Delineation of Blood Vessels in Coronary Artery Region for Classification of Different Types of Plaques

2020 ◽  
Vol 10 (7) ◽  
pp. 901-914
Author(s):  
D. Indumathy ◽  
S. Sudha
Keyword(s):  
Cardiovascular Diseases ◽  
Blood Vessel ◽  
Heart Defects ◽  
Image Data ◽  
Data Set ◽  
Different Types ◽  
Cardiac Muscles ◽  
Pixel Analysis ◽  
Effective Medication

Cardiac arrest in human arises owing to blood vessel diseases or heart defects. Blood vessel diseases result due to the blockage of blood in the heart vessels, which leads to pain in the heart. Heart defects occur because of damage in the cardiac muscles indicated by abnormal heart rhythms. Cardiovascular diseases cause mortality which could be avoided through the earlier detection of cardiovascular diseases. The major cause for cardiovascular diseases is cholesterol deposition inside the artery walls which later forms plaques that block the blood flow. Until now, plaques have been detected through medical imaging only after the heart attack. The plaques are blasted through angioplasty or reduced with medicine. Classification of the plaques before treatment, leads to effective medication based on the type of plaque. The sub classification of the plaque types such as rupture-prone plaque, ruptured plaque with sub occlusive thrombus, erosion-prone plaque, calcified nodule and non-plaque has been segmented and identified. In this paper, we propose a novel Spatial Fuzzy Propensity Score Matching (SFPSM) method to classify the plaques. The SFPSM method consists of clustering, ranking the cluster and region-based pixel wise analysis. Pixel analysis inspects specific regions of sub pixel points and calibrates the plaque. From the experimental results, the classification of plaque based on the 50-image data set has exhibited accuracy of 85% after validation. The plaque accuracy of classification provides the standard digital number values for the sub classification of plaques.

EFFICIENT CLASSIFICATION OF SCANNED MEDIA USING SPATIAL STATISTICS

2010 ◽  
Vol 24 (06) ◽  
pp. 917-946
Author(s):  
GOZDE UNAL ◽  
GAURAV SHARMA ◽  
REINER ESCHBACH
Keyword(s):  
Spatial Statistics ◽  
High Reliability ◽  
Image Data ◽  
Training Data ◽  
Automated Classification ◽  
Data Set ◽  
Statistical Measures ◽  
Scanned Image ◽  
Scanned Images

Photography, lithography, xerography, and inkjet printing are the dominant technologies for color printing. Images produced on these "different media" are often scanned either for the purpose of copying or creating an electronic representation. For an improved color calibration during scanning, a media identification from the scanned image data is desirable. In this paper, we propose an efficient algorithm for automated classification of input media into four major classes corresponding to photographic, lithographic, xerographic and inkjet. Our technique exploits the strong correlation between the type of input media and the spatial statistics of corresponding images, which are observed in the scanned images. We adopt ideas from spatial statistics literature, and design two spatial statistical measures of dispersion and periodicity, which are computed over spatial point patterns generated from blocks of the scanned image, and whose distributions provide the features for making a decision. We utilize extensive training data and determined well separated decision regions to classify the input media. We validate and tested our classification technique results over an independent extensive data set. The results demonstrate that the proposed method is able to distinguish between the different media with high reliability.

scholarly journals Efficient Image Mining Technique for Classification of Mammograms to Detect Breast Cancer

2012 ◽  
pp. 44-51
Author(s):  
Aswini Kumar Mohanty ◽  
Saroj Kumar Lenka
Keyword(s):  
Hybrid Approach ◽  
Image Mining ◽  
Data Set ◽  
Image Domain ◽  
Mining Technique ◽  
Different Types ◽  
Mammogram Image ◽  
Detect Breast Cancer ◽  
Mammogram Images

The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign and malignant class. Total of 24 features including histogram intensity features and GLCM features are extracted from mammogram images. A hybrid approach of feature selection is proposed which approximately reduces 75% of the features and new decision tree is used for classification. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum no. of rules to cover more patterns.

Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Patrick Beyersdorffer ◽  
Wolfgang Kunert ◽  
Kai Jansen ◽  
Johanna Miller ◽  
Peter Wilhelm ◽  
...  
Keyword(s):  
Neural Networks ◽  
Adverse Events ◽  
Convolutional Neural Networks ◽  
Surgical Training ◽  
Binary Classification ◽  
Image Data ◽  
Training Image ◽  
Training Environment ◽  
Data Set

Abstract Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train the CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labeled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.

scholarly journals Autonomous Interpretation of the Microstructure of Steels and Special Alloys

2019 ◽  
Vol 949 ◽  
pp. 24-31 ◽  
Author(s):  
Bartłomiej Mulewicz ◽  
Grzegorz Korpala ◽  
Jan Kusiak ◽  
Ulrich Prahl
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Classification Systems ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Steel Microstructure ◽  
Different Types ◽  
Steel Grades

The main objective of presented research is an attempt of application of techniques taken from a dynamically developing field of image analysis based on Artificial Intelligence, particularly on Deep Learning, in classification of steel microstructures. Our research focused on developing and implementation of Deep Convolutional Neural Networks (DCNN) for classification of different types of steel microstructure photographs received from the light microscopy at the TU Bergakademie, Freiberg. First, brief presentation of the idea of the system based on DCNN is given. Next, the results of tests of developed classification system on 8 different types (classes) of microstructure of the following different steel grades: C15, C45, C60, C80, V33, X70 and carbide free steel. The DCNN based classification systems require numerous training data and the system accuracy strongly depend on the size of these data. Therefore, created data set of numerous micrograph images of different types of microstructure (33283 photographs) gave the opportunity to develop high precision classification systems and segmentation routines, reaching the accuracy of 99.8%. Presented results confirm, that DCNN can be a useful tool in microstructure classification.

Approaches for Assessment of Non-Determinism in Different Stages of Engineering Product Design Using Finite Element Analysis

2011 ◽  
Vol 104 ◽  
pp. 23-32 ◽  
Author(s):  
Dirk Vandepitte ◽  
David Moens
Keyword(s):  
Scientific Literature ◽  
Technical Problem ◽  
Probabilistic Methods ◽  
Element Analysis ◽  
Engineering Product ◽  
Data Set ◽  
Development Cycle ◽  
Different Types ◽  
Realistic Data

Development of a complicated technical problem encompasses different successive decisions that are based on techical analysis and engineering judgment. In each of these steps, somedegree of non-determinism is inevitable. The paper discusses different types of non-deterministic parameters that may be relevant in different stages of engineering analysis. The entire cycle of development of a product is considered, and it is shown that the relevance of methods isdifferent in different stages of the development cycle. A classification of different types of non-deterministic properties is presented. Based on the nature of these different classes of model properties, it is discussed to what degree each of these fits in the framework of either a probabilistic or a non-probabilistic concept.The availability of realistic data in an appropriate format is another issue that should be taken into account. A validated probabilistic representation is usually only possible after an extensive campaign of data acquisition has been conducted, or at least after suffcient data have been collected to allow for a reliable estimation of a statistical model. A study of scientific literature shows that validated information is not always available. A general conclusion is that probabilistic methods are applicable in later stages of development, when a suffciently large database of product data has been gathered. Probabilistic approaches are perfectly suited for conditions when the product is already in service. Possibilistic analysis on the other hand is best suited for application in cases when the data set about the product at hand is still incomplete.

Optimal quantile level selection for disease classification and biomarker discovery with application to electrocardiogram data

2017 ◽  
Vol 27 (11) ◽  
pp. 3340-3349
Author(s):  
Yingchun Zhou ◽  
Rong Huang ◽  
Shanshan Yu ◽  
Yanyuan Ma
Keyword(s):  
Cardiovascular Diseases ◽  
Medical Research ◽  
Biomarker Discovery ◽  
Selection Procedure ◽  
Real Data ◽  
Disease Classification ◽  
Intensive Study ◽  
Data Set ◽  
Selection For

Classification with a large number of predictors and biomarker discovery become increasingly important in biological and medical research. This paper focuses on performing classification of cardiovascular diseases based on electrocardiogram analysis which deals with many variables and a lot of measurements within variables. We propose an optimal quantile level selection procedure to reduce dimension by characterizing distributions with quantiles and combine with classification tools to produce sensible classification and biomarker discovery results. Simulation and an intensive study of a real data set are performed to illustrate the performance of the proposed method.

scholarly journals Review of Internet of Wearable Things and Healthcare based Computational Devices

2021 ◽  
Vol 3 (3) ◽  
pp. 263-275
Author(s):  
Joy Iong-Zong Chen
Keyword(s):  
Drug Delivery ◽  
Health Care ◽  
Cardiovascular Diseases ◽  
Drug Delivery Systems ◽  
Wearable Computing ◽  
Future Perspective ◽  
Computing Systems ◽  
Muscle Disorders ◽  
Different Types

