Heuvelmans, M. A. 2015 [Groningen]: University of Groningen. 224 p.
Research output. Scientific › Doctoral Thesis
Heuvelmans, M. A. (2015). Optimization of nodule management in CT lung cancer screening [Groningen]: University of Groningen
Heuvelmans, Marjolein Anne. / Optimization of nodule management in CT lung cancer screening. [Groningen]. University of Groningen, 2015. 224 p.
Heuvelmans, MA 2015, ‘ Optimization of nodule management in CT lung cancer screening ‘, Doctor of Philosophy, University of Groningen, [Groningen].
Optimization of nodule management in CT lung cancer screening. / Heuvelmans, Marjolein Anne .
[Groningen]. University of Groningen, 2015. 224 p.
Research output. Scientific › Doctoral Thesis
Heuvelmans MA. Optimization of nodule management in CT lung cancer screening. [Groningen]: University of Groningen, 2015. 224 p.
title = “Optimization of nodule management in CT lung cancer screening”,
publisher = “University of Groningen”,
T1 – Optimization of nodule management in CT lung cancer screening
AU – Heuvelmans,Marjolein Anne
N2 – Lung cancer is the leading cancer-related cause of death. Through computed tomography (CT) screening, cancer can be detected at the earliest stage, with a much greater probability of cure. After the positive outcome of the US National Lung Screening Trial (NLST), screening with low-dose CT in heavy (former) smokers is now being implemented in the US. An important disadvantage of this study, however, is the high number of false-positive screening results, about 27%, meaning that many individuals without lung cancer suffer from unnecessary follow-up scans, invasive investigations with complication risk, and fear of cancer.br/br/The Dutch-Belgian NELSON study used a screening protocol based on lung nodule volume, rather than diameter (NLST), and on growth rate expressed as volume-doubling time (VDT).
This approach yields 10x fewer false-positive screening results and a better cancer detection rate. The aim of this thesis was to examine how the percentage of false-positive results can be reduced even further while maintaining reliable cancer detection.br/br/Management of screening-detected lung nodules can be optimized by semi-automatic volume measurements instead of manual diameter measurements: VDT can reliably quantify nodule growth, and optimized cut-off values for nodule volume and VDT can distinguish between negative, intermediate and positive screening results. For new nodules, more stringent guidelines are needed. Ultimately, the interpretation by the radiologist, who can deviate from the screening protocol, remains important to further improve the results. Mortality analysis of the NELSON trial is essential to decide whether lung cancer screening in Europe should be implemented (cite Editorial Lancet).
AB – Lung cancer is the leading cancer-related cause of death. Through computed tomography (CT) screening, cancer can be detected at the earliest stage, with a much greater probability of cure. After the positive outcome of the US National Lung Screening Trial (NLST), screening with low-dose CT in heavy (former) smokers is now being implemented in the US.
An important disadvantage of this study, however, is the high number of false-positive screening results, about 27%, meaning that many individuals without lung cancer suffer from unnecessary follow-up scans, invasive investigations with complication risk, and fear of cancer.br/br/The Dutch-Belgian NELSON study used a screening protocol based on lung nodule volume, rather than diameter (NLST), and on growth rate expressed as volume-doubling time (VDT). This approach yields 10x fewer false-positive screening results and a better cancer detection rate. The aim of this thesis was to examine how the percentage of false-positive results can be reduced even further while maintaining reliable cancer detection.br/br/Management of screening-detected lung nodules can be optimized by semi-automatic volume measurements instead of manual diameter measurements: VDT can reliably quantify nodule growth, and optimized cut-off values for nodule volume and VDT can distinguish between negative, intermediate and positive screening results. For new nodules, more stringent guidelines are needed. Ultimately, the interpretation by the radiologist, who can deviate from the screening protocol, remains important to further improve the results. Mortality analysis of the NELSON trial is essential to decide whether lung cancer screening in Europe should be implemented (cite Editorial Lancet).
M3 – Doctoral Thesis
PB – University of Groningen
Our Guarantees Our Quality Standards Our Fair Use Policy
What Makes UK Essays Different?
