180 research outputs found
Detection of lung pathology using the fractal method
Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis.
Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases
Lung diseases are one of the major causes of suffering and death in the world. Improved
survival rate could be obtained if the diseases can be detected at its early stage. Specialist
doctors with the expertise and experience to interpret medical images and diagnose
complex lung diseases are scarce. In this work, a rule-based expert system with an
embedded imaging module is developed to assist the general physicians in hospitals and
clinics to diagnose lung diseases whenever the services of specialist doctors are not
available. The rule-based expert system contains a large knowledge base of data from
various categories such as patient's personal and medical history, clinical symptoms,
clinical test results and radiological information. An imaging module is integrated into
the expert system for the enhancement of chest X-Ray images. The goal of this module is
to enhance the chest X-Ray images so that it can provide details similar to more
expensive methods such as MRl and CT scan. A new algorithm which is a modified
morphological grayscale top hat transform is introduced to increase the visibility of lung
nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of
malignancy of the nodules. The output generated by the expert system was compared
with the diagnosis made by the specialist doctors. The system is able to produce results\ud
which are similar to the diagnosis made by the doctors and is acceptable by clinical
standards
Radiological images and machine learning: trends, perspectives, and prospects
The application of machine learning to radiological images is an increasingly
active research area that is expected to grow in the next five to ten years.
Recent advances in machine learning have the potential to recognize and
classify complex patterns from different radiological imaging modalities such
as x-rays, computed tomography, magnetic resonance imaging and positron
emission tomography imaging. In many applications, machine learning based
systems have shown comparable performance to human decision-making. The
applications of machine learning are the key ingredients of future clinical
decision making and monitoring systems. This review covers the fundamental
concepts behind various machine learning techniques and their applications in
several radiological imaging areas, such as medical image segmentation, brain
function studies and neurological disease diagnosis, as well as computer-aided
systems, image registration, and content-based image retrieval systems.
Synchronistically, we will briefly discuss current challenges and future
directions regarding the application of machine learning in radiological
imaging. By giving insight on how take advantage of machine learning powered
applications, we expect that clinicians can prevent and diagnose diseases more
accurately and efficiently.Comment: 13 figure
Machine learning approaches for lung cancer diagnosis.
The enormity of changes and development in the field of medical imaging technology is hard to fathom, as it does not just represent the technique and process of constructing visual representations of the body from inside for medical analysis and to reveal the internal structure of different organs under the skin, but also it provides a noninvasive way for diagnosis of various disease and suggest an efficient ways to treat them. While data surrounding all of our lives are stored and collected to be ready for analysis by data scientists, medical images are considered a rich source that could provide us with a huge amount of data, that could not be read easily by physicians and radiologists, with valuable information that could be used in smart ways to discover new knowledge from these vast quantities of data. Therefore, the design of computer-aided diagnostic (CAD) system, that can be approved for use in clinical practice that aid radiologists in diagnosis and detecting potential abnormalities, is of a great importance. This dissertation deals with the development of a CAD system for lung cancer diagnosis, which is the second most common cancer in men after prostate cancer and in women after breast cancer. Moreover, lung cancer is considered the leading cause of cancer death among both genders in USA. Recently, the number of lung cancer patients has increased dramatically worldwide and its early detection doubles a patient’s chance of survival. Histological examination through biopsies is considered the gold standard for final diagnosis of pulmonary nodules. Even though resection of pulmonary nodules is the ideal and most reliable way for diagnosis, there is still a lot of different methods often used just to eliminate the risks associated with the surgical procedure. Lung nodules are approximately spherical regions of primarily high density tissue that are visible in computed tomography (CT) images of the lung. A pulmonary nodule is the first indication to start diagnosing lung cancer. Lung nodules can be benign (normal subjects) or malignant (cancerous subjects). Large (generally defined as greater than 2 cm in diameter) malignant nodules can be easily detected with traditional CT scanning techniques. However, the diagnostic options for small indeterminate nodules are limited due to problems associated with accessing small tumors. Therefore, additional diagnostic and imaging techniques which depends on the nodules’ shape and appearance are needed. The ultimate goal of this dissertation is to develop a fast noninvasive diagnostic system that can enhance the accuracy measures of early lung cancer diagnosis based on the well-known hypotheses that malignant nodules have different shape and appearance than benign nodules, because of the high growth rate of the malignant nodules. The proposed methodologies introduces new shape and appearance features which can distinguish between benign and malignant nodules. To achieve this goal a CAD system is implemented and validated using different datasets. This CAD system uses two different types of features integrated together to be able to give a full description to the pulmonary nodule. These two types are appearance features and shape features. For the appearance features different texture appearance descriptors are developed, namely the 3D histogram of oriented gradient, 3D spherical sector isosurface histogram of oriented gradient, 3D adjusted local binary pattern, 3D resolved ambiguity local binary pattern, multi-view analytical local binary pattern, and Markov Gibbs random field. Each one of these descriptors gives a good description for the nodule texture and the level of its signal homogeneity which is a distinguishable feature between benign and malignant nodules. For the shape features multi-view peripheral sum curvature scale space, spherical harmonics expansions, and different group of fundamental geometric features are utilized to describe the nodule shape complexity. Finally, the fusion of different combinations of these features, which is based on two stages is introduced. The first stage generates a primary estimation for every descriptor. Followed by the second stage that consists of an autoencoder with a single layer augmented with a softmax classifier to provide us with the ultimate classification of the nodule. These different combinations of descriptors are combined into different frameworks that are evaluated using different datasets. The first dataset is the Lung Image Database Consortium which is a benchmark publicly available dataset for lung nodule detection and diagnosis. The second dataset is our local acquired computed tomography imaging data that has been collected from the University of Louisville hospital and the research protocol was approved by the Institutional Review Board at the University of Louisville (IRB number 10.0642). These frameworks accuracy was about 94%, which make the proposed frameworks demonstrate promise to be valuable tool for the detection of lung cancer
Radiomics and artificial Intelligence for PET imaging analysis
In recent years, processing of the imaging signal derived from CT, MR or positron emission
has proven to be able to predict outcome parameters in cancer patients. The processing techniques of the signal constitute the discipline of radiomics.