Wearable computing have variety of applications in healthcare ranging from muscle disorders to neurocognitive disorders, Alzheimer’s disease, Parkinson’s disease, and psychological diseases, such as cardiovascular diseases, hypertension and so on. Different types of wearable computing devices are used, for example, bio fluidic-place on wearables, textile-place on wearables, and skin-place on wearables including tattoo place on wearables. In drug delivery systems, the wearable computing systems have shown promising developments, increasing its use in personalized healthcare. Wearable contain experiments, which need to be addressed before their consumerist as a fully customized healthcare system. Distinct types of wearable computing devices currently used in healthcare field are reviewed in this paper. Based on various factors, the paper provides an extensive classification of wearable computing devices. Additionally, limitations, current challenges and future perspective in health care is reviewed.

scholarly journals Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243243
Author(s):  
Asim Khan ◽  
Umair Nawaz ◽  
Anwaar Ulhaq ◽  
Randall W. Robinson
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Image Data ◽  
Fruit Trees ◽  
Disease Diagnosis ◽  
Economic Losses ◽  
Plant Leaves ◽  
Data Set ◽  
Plant Leaf

The control of plant leaf diseases is crucial as it affects the quality and production of plant species with an effect on the economy of any country. Automated identification and classification of plant leaf diseases is, therefore, essential for the reduction of economic losses and the conservation of specific species. Various Machine Learning (ML) models have previously been proposed to detect and identify plant leaf disease; however, they lack usability due to hardware sophistication, limited scalability and realistic use inefficiency. By implementing automatic detection and classification of leaf diseases in fruit trees (apple, grape, peach and strawberry) and vegetable plants (potato and tomato) through scalable transfer learning on Amazon Web Services (AWS) SageMaker and importing it into AWS DeepLens for real-time functional usability, our proposed DeepLens Classification and Detection Model (DCDM) addresses such limitations. Scalability and ubiquitous access to our approach is provided by cloud integration. Our experiments on an extensive image data set of healthy and unhealthy fruit trees and vegetable plant leaves showed 98.78% accuracy with a real-time diagnosis of diseases of plant leaves. To train DCDM deep learning model, we used forty thousand images and then evaluated it on ten thousand images. It takes an average of 0.349s to test an image for disease diagnosis and classification using AWS DeepLens, providing the consumer with disease information in less than a second.

Dual stain kit for the microscopic gram classification of ocular infection bacteria

1993 ◽  
Vol 51 ◽  
pp. 248-249
Author(s):  
Jacob S. Hanker ◽  
Dale N. Holdren ◽  
Kenneth L. Cohen ◽  
Beverly L. Giammara
Keyword(s):  
Contact Lenses ◽  
Ocular Infection ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Gram Staining ◽  
Ocular Infections ◽  
Different Types ◽  
Culture Positive ◽  
Increased Susceptibility

Keratitis and conjunctivitis (infections of the cornea or conjunctiva) are ocular infections caused by various bacteria, fungi, viruses or parasites; bacteria, however, are usually prominent. Systemic conditions such as alcoholism, diabetes, debilitating disease, AIDS and immunosuppressive therapy can lead to increased susceptibility but trauma and contact lens use are very important factors. Gram-negative bacteria are most frequently cultured in these situations and Pseudomonas aeruginosa is most usually isolated from culture-positive ulcers of patients using contact lenses. Smears for staining can be obtained with a special swab or spatula and Gram staining frequently guides choice of a therapeutic rinse prior to the report of the culture results upon which specific antibiotic therapy is based. In some cases staining of the direct smear may be diagnostic in situations where the culture will not grow. In these cases different types of stains occasionally assist in guiding therapy.

Explanatory Power for Medical Expert Systems: Studies in the Representation of Causal Relationships for Clinical Consultations

1982 ◽  
Vol 21 (03) ◽  
pp. 127-136 ◽  
Author(s):  
J. W. Wallis ◽  
E. H. Shortliffe
Keyword(s):  
Explanatory Power ◽  
Prototype System ◽  
Inference Rules ◽  
Medical Expert ◽  
Comprehensive Knowledge ◽  
Different Types ◽  
Medical Expert Systems ◽  
Explanation System ◽  
Systems Studies

This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user.

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