- We have a verifiable trading history as a UK registered company (details at the bottom of every page).
- Our Nottingham offices are open to the public where you can meet our team of over 40 full-time staff.
- UK Essays partner with Feefo.com to publish verified customer testimonials – both good and bad!
Ask an Expert FREE
Ask an Expert Index Ask a Question Paid Services
About Our Ask an Expert Service
Our totally free “Ask an Expert” Service allows users to get an answer of up to 300 words to any academic question.
- Questions typically answered within 24 hours.
- All answers are researched and written by fully qualified academics in the question’s subject area.
- Our service is completely confidential, only the answer is published – we never publish your personal details.
- Each professional answer comes with appropriate references.
More About Us
Published: 23, March 2015
The best way to reduce death rates due to this disease is to treat it at an early stage. Early diagnosis of Lung cancer requires an effective procedure to allow physicians to differentiate between benign tumors from malignant ones. Computer-Aided Diagnosis (CAD) systems can be used to assist with this task. CAD is a non-trivial problem, and present approaches employed have the complexity in increasing both sensitivity to tumoral growths and specificity in identifying their nature.
CAD is an approach designed to reduce observational oversights and the false negative rates of physicians interpreting medical images. Future clinical studies have proved that there will be an increased use of cancer detection with CAD assistance. Computer programs have been widely used in clinical practices that support radiologists in detecting possible abnormalities on diagnostic radiology exams. The most common application is the computer aided (or assisted) detection, commonly referred to as CAD. The term CAD is pattern recognition technique that recognizes malignant features on the image and it is reported to the radiologist, in order to minimize false negative readings. CAD technique is presently FDA and CE approved for use with both film and digital mammography, for both screening and diagnostic exams; for chest CT; and, for chest radiographs. The main aim of CAD is to enhance the detection of disease by minimizing the false negative rate due to observational oversights. By using CAD, there are no demands on the radiologist. The main aspect of the approach is to increase disease detection quality. CAD approaches are developed to investigate for the same features that a radiologist expects during case review. Thus, CAD algorithms in terms of breast cancer on mammograms look for micro calcifications and masses. On chest radiographs and CT scans, present CAD approaches look for pulmonary densities that have particular physical features.
Get your grade
or your money back
using our Essay Writing Service!
Essay Writing Service
The development of CAD systems is mainly to support the radiologist and not to replace the radiologist. For instance, a CAD system could scan a mammogram and draw red circles around suspicious areas. Later, a radiologist can observe these areas and examine the true nature of those areas.
A number of CAD schemes have been investigated in literature. These include:
Subtraction approaches that detect abnormality by comparison with normal tissue
topographic approaches that perform feature extraction and analysis to detect abnormalities
Filtering approaches that use digital signal processing filters to augment abnormalities for easy detection
staged expert systems that perform rule-based analysis of image data in an attempt to provide a correct diagnosis
Most of the CAD approaches follow the subtraction techniques  in which the detection of abnormalities is by searching for image differences based on comparison with known normal tissue. In topographic techniques, the detection of the anomalies is based on image feature identification and extraction of features that associate with pathological anomalies, such as in texture analysis . Most approaches follow the following stages which includes
examining the image data
extracting pre-determined features
Localizing regions of interest or ROIs which can be observed for potential abnormalities.
Several of these approaches are used for high degrees of sensitivity, but many have been vulnerable by high false-positive rates and hence low specificity. The problem of false positives is aggravated by the fact that false positive rates are reported per image, not per case. As many radiological examinations include more than one image, the actual number of false positives may be a multiple of those reported.
A number of different approaches have been employed in an effort to reduce false positive rates, many of them focusing on the use of Artificial Neural Networks (ANN), Machine Learning Approaches etc. Receiver Operating Curve or ROC is a general metric used for evaluating the performance of CAD systems and is commonly used to evaluate a CAD approach degree of tradeoff between sensitivity and specificity.
CAD is basically depends on highly complex pattern recognition. X-ray images are scanned for suspicious structures. Generally a few thousand images are needed to optimize the algorithm. Digital image data are copied to a CAD server in a DICOM-format and are prepared and analyzed in several steps.
