scholarly journals Ground-based image analysis: A tutorial on machine-learning techniques and applications

2016 ◽  
Vol 4 (2) ◽  
pp. 79-93 ◽  
Author(s):  
Soumyabrata Dev ◽  
Bihan Wen ◽  
Yee Hui Lee ◽  
Stefan Winkler
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Evaluation of the Risk of Recurrence in Patients with Local Advanced Rectal Tumours by Different Radiomic Analysis Approaches

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Alaa Khadidos ◽  
Adil Khadidos ◽  
Olfat M. Mirza ◽  
Tawfiq Hasanin ◽  
Wegayehu Enbeyle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Locally Advanced ◽  
Textural Analysis ◽  
Machine Learning Techniques ◽  
Response To Treatment ◽  
Learning Techniques ◽  
Potential Applications ◽  
Analysis Models

The word radiomics, like all domains of type omics, assumes the existence of a large amount of data. Using artificial intelligence, in particular, different machine learning techniques, is a necessary step for better data exploitation. Classically, researchers in this field of radiomics have used conventional machine learning techniques (random forest, for example). More recently, deep learning, a subdomain of machine learning, has emerged. Its applications are increasing, and the results obtained so far have demonstrated their remarkable effectiveness. Several previous studies have explored the potential applications of radiomics in colorectal cancer. These potential applications can be grouped into several categories like evaluation of the reproducibility of texture data, prediction of response to treatment, prediction of the occurrence of metastases, and prediction of survival. Few studies, however, have explored the potential of radiomics in predicting recurrence-free survival. In this study, we evaluated and compared six conventional learning models and a deep learning model, based on MRI textural analysis of patients with locally advanced rectal tumours, correlated with the risk of recidivism; in traditional learning, we compared 2D image analysis models vs. 3D image analysis models, models based on a textural analysis of the tumour versus models taking into account the peritumoural environment in addition to the tumour itself. In deep learning, we built a 16-layer convolutional neural network model, driven by a 2D MRI image database comprising both the native images and the bounding box corresponding to each image.

An Overview of Machine Learning in Medical Image Analysis

2017 ◽  
pp. 36-58 ◽  
Author(s):  
Anand Narasimhamurthy
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Medical Imaging ◽  
Health Informatics ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The Common ◽  
Applications Of Machine Learning

Medical image analysis is an area which has witnessed an increased use of machine learning in recent times. In this chapter, the authors attempt to provide an overview of applications of machine learning techniques to medical imaging problems, focusing on some of the recent work. The target audience comprises of practitioners, engineers, students and researchers working on medical image analysis, no prior knowledge of machine learning is assumed. Although the stress is mostly on medical imaging problems, applications of machine learning to other proximal areas will also be elucidated briefly. Health informatics is a relatively new area which deals with mining large amounts of data to gain useful insights. Some of the common challenges in health informatics will be briefly touched upon and some of the efforts in related directions will be outlined.

A methodology for automated CPA extraction using liver biopsy image analysis and machine learning techniques

2017 ◽  
Vol 140 ◽  
pp. 61-68 ◽  
Author(s):  
Markos G. Tsipouras ◽  
Nikolaos Giannakeas ◽  
Alexandros T. Tzallas ◽  
Zoe E. Tsianou ◽  
Pinelopi Manousou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Liver Biopsy ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Biopsy Image

scholarly journals Genetic programming for region detection, feature extraction, feature construction and classification in image data

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
Bing Xue
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Genetic Programming ◽  
Image Classification ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Efficient Manner ◽  
Speeded Up Robust Features ◽  
Learning Techniques ◽  
High Level

© Springer International Publishing Switzerland 2016. Image analysis is a key area in the computer vision domain that has many applications. Genetic Programming (GP) has been successfully applied to this area extensively, with promising results. Highlevel features extracted from methods such as Speeded Up Robust Features (SURF) and Histogram of Oriented Gradients (HoG) are commonly used for object detection with machine learning techniques. However, GP techniques are not often used with these methods, despite being applied extensively to image analysis problems. Combining the training process of GP with the powerful features extracted by SURF or HoG has the potential to improve the performance by generating high-level, domain-tailored features. This paper proposes a new GP method that automatically detects different regions of an image, extracts HoG features from those regions, and simultaneously evolves a classifier for image classification. By extending an existing GP region selection approach to incorporate the HoG algorithm, we present a novel way of using high-level features with GP for image classification. The ability of GP to explore a large search space in an efficient manner allows all stages of the new method to be optimised simultaneously, unlike in existing approaches. The new approach is applied across a range of datasets, with promising results when compared to a variety of well-known machine learning techniques. Some high-performing GP individuals are analysed to give insight into how GP can effectively be used with high-level features for image classification.

