Maneerat, Noppadol

Preferred name

Maneerat, Noppadol

Main Affiliation

School of Engineering

noppadol.ma@kmitl.ac.th

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Automatic exudate extraction for early detection of Diabetic Retinopathy
(2013-01-01)
Sreng, Syna
;
Takada, Jun Ichi
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Maneerat, Noppadol
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Isarakorn, Don
;
Pasaya, Bundit
Diabetic Retinopathy (DR) is the most common cause of blindness in diabetic patients, but early detection and timely treatment can prevent this problem. Exudates have been found to be one of the signs and serious DR anomalies so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. The aim of this study is to automatically detect these lesions in fundus images. To achieve this goal, the proposed method first preprocesses to improve the quality of fundus image, and then Optic Disc (OD) is detected and eliminated to prevent the interference to the result of exudate detection by combination of 3 methods; image binarization, Region Of Interest (ROI) based segmentation and Morphological Reconstruction (MR). Next, exudates are detected by applying the maximum entropy thresholding to filter out the bright pixels from the result of OD region eliminated. Since the result contains some noises which appear as bright light at the edge of fundus area in some images, that affect is considered and eliminated to improve the result of false positive. Finally, exudates are extracted by using MR. The proposed technique has been tested on 100 fundus images from hospital. Experimental results show that 91 % of exudate is extracted correctly with the average process of 3.92 second per image. © 2013 IEEE.
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Automated microaneurysms detection in fundus images using image segmentation
(2017-04-19)
Sreng, Syna
;
Maneerat, Noppadol
;
Hamamoto, Kazuhiko
Diabetic retinopathy is one of the complicated diseases which occurs in diabetic patients when the affects damage the retina. The eyes vision can lead to be lost in case of late treatment. Microaneurysms are the earliest detectable abnormalities of diabetic retinopathy, so the automated detection of the lesions is essential and useful task. This paper proposed a simple method to detect microaneurysms based on its characteristics in fundus images using some techniques in image segmentation. First, we preprocessed to reduce image noise and improve the contrast. Then we segmented them using Canny edges detection and maximum entropy thresholding. The characteristics of microaneurysms which appear as small red dots and circular shape are the specific points to discriminate them from the other lesions as well as the anatomical structures of the fundus image by applying area and eccentricity methods. Finally, the morphological operation was applied to mark out these symptoms. The results were analysis by ophthalmologist in order to define system accuracy and preciseness. According to results of comparison, we found that the accuracy is 90 % and the average processing time is 9.53 seconds per image.
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A "dual-acceptor channel" membraneless gas-diffusion unit for simultaneous determination of ethanol and acetaldehyde in liquors using reverse flow injection
(2018-01-01)
Choengchan, Nathawut
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Poontong, Bangerdsuk
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Mathaweesansurn, Arjnarong
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Maneerat, Noppadol
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Motomizu, Shoji
A new design of membraneless gas-diffusion unit with dual acceptor channels for separation, collection and simultaneous determination of two volatile analytes in liquid sample is presented. The unit is comprised of three parallel channels in a closed module. A sample is aspirated into the central channel and two kinds of reagents are introduced into the other two channels. Two analytes are isolated from the sample matrix by diffusion into head-space and absorbed into the specific reagents. Non-absorbed vapor is released by opening the programmable controlled lid. The unit was applied to liquors for measurement of ethanol and acetaldehyde using reverse flow injection. Dichromate and nitroprusside were exploited as reagents for colorimetric detection of ethanol and acetaldehyde, respectively. Good linearity ranges (r2 > 0.99) with high precision (RSD < 2%) and high accuracy (recovery: 90 - 105%) were achieved. The results were compared to the results by GC-FID and no significant difference was observed by paired t-test (95% confidence).
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Automated diabetic retinopathy screening system using hybrid simulated annealing and ensemble bagging classifier
(2018-07-22)
Sreng, Syna
;
Maneerat, Noppadol
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Hamamoto, Kazuhiko
;
Panjaphongse, Ronakorn
Diabetic Retinopathy (DR) is the leading cause of blindness in working-age adults globally. Primary screening of DR is essential, and it is recommended that diabetes patients undergo this procedure at least once per year to prevent vision loss. However, in addition to the insufficient number of ophthalmologists available, the eye examination itself is labor-intensive and time-consuming. Thus, an automated DR screening method using retinal images is proposed in this paper to reduce the workload of ophthalmologists in the primary screening process and so that ophthalmologists may make effective treatment plans promptly to help prevent patient blindness. First, all possible candidate lesions of DR were segmented from the whole retinal image using a combination of morphological-top-hat and Kirsch edge-detection methods supplemented by pre- and post-processing steps. Then, eight feature extractors were utilized to extract a total of 208 features based on the pixel density of the binary image as well as texture, color, and intensity information for the detected regions. Finally, hybrid simulated annealing was applied to select the optimal feature set to be used as the input to the ensemble bagging classifier. The evaluation results of this proposed method, on a dataset containing 1200 retinal images, indicate that it performs better than previous methods, with an accuracy of 97.08%, a sensitivity of 90.90%, a specificity of 98.92%, a precision of 96.15%, an F-measure of 93.45% and the area under receiver operating characteristic curve at 98.34%.
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Suitable Supervised Machine Learning Techniques for Malignant Mesothelioma Diagnosis
(2019-01-10)
Win, Khin Yadanar
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Maneerat, Noppadol
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Choomchuay, Somsak
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Sreng, Syna
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Hamamoto, Kazuhiko
