Comparison of color thresholding and global thresholding for Ziehl-Neelsen TB bacilli slide images in sputum samples

Wahidah, M. F. Nurul; Mustafa, Nazahah; Mashor, Mohd Yusoff; Noor, Siti Suraiya Md

doi:10.1109/icobe.2015.7235913

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…5. This approach is compared with global thresholding method on the same dataset in [47], where, global thresholding is performed based on the C-Y color information of the image pixels which contains TB. The technique retains the red pixels of TB bacilli which fall in the range of hue 0 0 till 120 0 while the other pixels which fall in the range of 121 0 till 360 0 are eliminated.…”

Section: A Threshold Based Segmentation (Tbs)mentioning

confidence: 99%

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A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential

Kulwa

Zhao

et al. 2019

IEEE Access

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Microorganisms play a great role in ecosystem, wastewater treatment, monitoring of environmental changes, and decomposition of waste materials. However, some of them are harmful to humans and animals such as tuberculosis bacteria and plasmodium. In such course, it is important to identify, track, analyze, consider the beneficial side and get rid of the negative effects of microorganisms using fast, accurate, and reliable methods. In recent decades, image analysis techniques have been used to address the drawbacks of manual traditional approaches in the identification and analysis of microorganisms. As image segmentation being an important step (technique) in the detection, tracking, monitoring, feature extraction, modeling, and analysis of microorganisms, different methods have been deployed, from classical approaches to current deep neural networks upon different challenges on microorganism images. This survey comprehensively analyses the various studies focused on developing microorganism image segmentation methods in the last 30 years (since 1989). In this survey, segmentation methods are categorized into classical and machine learning methods. Furthermore, these methods are subcategorized into threshold-based, regionbased, and edge-based which belong to classical methods, supervised and unsupervised machine leaningbased methods which belong to machine learning category. A growth trend of different methods and most frequently used methods in each category are meticulously analyzed. A clear explanation of the suitability of these methods for different segmentation challenges encountered on microscopic microorganism images is also enlightened. INDEX TERMSMicroorganism segmentation, content-based microscopic image analysis, feature extraction, microscopic images, classical methods, machine learning.

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Section: A Threshold Based Segmentation (Tbs)mentioning

confidence: 99%

“…During experiments, FIGURE 6. Global and color tresholding processes [47]. different combination of five population images that include 255 isolated overlapping C. elegan worms are used.…”

Section: A Threshold Based Segmentation (Tbs)mentioning

confidence: 99%

A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential

Kulwa

Zhao

et al. 2019

IEEE Access

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“…If the image pixel f (x,y) is above the threshold value, the pixel will be changed to white and the value will be 1. If the image pixel f (x,y) has the same value or is a level below the threshold value, so the pixel is changed to black and the value becomes 0 [16].…”

Section: Thresholdmentioning

confidence: 99%

Processing of Drone’s Digital Image for Determining Border of Rice Fields with Edge Detection Method

Diman

Zainuddin

Manjang

2019

EPI-IJE

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Edge detection was the basic thing used in most image processing applications to get information from the image frame as a beginning for extracting the features of the segmentation object that will be detected. Nowadays, many edge detection methods create doubts in choosing the right edge detection method and according to image conditions. Based on the problems, a study was conducted to compare the performance of edge detection using methods of canny, Sobel and laplacian by using object of rice field. The program was created by using the Python programming language on OpenCV. The result of the study on one image test that the Canny method produces thin and smooth edges and did not omit the important information on the image while it has required a lot of computing time. Classification is generally started from the data acquisition process; pre-processing and post-processing. Canny edge detection can detect actual edges with minimum error rates and produce optimal image edges. The threshold value obtained from the Canny method was the best and optimal threshold value for each method. The result of a test by comparing the three methods showed that the Canny edge detection method gives better results in determining the rice field boundary, which was 90% compared to Sobel 87% and laplacian 89%.

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“…In addition, early diagnosis also reduces the risk of transmitting viruses and bacteria to susceptible persons. Early diagnosis of lung conditions will be difficult to implement if conventional tests, such as sputum tests, spirometry tests, chest x-ray and CT scan are used [6]- [9]. The reason is because these tests must be carried out in adequate health care facilities and take a long time to get the test results.…”

Section: Introductionmentioning

confidence: 99%

Classification of lung condition for early diagnosis of pneumonia and tuberculosis based on embedded system

2021

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The lungs are the main organs in the respiratory system that have a function as a place for exchange of oxygen and carbon dioxide. Due to the importance of lung function, indications of lung disorders must be detected and diagnosed early. Research on the classification of lung conditions generally uses chest x-ray image data. Where a time-consuming procedure is needed to obtain the data. In this research, an embedded system to diagnose lung conditions was designed. The system was made to be easy to use independently and provides real-time examination results. This system uses parameters of body temperature, oxygen saturation, fingernail color and lung volume in classifying lung conditions. There are three conditions that can be classified by the system, that is healthy lungs, pneumonia and tuberculosis. The k-nearest neighbor method was used in the classification process in the designed system. The dataset used was 51 data obtained from the hospital. Each data already has a label in the form of lung condition based on the doctor’s diagnosis. The proposed system has an accuracy of 88.24% in classifying lung conditions.

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Comparison of color thresholding and global thresholding for Ziehl-Neelsen TB bacilli slide images in sputum samples

Cited by 7 publications

References 4 publications

A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential

A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential

Processing of Drone’s Digital Image for Determining Border of Rice Fields with Edge Detection Method

Classification of lung condition for early diagnosis of pneumonia and tuberculosis based on embedded system

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