scholarly journals Detection of Cardiac Tissues using K-means Analysis Methods in Nuclear Medicine Images

2021 ◽  
Vol 9 (A) ◽  
pp. 1272-1276
Author(s):  
Yousif Abdallah
Keyword(s):  
Image Processing ◽  
Nuclear Medicine ◽  
Nuclear Cardiology ◽  
Detection Rate ◽  
Color Space ◽  
Heart Tissue ◽  
Color Analysis ◽  
Cardiac Tissues ◽  
Analysis Methods ◽  
Cardiac Disorders

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.

Position paper nuclear cardiology: Update 2008

2009 ◽  
Vol 48 (02) ◽  
pp. 71-78 ◽  
Author(s):  
F. Bengel ◽  
U. Büll ◽  
W. Burchert ◽  
P. Kies ◽  
R. Kluge ◽  
...  
Keyword(s):  
Nuclear Medicine ◽  
Nuclear Cardiology ◽  
State Of The Art ◽  
Position Paper ◽  
Circulation Research ◽  
Diagnostic Algorithms ◽  
Clinical Diagnostic ◽  
Common Position ◽  
German Association

SummaryNuclear cardiology is well established in clinical diagnostic algorithms for many years. This is an update 2008 of the first common position paper of the German Association of Nuclear Medicine and the German Association of Cardiology, Heart and Circulation Research published in 2001 aiming at an overview of state-of-the-art scintigraphic methods.

scholarly journals A perception-based color space for illumination-invariant image processing

2008 ◽  
Vol 27 (3) ◽  
pp. 1-7 ◽  
Author(s):  
Hamilton Y. Chong ◽  
Steven J. Gortler ◽  
Todd Zickler
Keyword(s):  
Image Processing ◽  
Color Space ◽  
Illumination Invariant

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

scholarly journals Second Wave of COVID-19 Pandemic in Malaysia and the Impact on Nuclear Cardiology Services: An Institutional Experience

2021 ◽  
Vol 5 ◽  
pp. 25
Author(s):  
Ahmad Zaid Zanial ◽  
Syifaa Aminudin ◽  
FatinHayani Mohamad Najib ◽  
Siti Zarina Amir Hassan
Keyword(s):  
Nuclear Medicine ◽  
Infection Control ◽  
Nuclear Cardiology ◽  
Heart Diseases ◽  
Imaging Techniques ◽  
Main Difficulty ◽  
Technetium 99M ◽  
Institutional Experience ◽  
The Impact ◽  
Second Wave

Introduction: Nuclear cardiology applying radioactive tracer and hybrid imaging techniques are able to provide information needed to detect and evaluate ischaemic heart diseases. In our centre, nuclear cardiology services involving stress and rest myocardial perfusion scans and viability studies contribute about 40% of overall scan workload. The second wave of COVID- 19 pandemic in Malaysia announced by the end of February 2020 has affected nuclear cardiology services.Objectives: Our aims were to determine the impact of COVID-19 pandemic second wave on the nuclear cardiology imaging studies performed as well as to ascertain crucial institutional experience especially unavoidable problem and adjustment during the period.Methods: A review of Technetium-99m tetrofosmin radiopharmaceutical dispensing data and scan records for 1st February to 31st August 2019 and 2020 was conducted at Nuclear Medicine Department, Hospital Kuala Lumpur. Figures were compiled and statistical analysis done. Survey and focus discussion conducted involving nuclear medicine physicians, pharmacists and physic officers to identify the main difficulty faced and most important Job-adapting measure taken.Results: A total of 1109 cardiac radiopharmaceutical doses dispensed during the studied period but were less when compared to 1342 doses dispensed last year. Significant reduction was noted in April and May 2020 with only 69 and 67 cases respectively in comparison with monthly average of 192 cases in 2019. Although some scan appointments were postponed to avoid the usual waiting area congestions, the main difficulty faced was Technetium-99m generator supply disruption with limited production in Europe and international transportation restriction. Implementing infection control standard operating procedure (SOP) instructions as part of routine work practice with emphasis of time, distance and shielding concept was the most important Job-adapting measure.Conclusion: Nuclear cardiology services were affected by the second wave of COVID-19 pandemic locally. Main problem ascertained was disruption of radioactive supply. Most important adjustment was infection control SOP implementation.International Journal of Human and Health Sciences Supplementary Issue: 2021 Page: S25

scholarly journals Impurities Detection in Intensity Inhomogeneous Edible Bird’s Nest (EBN) Using a U-Net Deep Learning Model

2021 ◽  
Vol 11 (2) ◽  
pp. 135-145
Author(s):  
Ying-Heng Yeo ◽  
Kin-Sam Yen
Keyword(s):  
Image Processing ◽  
Optical Properties ◽  
Detection Rate ◽  
Detection Algorithm ◽  
Misclassification Rate ◽  
Dice Coefficient ◽  
Edible Bird’S Nest ◽  
Deep Learning Model ◽  
Output Probability ◽  
Previous Image

