A rapid segmentation method of cell boundary for developing embryos using machine learning with a personal computer

2021 ◽  
Author(s):  
Rikifumi Ota ◽  
Takahiro Ide ◽  
Tatsuo Michiue
Keyword(s):  
Machine Learning ◽  
Personal Computer ◽  
Cell Boundary ◽  
Segmentation Method

scholarly journals Smart Home Automation using Hand Gesture Recognition System

2019 ◽  
Vol 9 (2) ◽  
pp. 18-21
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Personal Computer ◽  
Smart Home ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Home Automation ◽  
Proof Of Concept ◽  
Visual Interpretation

Visual interpretation of hand gestures is a natural method of achieving Human-Computer Interaction (HCI). In this paper, we present an approach to setting up of a smart home where the appliances can be controlled by an implementation of a Hand Gesture Recognition System. More specifically, this recognition system uses Transfer learning, which is a technique of Machine Learning, to successfully distinguish between pre-trained gestures and identify them properly to control the appliances. The gestures are sequentially identified as commands which are used to actuate the appliances. The proof of concept is demonstrated by controlling a set of LEDs that represent the appliances, which are connected to an Arduino Uno Microcontroller, which in turn is connected to the personal computer where the actual gesture recognition is implemented

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.

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 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.

scholarly journals Expression Invariant Features for Face Recognition

2019 ◽  
Vol 9 (2) ◽  
pp. 2012-2016
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Facial Expression ◽  
Facial Expressions ◽  
Personal Computer ◽  
The Other ◽  
Machine Learning Methods ◽  
Face Images ◽  
Invariant Features ◽  
The Face

Personal Computer sourced Face Recognition has been a sophisticated and well-found technique which is being rationally utilized for most of the authenticated cases. In reality, there is a number of situations where the expressions of the face will be different. We are here able to instinctively detect the five universal expressions: smile, sadness, anger, surprise, neutral by studying face geometry by determining which type of facial expression has been carried out. Using some facial data with variant expressions. We hereby made some experimentations to calculate the accuracies of some machine learning methods by making some changes in the face images such as a change in expressions, which at last needed for training and recognition identifiers. Our objective is to take the features of neutral facial expressions and add them with the other expressive face images like smiling, angry, sadness to improve the accuracy.

scholarly journals Corn Classification System based on Computer Vision

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 591 ◽  
Author(s):  
Xiaoming Li ◽  
Baisheng Dai ◽  
Hongmin Sun ◽  
Weina Li
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Maximum Likelihood ◽  
Classification System ◽  
Automated Classification ◽  
Shape Features ◽  
Segmentation Method ◽  
Likelihood Estimator ◽  
Color Features

Automated classification of corn is important for corn sorting in intelligent agriculture. This paper presents a reliable corn classification method based on techniques of computer vision and machine learning. To discriminate different damaged types of corns, a line profile segmentation method is firstly used to segment and separate a group of touching corns. Then, twelve color features and five shape features are extracted for each individual corn object. Finally, a maximum likelihood estimator is trained to classify normal and damaged corns. To evaluate the performance of the proposed method, a private dataset consisting of images of normal corn and six kinds of damage corns, including heat-damaged, germ-damaged, cob-rot-damaged, blue eye mold-damaged, insect-damaged, and surface mold-damaged, were collected in this work. The proposed method achieved an accuracy of 96.67% for the classification between normal corns and the first four common damaged corns, and an accuracy of 74.76% was achieved for the classification between normal corns and six kinds of damaged corns. The experimental results demonstrated the effectiveness of the proposed corn classification system.

Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material to Objective Evaluation of Bowel Preparation Quality in Colonoscopy (Preprint)

2021 ◽  
Author(s):  
Yen-Po Wang ◽  
Ying-Chun Jheng ◽  
Kuang-Yi Sung ◽  
Hung-En Lin ◽  
I-Fang Hsin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Automatic Segmentation ◽  
Objective Evaluation ◽  
Manual Segmentation ◽  
Segmentation Method ◽  
Bowel Cleansing ◽  
Fully Automatic ◽  
Colon Preparation ◽  
Overlap Area

BACKGROUND Adequate bowel cleansing is important for a complete examination of the colon mucosa during colonoscopy. Current bowel cleansing evaluation scales are subjective with a wide variation in consistency among physicians and low reported rate. Artificial intelligence (AI) has been increasingly used in endoscopy. OBJECTIVE We aim to use machine learning to develop a fully automatic segmentation method to mark the fecal residue-coated mucosa for objective evaluation of the adequacy of colon preparation. METHODS Colonoscopy videos were retrieved from a video data cohort and transferred to qualified images, which were randomly divided into training, validation and verification datasets. The fecal residue was manually segmented by skilled technicians. Deep learning model based on the U-Net convolutional network architecture was developed to perform automatic segmentation. TheA total of 10,118 qualified images from 119 videos were captured, and labelled manually. The model averaged 0.3634 seconds to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation to 94.7% ± 0.67% with an intersection over union (IOU) of 0.607 ± 0.17. The area predicted by our AI model correlated well with the area measured manually (r=0.915, p<0.001). The AI system can be applied real-time to qualitatively and quantitatively display the mucosa covered by fecal residue. performance of the automatic segmentation was evaluated on the overlap area with the manual segmentation. RESULTS A total of 10,118 qualified images from 119 videos were captured, and labelled manually. The model averaged 0.3634 seconds to segmentate one image automatically. The models produced a strong high-overlap area with manual segmentation to 94.7% ± 0.67% with an intersection over union (IOU) of 0.607 ± 0.17. The area predicted by our AI model correlated well with the area measured manually (r=0.915, p<0.001). The AI system can be applied real-time to qualitatively and quantitatively display the mucosa covered by fecal residue. CONCLUSIONS We used machine learning to establish a fully automatic segmentation method to rapidly and accurately mark the fecal residue-coated mucosa for objective evaluation of colon preparation.

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