scholarly journals Machine Learning Techniques used for the Histopathological Image Analysis of Oral Cancer-A Review

2020 ◽  
Vol 13 (1) ◽  
pp. 106-118
Author(s):  
Santisudha Panigrahi ◽  
Tripti Swarnkar
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Oral Cancer ◽  
Oral Disease ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Histopathological Image Analysis

Oral diseases are the 6th most revealed malignancy happening in head and neck regions found mainly in south Asian countries. It is the most common cancer with fourteen deaths in an hour on a yearly basis, as per the WHO oral cancer incidence in India. Due to the cost of tests, mistakes in the recognition procedure, and the enormous remaining task at hand of the cytopathologist, oral growths cannot be diagnosed promptly. This area is open to be looked into by biomedical analysts to identify it at an early stage. At present, with the advent of entire slide computerized scanners and tissue histopathology, there is a gigantic aggregation of advanced digital histopathological images, which has prompted the necessity for their analysis. A lot of computer aided analysis techniques have been developed by utilizing machine learning strategies for prediction and prognosis of cancer. In this review paper, first various steps of obtaining histopathological images, followed by the visualization and classification done by the doctors are discussed. As machine learning techniques are well known, in the second part of this review, the works done for histopathological image analysis as well as other oral datasets using these strategies for growth prognosis and anticipation are discussed. Comparing the pitfalls of machine learning and how it has overcome by deep learning mostly for image recognition tasks are also discussed subsequently. The third part of the manuscript describes how deep learning is beneficial and widely used in different cancer domains. Due to the remarkable growth of deep learning and wide applicability, it is best suited for the prognosis of oral disease. The aim of this review is to provide insight to the researchers opting to work for oral cancer by implementing deep learning and artificial neural networks.

Magnetic Resonance Image Analysis for Brain CAD Systems with Machine Learning

2012 ◽  
pp. 258-296 ◽  
Author(s):  
Hidetaka Arimura ◽  
Chiaki Tokunaga ◽  
Yasuo Yamashita ◽  
Jumpei Kuwazuru
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Magnetic Resonance ◽  
Image Feature ◽  
Machine Learning Techniques ◽  
Brain Diseases ◽  
Cad Systems ◽  
Unruptured Aneurysms ◽  
Development Cycle ◽  
Learning Techniques

This chapter describes the image analysis for brain Computer-Aided Diagnosis (CAD) systems with machine learning techniques, which could assist radiologists in the detection of such brain diseases as asymptomatic unruptured aneurysms, Alzheimer’s Disease (AD), vascular dementia, and Multiple Sclerosis (MS) by magnetic resonance imaging. Image analysis in CAD systems consists of image enhancement, initial detection, and image feature extraction, including segmentation. In addition, the authors review the classification of true and false positives using machine learning techniques, as well as the evaluation methods and development cycle for CAD systems.

scholarly journals Genetic programming for region detection, feature extraction, feature construction and classification in image data

2020 ◽  
Author(s):  
Andrew Lensen ◽  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
Bing Xue
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Genetic Programming ◽  
Image Classification ◽  
Search Space ◽  
Machine Learning Techniques ◽  
Efficient Manner ◽  
Speeded Up Robust Features ◽  
Learning Techniques ◽  
High Level

© Springer International Publishing Switzerland 2016. Image analysis is a key area in the computer vision domain that has many applications. Genetic Programming (GP) has been successfully applied to this area extensively, with promising results. Highlevel features extracted from methods such as Speeded Up Robust Features (SURF) and Histogram of Oriented Gradients (HoG) are commonly used for object detection with machine learning techniques. However, GP techniques are not often used with these methods, despite being applied extensively to image analysis problems. Combining the training process of GP with the powerful features extracted by SURF or HoG has the potential to improve the performance by generating high-level, domain-tailored features. This paper proposes a new GP method that automatically detects different regions of an image, extracts HoG features from those regions, and simultaneously evolves a classifier for image classification. By extending an existing GP region selection approach to incorporate the HoG algorithm, we present a novel way of using high-level features with GP for image classification. The ability of GP to explore a large search space in an efficient manner allows all stages of the new method to be optimised simultaneously, unlike in existing approaches. The new approach is applied across a range of datasets, with promising results when compared to a variety of well-known machine learning techniques. Some high-performing GP individuals are analysed to give insight into how GP can effectively be used with high-level features for image classification.

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