As an important export, cleanliness control on edible bird’s nest (EBN) is paramount. Automatic impurities detection is in urgent need to replace manual practices. However, effective impurities detection algorithm is yet to be developed due to the unresolved inhomogeneous optical properties of EBN. The objective of this work is to develop a novel U-net based algorithm for accurate impurities detection. The algorithm leveraged the convolution mechanisms of U-net for precise and localized features extraction. Output probability tensors were then generated from the deconvolution layers for impurities detection and positioning. The U-net based algorithm outperformed previous image processing-based methods with a higher impurities detection rate of 96.69% and a lower misclassification rate of 10.08%. The applicability of the algorithm was further confirmed with a reasonably high dice coefficient of more than 0.8. In conclusion, the developed U-net based algorithm successfully mitigated intensity inhomogeneity in EBN and improved the impurities detection rate.

scholarly journals IDENTIFICATION OF MEGALOBLASTIC ANEMIA CELLS THROUGH THE USE OF IMAGE PROCESSING TECHNIQUES

2018 ◽  
Vol 4 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Asaad Babker ◽  
Vyacheslav Lyashenko
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Blood Smear ◽  
Color Image ◽  
Megaloblastic Anemia ◽  
Cell Structure ◽  
Color Space ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Blood Smear Image

Objective: Our aim is to show the possibility of using different image processing techniques for blood smear analysis. Also our aim is to determine the sequence of image processing techniques to identify megaloblastic anemia cells. Methods: We consider blood smear image. We use a variety of image processing techniques to identify megaloblastic anemia cells. Among these methods, we distinguish the modification of the color space and the use of wavelets. Results: We developed a sequence of image processing techniques for blood smear image analysis and megaloblastic anemia cells identification. As a characteristic feature for megaloblastic anemia cells identification, we consider neutrophil image structure. We also use the morphological methods of image analysis in order to reveal the nuclear lobes in neutrophil structure. Conclusion: We can identify the megaloblastic anemia cells. To do this, we use the following sequence of blood smear image processing: color image modification, change of the image contrast, use of wavelets and morphological analysis of the cell structure. 

Plaque Color Analysis by the Conventional Yellow-Color Grading System and Quantitative Measurement Using LCH Color Space

2007 ◽  
Vol 20 (5) ◽  
pp. 324-334 ◽  
Author(s):  
KATSUKI OKADA ◽  
YASUNORI UEDA ◽  
JOTA OYABU ◽  
NOBUYUKI OGASAWARA ◽  
ATSUSHI HIRAYAMA ◽  
...  
Keyword(s):  
Quantitative Measurement ◽  
Color Space ◽  
Grading System ◽  
Color Analysis ◽  
Color Grading ◽  
Yellow Color

scholarly journals Linked color imaging enhances endoscopic detection of sessile serrated adenoma/polyps

2018 ◽  
Vol 06 (03) ◽  
pp. E322-E334 ◽  
Author(s):  
Daisaku Fujimoto ◽  
Naoki Muguruma ◽  
Koichi Okamoto ◽  
Yasuteru Fujino ◽  
Kaizo Kagemoto ◽  
...  
Keyword(s):  
Detection Rate ◽  
Controlled Trial ◽  
Color Space ◽  
Blue Laser ◽  
Sessile Serrated Adenoma ◽  
Serrated Adenoma ◽  
Color Imaging ◽  
Detection Rates ◽  
Still Image ◽  
Linked Color Imaging

Abstract Background and study aims Although new image-enhanced endoscopy (IEE) technologies such as blue laser imaging (BLI), BLI-bright, and linked color imaging (LCI) have been developed, their utility for the detection of sessile serrated adenoma/polyps (SSA/Ps) is still unclear. This study aimed to evaluate the utility of BLI, BLI-bright, and LCI for SSA/P detection in still image examinations and in a prospective randomized controlled trial (RCT). Patients and methods A group of 6 expert and non-expert endoscopists read 200 endoscopic still images containing SSA/P lesions using white light image (WLI), BLI, BLI-bright, and LCI. Color differences were calculated using the color space method. A prospective RCT of tandem colonoscopy with WLI and LCI was performed. Patients with SSA/P and those with a history of SSA/P that had been endoscopically removed were enrolled and randomly allocated to WLI-LCI or LCI-WLI groups. Additional endoscopic detection rates for SSA/P were compared between the 2 groups. Results LCI showed the highest SSA/P detection rate among the 4 modes for both expert and non-expert endoscopists. The detection rate with LCI for the 6 expert endoscopists (mean 98.3 ± standard deviation 2.0 %) was significantly higher than that with WLI (86.7 ± 6.0 %, P < 0.01). Likewise, the detection rate with LCI for the 6 non-expert endoscopists (92.3 ± 2.9 %) was significantly higher than that with WLI (72.7 ± 11.5 %, P < 0.01). The color difference of SSA/P with LCI was the highest among the 4 modes, and was significantly higher than with WLI (median 15.9, (interquartile range 13.7 – 20.6) vs. 10.2, (7.6 – 14.2); P < 0.0001). In the RCT, a total of 44 patients (WLI-LCI 22 vs. LCI-WLI 22) underwent colonoscopy. The additional detection rate for SSA/P in the second inspection in the WLI-LCI group (21.6 %, 8/37) was significantly higher than in the LCI-WLI group (3.2 %, 1/31; P = 0.02). The small, flat, non-mucus and isochromatic SSA/Ps in the transverse colon were detected more frequently in the second inspection with LCI. Conclusions LCI was the most sensitive mode for SSA/P detection among WLI, BLI, BLI-bright, and LCI in the still image examinations. Our RCT strongly suggests that LCI is superior to conventional WLI for SSA/P detection during colonoscopy. UMIN000017599.

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