not an applicable approach for determining ripeness; thus, the determination of the pulp color with the color code 0550-Y20R gave promising results.

Human Body Parts Detection Using YCbCr Color Space

2013 ◽  
Vol 393 ◽  
pp. 556-560
Author(s):  
Nurul Fatiha Johan ◽  
Yasir Mohd Mustafah ◽  
Nahrul Khair Alang Md Rashid
Keyword(s):  
Human Body ◽  
Skin Color ◽  
Color Space ◽  
Detection Algorithm ◽  
Color Segmentation ◽  
Color Model ◽  
Body Parts ◽  
Color Spaces ◽  
Morphological Operation ◽  
Ycbcr Color Space

Skin color is proved to be very useful technique for human body parts detection. The detection of human body parts using skin color has gained so much attention by many researchers in various applications especially in person tracking, search and rescue. In this paper, we propose a method for detecting human body parts using YCbCr color spaces in color images. The image captured in RGB format will be transformed into YCbCr color space. This color model will be converted to binary image by using color thresholding which contains the candidate human body parts like face and hands. The detection algorithm uses skin color segmentation and morphological operation.

A hybrid tissue segmentation approach for brain MR images

2006 ◽  
Vol 44 (3) ◽  
pp. 242-249 ◽  
Author(s):  
Tao Song ◽  
Charles Gasparovic ◽  
Nancy Andreasen ◽  
Jeremy Bockholt ◽  
Mo Jamshidi ◽  
...  
Keyword(s):  
Mr Images ◽  
Tissue Segmentation ◽  
Segmentation Approach ◽  
Brain Mr Images

New data clarifying the taxonomy of European members of the Lepidocyrtus pallidus–serbicus group (Collembola, Entomobryidae)

Zootaxa ◽  
2018 ◽  
Vol 4429 (3) ◽  
pp. 548 ◽  
Author(s):  
EDUARDO MATEOS ◽  
DANIEL WINKLER
Keyword(s):  
New Species ◽  
Related Species ◽  
Identification Key ◽  
Blue Color ◽  
Dark Blue ◽  
Dark Blue Color

The genus Lepidocyrtus is fairly well explored in Hungary and was up to now represented by 18 species. Systematic mesofauna survey of a swamp woodland gave us the opportunity to describe the new species, L. florae sp. nov., characterized by the dark blue color, the dorsal macrochaetae formula A0A2aA2A3S3Pa5/00/0101+2 and the absence of scales on the antennae. Related species L. arrabonicus, L. pallidus, L. pseudosinelloides and L. weidneri were also revised with particular attention to clarify the interpretation of the dorsal chaetotaxy of the head. The observed variability in abdominal chaetotaxy of L. pallidus suggests that the only character differentiating between this species and L. weidneri is the labial chaetotaxy, with chaeta r (in L. pallidus) and chaeta R (in L. weidneri). An identification key to European Lepidocyrtus species with dorsal trunk macrochaetae formula 00/0101+2 is also provided. 

scholarly journals Deteksi Api pada Video dengan Gaussian Mixture Model Untuk Deteksi Gerakan Dan Segmentasi Warna Api Dalam Ruang Warna YCbCr

2021 ◽  
Vol 3 (1) ◽  
pp. 108-119
Author(s):  
Ristirianto Adi ◽  
I Gede Pasek Suta Wijaya
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Processing Speed ◽  
Color Space ◽  
Gaussian Mixture ◽  
Color Segmentation ◽  
Test Results ◽  
Ycbcr Color Space ◽  
Property Loss ◽  
Better Than

Fire is a disaster that can endanger lives and cause property loss. The solution to detect fire that is commonly used today is to use a sensor. Fire sensors can be used together with surveillance cameras (CCTV) which are now being installed in many office buildings. This study tries to build a model for detecting fire in video with a digital image processing approach using the Gaussian Mixture Model for motion detection and fire color segmentation in the YCbCr color space. The model is then tested with metrics for accuracy, precision, recall, and processing speed. The dataset used is in the form of videos with small, medium, large fire sizes, and videos that only have fire-like objects. The test results show that the algorithm is able to detect fire when the size of the fire is not too small or the position of the fire is close enough to the camera. For videos with a resolution of 800x600 and a framerate of 30 fps, it can achieve 66.89% accuracy, 73.77% precision, and 66.66% recall. The performance during the day is relatively better than at night. Algorithm processing speed is too slow to be implemented in real-time

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