PM-04Contour detection and segmentation method applicable to Electron tomography images with auto-classification by machine learning

Microscopy ◽  
2016 ◽  
Vol 65 (suppl 1) ◽  
pp. i33.2-i33
Author(s):  
Gen Maeda ◽  
Shoki Tezuka ◽  
Shohei Sakamoto ◽  
Misuzu Baba ◽  
Norio Baba
Keyword(s):  
Machine Learning ◽  
Electron Tomography ◽  
Segmentation Method

scholarly journals Statistical Machine Learning and Compressed Sensing Approaches for Analytical Electron Tomography - Application to Phase Change Materials

2019 ◽  
Vol 25 (S2) ◽  
pp. 156-157 ◽  
Author(s):  
Martin Jacob ◽  
Loubna El Gueddari ◽  
Gabriele Navarro ◽  
Marie-Claire Cyrille ◽  
Pascale Bayle-Guillemaud ◽  
...  
Keyword(s):  
Machine Learning ◽  
Compressed Sensing ◽  
Phase Change ◽  
Phase Change Materials ◽  
Electron Tomography ◽  
Statistical Machine Learning ◽  
Analytical Electron

Intelligent system for predicting breast tumors using machine learning

2020 ◽  
Vol 39 (4) ◽  
pp. 4813-4822
Author(s):  
Meifang Li ◽  
Binlin Ruan ◽  
Caixing Yuan ◽  
Zhishuang Song ◽  
Chongchong Dai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Breast Tumor ◽  
Intelligent System ◽  
Breast Tumors ◽  
Detection Accuracy ◽  
Segmentation Method ◽  
X Ray ◽  
Image Diagnosis ◽  
Mammogram Image ◽  
Aided Diagnosis

The early hidden characteristics of breast tumors make their features difficult to be effectively identified. In order to improve the detection accuracy of breast tumors, this study combined with computer-aided diagnosis techniques such as machine learning and computer vision and used X-ray analysis to study breast tumor diagnosis techniques. Moreover, this study combines breast tumor diagnostic images to determine various parameters of the image. At the same time, through experimental research and analysis of the region segmentation method and preprocessing method of breast detection images, the best diagnostic images are obtained, and the influence of background and other noise on the image diagnosis results is effectively proposed. In addition, this study proposes a method for detecting the distortion of the mammogram image structure, which accurately detects the structural distortion and reduces the interference of various influencing factors. Finally, this paper designs experiments to study the effects of the diagnostic method of this paper. Through comparative analysis, it can be seen that the results of this study have certain advantages in accuracy and image clarity, and have certain clinical significance, and can provide theoretical reference for subsequent related research.

scholarly journals Proposal of Unsupervised Defect Segmentation Method for Patterned Textiles Based on Machine Learning

2020 ◽  
Vol 66 (3) ◽  
pp. 47-54
Author(s):  
Motoshi HONDA ◽  
Satoru HIROSAWA ◽  
Mitsuru MIMURA ◽  
Tadashi HAYAMI ◽  
Saori KITAGUCHI ◽  
...  
Keyword(s):  
Machine Learning ◽  
Segmentation Method

scholarly journals Droplets Image Segmentation Method Based on Machine learning and Watershed

CONVERTER ◽  
2021 ◽  
pp. 219-227
Author(s):  
He Li, Et al.
Keyword(s):  
Machine Learning ◽  
Watershed Algorithm ◽  
Test Paper ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Pesticide Application ◽  
Spray Droplet ◽  
Iterative Processing ◽  
Segmentation Accuracy ◽  
Seed Points

Watershed algorithm is used widely in segmentation of droplet overlapped spots on water-sensitive test paper. However, the phenomenon of over-segmentation, however, is often caused by noise and subtle changes of gray levels in images. To further improve segmentation accuracy of watershed algorithm, this paper proposes a cyclic iterative watershed segmentation algorithm. Through statistical analysis and logistic regression, machine learning models were classified to extract overlapping droplets on test papers. Loop iterative processing of seed points segments overlapping droplets with appropriate thresholds. Compared with fixed threshold watershed segmentation, this method has higher precision and efficiency for spray droplet evaluation in pesticide application.

scholarly journals A novel cell segmentation method for developing embryos using machine learning

2018 ◽  
Author(s):  
Rikifumi Ota ◽  
Takahiro Ide ◽  
Tatsuo Michiue
Keyword(s):  
Machine Learning ◽  
Cell Shape ◽  
State Of The Art ◽  
Cell Segmentation ◽  
Segmentation Method ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Segmented Image ◽  
Novel Method ◽  
Better Than

AbstractCell segmentation is crucial in the study of morphogenesis in developing embryos, but it is limited in its accuracy. In this study we provide a novel method for cell segmentation using machine-learning, termed Cell Segmenter using Machine Learning (CSML). CSML performed better than state-of-the-art methods, such as RACE and watershed, in the segmentation of ectodermal cells in the Xenopus embryo. CSML required only one whole embryo image for training a Fully Convolutional Network classifier, and it took 20 seconds per each image to return a segmented image. To validate its accuracy, we compared it to other methods in assessing several indicators of cell shape. We also examined the generality by measuring its performance in segmenting independent images. Our data demonstrates the superiority of CSML, and we expect this application to significantly improve efficiency in cell shape studies.

scholarly journals Discrimination of indoor versus outdoor environmental state with machine learning algorithms in myopia observational studies

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Bin Ye ◽  
Kangping Liu ◽  
Siting Cao ◽  
Padmaja Sankaridurg ◽  
Wayne Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Real Time ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Segmentation Method ◽  
Time Data ◽  
Threshold Segmentation ◽  
Svm Model ◽  
Real Time Data

Abstract Background Wearable smart watches provide large amount of real-time data on the environmental state of the users and are useful to determine risk factors for onset and progression of myopia. We aim to evaluate the efficacy of machine learning algorithm in differentiating indoor and outdoor locations as collected by use of smart watches. Methods Real time data on luminance, ultraviolet light levels and number of steps obtained with smart watches from dataset A: 12 adults from 8 scenes and manually recorded true locations. 70% of data was considered training set and support vector machine (SVM) algorithm generated using the variables to create a classification system. Data collected manually by the adults was the reference. The algorithm was used for predicting the location of the remaining 30% of dataset A. Accuracy was defined as the number of correct predictions divided by all. Similarly, data was corrected from dataset B: 172 children from 3 schools and 12 supervisors recorded true locations. Data collected by the supervisors was the reference. SVM model trained from dataset A was used to predict the location of dataset B for validation. Finally, we predicted the location of dataset B using the SVM model self-trained from dataset B. We repeated these three predictions with traditional univariate threshold segmentation method. Results In both datasets, SVM outperformed the univariate threshold segmentation method. In dataset A, the accuracy and AUC of SVM were 99.55% and 0.99 as compared to 95.11% and 0.95 with the univariate threshold segmentation (p < 0.01). In validation, the accuracy and AUC of SVM were 82.67% and 0.90 compared to 80.88% and 0.85 with the univariate threshold segmentation method (p < 0.01). In dataset B, the accuracy and AUC of SVM and AUC were 92.43% and 0.96 compared to 80.88% and 0.85 with the univariate threshold segmentation (p < 0.01). Conclusions Machine learning algorithm allows for discrimination of outdoor versus indoor environments with high accuracy and provides an opportunity to study and determine the role of environmental risk factors in onset and progression of myopia. The accuracy of machine learning algorithm could be improved if the model is trained with the dataset itself.

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