The art of taking in raw data and taking an action depending on the classification of the pattern is generally defined as pattern recognition. Most research in pattern recognition is about methods for supervised learning and unsupervised learning. The main purpose of pattern recognition is to categorize data (patterns) based on either a priori knowledge or on statistical information obtained from the patterns. The patterns to be categorized are normally groups of measurements or observations, defining points in a suitable multidimensional space. The entire pattern recognition system contains a sensor that gathers the observations to be classified or described, a feature extraction approach that evaluates numeric or symbolic information from the observations, and a classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features. The classification approach is generally based on the presence of a set of patterns that have already been classified.
Always on Time
Marked to Standard
In order to overcome these problems, this chapter introduces a proposed Computer Aided Diagnosing (CAD) system for detection of lung nodules using the Extreme Learning Machine. The lung cancer detection system is shown in figure 6.1. The proposed approach initially apply the different image processing techniques such as Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, Lung Border Extraction and Flood-Fill algorithms for extraction of lung region. Then for segmentation Modified Fuzzy Possibilistic C Mean algorithm  is used and for learning and classification Extreme Learning Machine is used.
Lung Regions Extraction
Segmentation of lung region using MFPCM
Analysis of segmented lung region
Formation of diagnosis rules
Classification of occurrence and non occurrence of cancer in the lung using ELM
Figure 6.1: The Lung Cancer Detection System
MACHINE LEARNING TECHNIQUES
Machine learning is a scientific discipline that is mainly based on the design and development of techniques that allow computers to develop characteristic features based on empirical data. In order to capture the unknown underlying probability distribution of the learner, the previous experience is of great help. Data is seen as examples that show relations between observed variables. A main aim of machine learning research is to make them automatically learn to identify complex patterns and make intellectual decisions based on the nature of data. As the possible inputs are too large to be covered by the set of training data, it is very tough.
The main aim of a learner is to generalize from its past experience. The training data from its experience come from unknown probability distribution and the learner has to get something more general, something about that distribution that offers significant responses for the future cases. Figure 6.2 shows the architecture of ELM.
Figure 6.2: Machine Learning Approach
Importance of Machine Learning
There are several reasons for the machine learning approach still being an important technique. The important engineering reasons are:
Certain operations can be defined only by instances. It is able to identify input / output pairs but a brief relationship between inputs and desired outputs can be obtained only by instances. Machines are expected to alter their internal structure to create correct outputs for a large number of sample inputs and thus properly limit their input/output function to approximate the relationship implicit in the instances.
Machine learning techniques are often used in the extraction of the relationships and correlations among data (data mining).
Most of the machines designed by human can not perform in the environments in which they are used. Moreover, certain characteristics of the working environment are not entirely known at design time. Machine learning approaches can be used for on-the-job enhancement of existing machine designs.
Certain works has large amount of knowledge available which is tough for the humans to encode explicitly. But machines are capable of learning this knowledge and perform better.
Environments keep on changing. But the machines that can adapt to a changing environment are very significant as it reduces the need for constant redesign.
Human constantly discover the new knowledge about tasks. There is constant change in the vocabulary. So the redesign of Artificial Intelligence systems according to the new knowledge is impractical, but machine learning approaches can track the changes and can easily update the new technologies.
Types of machine learning algorithms
Machine learning algorithms are organized into taxonomy, based on the desired outcome of the algorithm.
Supervised learning: It generates a function that maps inputs to desired outputs.
Unsupervised learning: It models a set of inputs, like clustering.
Semi-supervised learning: This type combines both labeled and unlabeled samples to produce a suitable classifier.
Reinforcement learning: It learns how to act given an observation of the world.
Transduction: It predicts novel outputs depending on training inputs, training outputs, and test inputs.
Learning to learn: This approach learns its own inductive bias depending on previous experience.
