Image Processing Including Medical Liver Imaging

2018 ◽

pp. 380-392

Author(s):

Suganya Ramamoorthy ◽

Rajaram Sivasubramaniam

Keyword(s):

Image Processing ◽

Medical Diagnosis ◽

Small Variation ◽

Classification Problem ◽

Continuous Treatment ◽

Automated System ◽

Ct Scans ◽

Liver Imaging ◽

Analysis Process ◽

Disease Identification

Medical diagnosis has been gaining importance in everyday life. The diseases and their symptoms are highly varying and there is always a need for a continuous update of knowledge needed for the doctors. The diseases fall into different categories and a small variation of symptoms may leave to different categories of diseases. This is further supplemented by the medical analysts for a continuous treatment process. The treatment generally starts with a diagnosis and further goes through a set of procedures including X-ray, CT-scans, ultrasound imaging for qualitative analysis and diagnosis by doctors. A small level of error in disease identification introduces overhead in diagnosis and difficult in treatment. In such cases, an automated system that could retrieve medical images based on user's interest. This chapter deals with various techniques, methodologies that correspond to the classification problem in data analysis process and its methodological impacts to big data.

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Disease Identification and Grading of Pomegranate Leaves Using Image Processing and Fuzzy Logic

International Journal of Food Engineering ◽

10.1515/ijfe-2012-0241 ◽

2013 ◽

Vol 9 (4) ◽

pp. 467-479 ◽

Cited By ~ 3

Author(s):

Sanjeev S. Sannakki ◽

Vijay S. Rajpurohit ◽

V. B. Nargund ◽

R. Arunkumar

Keyword(s):

Image Processing ◽

Plant Diseases ◽

Automated System ◽

Database Analysis ◽

Query Image ◽

Horticultural Crops ◽

Disease Identification ◽

Proper Diagnosis ◽

Processing Steps ◽

Leaf Image

AbstractPlant diseases cause major losses to several agricultural and horticultural crops around the World. Therefore, methods for proper diagnosis of diseases found in any parts of the plant body play a crucial role in disease management. In the past few decades, many methods and techniques of image processing and soft computing are applied on a number of plants to diagnose and treat variety of plant diseases. Hence, the present work is aimed to develop an automated system that results in three major outcomes for a leaf image. They are disease identification, disease grading and treatment advisory. The methodology begins with capturing of samples of healthy and diseased leaf images of Pomegranate plant. All the images are made to undergo pre-processing steps and different features are extracted and stored in the database. Analysis is done on the extracted features to determine those features that constitute a disease in the leaf. Later, a query image is taken and is tested to determine whether that image is healthy or diseased one. If the query image is found to be diseased, then the grade of the disease is determined. Finally, a treatment advisory module is built which ultimately helps agriculturists/farmers.

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Ct-Caps: Feature Extraction-Based Automated Framework for Covid-19 Disease Identification From Chest Ct Scans Using Capsule Networks

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp39728.2021.9414214 ◽

2021 ◽

Author(s):

Shahin Heidarian ◽

Parnian Afshar ◽

Arash Mohammadi ◽

Moezedin Javad Rafiee MD ◽

Anastasia Oikonomou MD ◽

...

Keyword(s):

Feature Extraction ◽

Chest Ct ◽

Ct Scans ◽

Disease Identification

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Small flexible automated system for monitoring Caenorhabditis elegans lifespan based on active vision and image processing techniques

Scientific Reports ◽

10.1038/s41598-021-91898-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Joan Carles Puchalt ◽

Antonio-José Sánchez-Salmerón ◽

Eugenio Ivorra ◽

Silvia Llopis ◽

Roberto Martínez ◽

...

Keyword(s):