The quantitative analysis of medical images outperform the information that can be obtained through traditional visual analysis.
The recognition of neoplasm molecular and genetic characteristics in a non-invasive way, based on routine radiological examinations, potentially allow complete tumor profiling and subsequent treatment customization at practically zero costs.
This process is further boosted with the availability of increased computing power and development of artificial intelligence approaches
CAD Applications and Emerging Research Potential in Medical Imaging
Computer Aided Detection (CAD) is a valuable technique for precisely
interpreting medical images and it has a global business opportunity of about
USD 1.8 billion. The current aspects with reference to the four sub stages such
as image pre-processing, segmentation, feature extraction and classification
and the future scope of CAD in medical imaging has been discussed in this
paper. Many reviewers have emphasized the need for synergy between engineers
and medical professionals for successful development of CAD systems and the
current work is a move in that direction. The engineering aspects of the above
four stages in four imaging modalities viz. computed tomography, magnetic
resonance imaging, mammography and bone scintigraphy used in the diagnosis of
five critical diseases have been discussed with a clinical background.
Automatic classification of image can play an important role in preliminary
screening of very critical ailments bringing down the cost of health care.
Another recent advancement is using artificial intelligence and machine
learning techniques. This paper reviews these engineering aspects with a view
to explore the opportunities to researchers as well as the medical industry to
offer affordable medical services with accessibility in even remote locations.Comment: 14 pages, 11 figure
Measurement Variability in Treatment Response Determination for Non-Small Cell Lung Cancer: Improvements using Radiomics
Multimodality imaging measurements of treatment response are critical for clinical practice, oncology trials, and the evaluation of new treatment modalities. The current standard for determining treatment response in non-small cell lung cancer (NSCLC) is based on tumor size using the RECIST criteria. Molecular targeted agents and immunotherapies often cause morphological change without reduction of tumor size. Therefore, it is difficult to evaluate therapeutic response by conventional methods. Radiomics is the study of cancer imaging features that are extracted using machine learning and other semantic features. This method can provide comprehensive information on tumor phenotypes and can be used to assess therapeutic response in this new age of immunotherapy. Delta radiomics, which evaluates the longitudinal changes in radiomics features, shows potential in gauging treatment response in NSCLC. It is well known that quantitative measurement methods may be subject to substantial variability due to differences in technical factors and require standardization. In this review, we describe measurement variability in the evaluation of NSCLC and the emerging role of radiomics. © 2019 Wolters Kluwer Health, Inc. All rights reserved
Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives
Various structures in human physiology follow a treelike morphology, which
often expresses complexity at very fine scales. Examples of such structures are
intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large
collections of 2D and 3D images have been made available by medical imaging
modalities such as magnetic resonance imaging (MRI), computed tomography (CT),
Optical coherence tomography (OCT) and ultrasound in which the spatial
arrangement can be observed. Segmentation of these structures in medical
imaging is of great importance since the analysis of the structure provides
insights into disease diagnosis, treatment planning, and prognosis. Manually
labelling extensive data by radiologists is often time-consuming and
error-prone. As a result, automated or semi-automated computational models have
become a popular research field of medical imaging in the past two decades, and
many have been developed to date. In this survey, we aim to provide a
comprehensive review of currently publicly available datasets, segmentation
algorithms, and evaluation metrics. In addition, current challenges and future
research directions are discussed.Comment: 30 pages, 19 figures, submitted to CBM journa
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