Malignant Mesothelioma (MM) is a rare, aggressive cancer that grows in the lining of the internal organs such as lung, abdomen or heart. Fousing on MM diagnosis, in this paper, we investigate multiple machine learning methods and compare for accurate MM diagnosis results. Seven machine learning algorithms namely (i) Linear Discriminant Analysis (LDA), (ii) Naïve Bayes, (iii) K Nearest Neighborhood (KNN), (iv) Support Vector Machine (SVM), (v) Decision Tree (DT), (vi) Logistic Regression (LogR) and (vii) Random forest (RF) algorithms are exploited. The experiments dataset containing 324 cases with 34 features and six performance measures are used to assess the accuracy of evaluated classifiers. The average accuracy of LDA, NB, KNN, SVM, DT, LogR and RF are 61.73%, 67.90%, 91.36%, 100%, 100%, 100% and 100%, respectively. In addition, the computational complexity of each method is also analyzed. Each algoritm is judged based on its classification accuracy and computational complexity. It is found that SVM, DT, LogR and RF outperform the others and even previous studies.
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A cleaning robot for solar panels
(2019-07-01)
Ronnaronglit, Nawat
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Maneerat, Noppadol
Many types of Solar Panel Cleaning Robots are used in many countries, such as Solar Frames Connected Robot. Developing the Solar Panel Cleaning Robots can be used to work instead human especially it is not necessary to have a supervisor for working control and reduce a risk of damage from moving a robot. However, Solar Frames Connected Robot still has defect which limited size of solar panel and not easy to move. This research aims to design and develop the Solar Panel Cleaning Robots by studying Solar Panel Cleaning Robots movement which work suitable in Thailand, Wireless Joystick, Sensor Sonar using Gear Motor and ARDUINO microcontroller. The robot will clean a solar cell by using a rotary brush with water spray to improve cleaning system. Result of studying Solar Panel Cleaning Robots movement by using Gear Motor can operate at a surface level of 0-30 degrees Celsius and cleaning system by using rotary brush can be clean 80% of solar panel.
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A mobile phone-based analyzer for quantitative determination of urinary albumin using self-calibration approach
(2017-04-01)
Mathaweesansurn, Arjnarong
;
Maneerat, Noppadol
;
Choengchan, Nathawut
This work demonstrates use of a smart mobile phone installed with an Android application, termed ‘Albumin smart test’, as an analyzer for quantitative determination of urinary albumin. The reaction between albumin and tetrabromophenolphthalein ethyl ester (TBPE) in the presence of Triton X-100 was employed for detection principle.The mobile phone was exploited with the sample cassette and the test paper. One sample cassette composes of two holders for accommodation of control and test samples. The test paper was designed in order to contain standard colorimetric strip and space for situating the sample cassette. Optical images of the strip and the samples were simultaneously captured in a single shot by a digital camera of the mobile phone and were digitally processed by the developed application for quantification of the albumin concentration based on self-calibration approach. With the advantage of self-calibration, the albumin test by our mobile phone can be performed in ambient light without using any extra module integrated with lighting control device. The other advantages are portability, ease of implementation and rapid analysis (3 min) with high precision (RSD ≤ 2.5%) and high accuracy (Recovery = 98.7% ± 1.6). The mobile device was successfully applied to diagnosis of microalbuminuria.
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Diabetic retinopathy image analysis using radial inverse force histograms
(2017-07-02)
Kimpan, Somchok
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Maneerat, Noppadol
;
Kimpan, Chom
This research article discusses the process of increasing the efficiency of image retrieval based on details from the database of the retinal image of diabetic retinopathy patients. The image retrieval uses Radial Inverse Force Histograms which can improve the performance of the image retrieval process in using the details of the retinal image. The value of Radial Inverse Force Histograms can be used to retrieve the similar image. The experimental results indicated that using Radial Inverse Force Histograms can detect the diabetic eyes. Moreover, the image retrieval system is useful in diagnosis the retinal disorders for effectively screen or separate the diabetic retinopathy patients.
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A proposal of global engineering PBL education using by developed sequence control kit
(2017-07-01)
Yajima, Kuniaki
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Nitta, Akihiro
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Takeichi, Yoshihiro
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Maneerat, Noppadol
;
Sato, Jun
In recent years, a learning method called problem-solving learning (PBL), which is expected to acquire 'various facilitation' through learning, attracts attention. In this research, we aim to construct a learning environment that supports 'training human resources with various facilitation skills'. Therefore, we developed sequence control PBL learning kit which can carry out 1 Day PBL with overseas students and evaluate and improve the usefulness of PBL learning kit by actually implementing 1 Day PBL overseas we done.
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Automatic localization of optic disc in fundus image using iterative background removal
(2019-07-01)
Narkthewan, Athasart
;
Maneerat, Noppadol
Diabetic retinopathy (DR) is leading to cause of blood clot stimulating the formation of abnormal new blood vessels. hemorrhage and protein secretion from blood vessels to retinal tissue. The retinal damage causes the loss of sight thus the localization of Optic Disc is necessary for analysis of the abnormal retinal image. However, the data located at the Optic Disc image are similar to Hard Exudates in the retinal image. Therefore, the aim of the present study is to detect Optic Disc using the technique of background removal. The principle of image processing is also applied for the retinal image. The image data from Fundus camera are recorded in RGB color model separated into 3 channels: Red, green and blue channel. The data of all channels are evaluated by the preprocessing algorithm to make a more clear appearance of Optic Disc. Subsequently, the preprocessing image is analyzed by iterative background removal using entropy evaluation of the image data. Finally., the variance analysis of data intensity is performed in both the horizontal and vertical axis to determine the localization and size of Optic Disc. The result shows that the accuracy of localization and size of Optic Disc is approximately 98%. It indicates that this method is useful for retinal image analysis of diabetic retinopathy.

Maneerat, Noppadol

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