Image Processing ◽

Caenorhabditis Elegans ◽

Culture Media ◽

Active Vision ◽

Automated System ◽

Rank Test ◽

P Value ◽

Vision Systems ◽

C Elegans ◽

Processing Techniques

AbstractTraditionally Caenorhabditis elegans lifespan assays are performed by manually inspecting nematodes with a dissection microscope, which involves daily counting of live/dead worms cultured in Petri plates for 21–25 days. This manual inspection requires the screening of hundreds of worms to ensure statistical robustness, and is therefore a time-consuming approach. In recent years, various automated artificial vision systems have been reported to increase the throughput, however they usually provide less accurate results than manual assays. The main problems identified when using these vision systems are the false positives and false negatives, which occur due to culture media changes, occluded zones, dirtiness or condensation of the Petri plates. In this work, we developed and described a new C. elegans monitoring machine, SiViS, which consists of a flexible and compact platform design to analyse C. elegans cultures using the standard Petri plates seeded with E. coli. Our system uses an active vision illumination technique and different image-processing pipelines for motion detection, both previously reported, providing a fully automated image processing pipeline. In addition, this study validated both these methods and the feasibility of the SiViS machine for lifespan experiments by comparing them with manual lifespan assays. Results demonstrated that the automated system yields consistent replicates (p-value log rank test 0.699), and there are no significant differences between automated system assays and traditionally manual assays (p-value 0.637). Finally, although we have focused on the use of SiViS in longevity assays, the system configuration is flexible and can, thus, be adapted to other C. elegans studies such as toxicity, mobility and behaviour.

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Automated system for acquisition and image processing for the control and monitoring boned nopal

10.1117/12.2016743 ◽

2013 ◽

Author(s):

E. Luevano ◽

E. de Posada ◽

M. Arronte ◽

L. Ponce ◽

T. Flores

Keyword(s):

Image Processing ◽

Automated System

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UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2017.v10i4.17023 ◽

2017 ◽

Vol 10 (4) ◽

pp. 32

Author(s):

Srinivasan A ◽

Sudha S

Keyword(s):

Image Processing ◽

Fuzzy Logic ◽

Diabetic Retinopathy ◽

Characteristic Curve ◽

Area Under The Curve ◽

Computation Time ◽

Automated System ◽

Morphological Operations ◽

Advanced Stage ◽

Sensitivity Specificity

One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic.

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Lung Cancer Detection in CT Scans of Patients Using Image Processing and Machine Learning Technique

Lecture Notes in Electrical Engineering - Advanced Computational and Communication Paradigms ◽

10.1007/978-981-10-8240-5_37 ◽

2018 ◽

pp. 336-344 ◽

Cited By ~ 1

Author(s):

Karan Sharma ◽

Harshil Soni ◽

Kushika Agarwal

Keyword(s):

Machine Learning ◽

Lung Cancer ◽

Image Processing ◽

Cancer Detection ◽

Ct Scans ◽

Machine Learning Technique ◽

Learning Technique ◽

Lung Cancer Detection

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Artificial Intelligence in Medical Science

Medical Diagnosis Using Artificial Neural Networks ◽

10.4018/978-1-4666-6146-2.ch002 ◽

2014 ◽

pp. 11-23 ◽

Cited By ~ 1

Keyword(s):

Artificial Intelligence ◽

Image Processing ◽

Decision Making ◽

Signal Processing ◽

Expert System ◽

Knowledge Base ◽

Medical Diagnosis ◽

Medical Science ◽

Inference Engine ◽

Medical Decision

Applying Artificial Intelligence (AI) for increasing the reliability of medical decision making has been studied for some years, and many researchers have studied in this area. In this chapter, AI is defined and the reason of its importance in medical diagnosis is explained. Various applications of AI in medical diagnosis such as signal processing and image processing are provided. Expert system is defined and it is mentioned that the basic components of an expert system are a “knowledge base” or KB and an “inference engine”. The information in the KB is obtained by interviewing people who are experts in the area in question.

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Detection of Blood-Related Diseases Using Deep Neural Nets

Handbook of Research on Deep Learning Innovations and Trends - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-7862-8.ch001 ◽

2019 ◽

pp. 1-15

Author(s):

Rajithkumar B. K. ◽

Shilpa D. R. ◽

Uma B. V.

Keyword(s):

Image Processing ◽

Biomedical Engineering ◽

Medical Diagnosis ◽

Blood Smear ◽

Neural Nets ◽

Laboratory Methods ◽

Intelligent Algorithms ◽

Image Processing Techniques ◽

Processing Techniques ◽

Traditional Image

Image processing offers medical diagnosis and it overcomes the shortcomings faced by traditional laboratory methods with the help of intelligent algorithms. It is also useful for remote quality control and consultations. As machine learning is stepping into biomedical engineering, there is a huge demand for devices which are intelligent and accurate enough to target the diseases. The platelet count in a blood sample can be done by extrapolating the number of platelets counted in the blood smear. Deep neural nets use multiple layers of filtering and automated feature extraction and detection and can overcome the hurdle of devising complex algorithms to extract features for each type of disease. So, this chapter deals with the usage of deep neural networks for the image classification and platelets count. The method of using deep neural nets has increased the accuracy of detecting the disease and greater efficiency compared to traditional image processing techniques. The method can be further expanded to other forms of diseases which can be detected through blood samples.

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