Extreme Learning Machines
Extreme Learning Machines have very high capability that can resolve problems of data regression and classification. Certain challenging constraints on the use of feed-forward neural networks and other computational intelligence approaches can be overcome by ELM. Due to the growth and improvement in the ELM techniques, it integrates the advantages of both neural networks and support vector machines by having faster learning speed, requiring less human intervene and robust property. An Example of ELM is depicted in Figure 6.3.
This Essay is
This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.
Examples of our work
Figure 6.3: An Example of ELM
ELMs parameters can be analytically determined rather than being tuned. This algorithm provides good generalization performance at very fast learning speed. From function approximation point of view ELM is very different compared to the traditional methods. ELM shows that the hidden node parameters can be completely independent from the training data.
In conventional learning theory, the hidden node parameters cannot be created without seeing the training data.
In ELM, the hidden node parameters can be generated before seeing the training data.
Salient features of ELM
Compared to popular Back propagation (BP) Algorithm and Support Vector Machine (SVM), ELM has several salient features:
Ease of use: Except predefined network architecture, no other parameters need to be manually tuned. Users need not have spent much time in tuning and training learning machines.
Faster learning speed: The time taken for most of the training will be in milliseconds, seconds, and minutes. Other conventional methods cannot provide such a fast learning speed.
Higher generalization performance: The generalization performance of ELM is better than SVM and back propagation in most cases.
Applicable for all nonlinear activation functions: Discontinuous, differential, non-differential functions can be used as activation functions in ELM.
Applicable for fully complex activation functions: Complex functions can also be used as activation functions in ELM.
All the parameter of the feed forward networks need to be tuned and thus the dependency between different layers of parameters exist. Gradient descent-based methods have been used in various learning algorithms of feed forward neural networks. These approaches are usually very slow due to improper learning steps or may easily converge to local minima. To achieve the significant learning performance, many iterative learning steps are required by such learning algorithms
The initial stage of the proposed technique is lung region extraction using several image processing techniques. The second stage is segmentation  of extracted lung region using Fuzzy Possibilistic C Mean (FPCM) algorithm. Then the diagnosis rules for detecting false positive regions are elaborated. Finally Extreme Learning Machine (ELM) technique is applied in order to classify the cancer nodules.
Five phases included in the proposed computer aided diagnosis system for lung cancer detection are as follows:
â€¢ Extraction of lung region from chest computer tomography images
â€¢ Segmentation of lung region using Modified Fuzzy Possibilistic C-Mean
â€¢ Feature extraction from the segmented region
â€¢ Formation of diagnosis rules form the extracted features
â€¢ Classification of occurrence and non occurrence of cancer in the lung
Phase 1: Extraction of Lung Region from Chest Computer Tomography Images
The first phase of the proposed Computer Aided Diagnosing system is the extraction of lung region from the chest computer tomography scan image. This phase uses the basic image processing methods. The procedure for performing this phase using the image processing methods is provided in figure 6.4. The image processing methods used for this phase are Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, Lung Border Extraction and Flood-Fill algorithms.
Usually, the CT chest image not only contains the lung region, it also contains background, heart, liver and other organs areas. The main aim of this lung region extraction process is to detect the lung region and regions of interest (ROIs) from the CT scan image.
The first step in lung region extraction is application of bit plane slicing algorithm to the CT scan image. The different binary slices will be resulted from this algorithm. The best suitable slice with better accuracy and sharpness is chosen for the further enhancement of lung region.
The next is application of Erosion algorithm which enhances the sliced image by reducing the noise from the image. Then dilation and median filters are applied to the enhanced image for further improvement of the image from other distortion. Outlining algorithm is then applied to determine the outline of the regions from the obtained from noise reduced images. The lung region border is then obtained by applying the lung border extraction technique. Finally, flood fill algorithm is applied to fill the obtained lung border with the lung region. After applying these algorithms, the lung region is extracted from the CT scan image. This obtained lung region is further used for segmentation in order to detect the cancer nodule.
Lung Border Extraction
Flood Fill Algorithm
Figure 6.4: The proposed lung regions extraction method.
Figure 6.5 shows the application of different image processing techniques for the extraction of lung region from the CT scan image. The lung region obtained finally is shown in figure 6.5 (h).