A computer aided diagnosis system for lung nodules detection in postero anterior chest radiographs
Creators: name: CASIRAGHI, ELENA
Publisher: name: Universit� degli Studi di Milano
Year: 2004

description: This thesis describes a Computer Aided System aimed at lung nodules detection.
The fully automatized method developed to search for nodules is
composed by four steps. They are the segmentation of the lung field, the
enhancement of the image, the extraction of the candidate regions, and the
selection between them of the regions with the highest chance to be True
Positives. The steps of segmentation, enhancement and candidates extraction
are based on multi-scale analysis. The common assumption underlying
their development is that the signal representing the details to be detected
by each of them (lung borders or nodule regions) is composed by a mixture
of more simple signals belonging to different scales and level of details.
The last step of candidate region classification is the most complicate; its
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task is to discern among a high number of candidate regions, the few True
Positives. To this aim several features and different classifiers have been
investigated.
In Chapter 1 the segmentation algorithm is described; the algorithm has
been tested on the images of two different databases, the JSRT and the
Niguarda database, both described in the next section, for a total of 409
images. We compared the results obtained with another method presented
in the literature and described by Ginneken, in [85], as the one obtaining
the best performance at the state of the art; it has been tested on the same
images of the JSRT database. No errors have been detected in the results
obtained by our method, meanwhile the one previously mentioned produced
an overall number of error equal to 50. Also the results obtained on the
images of the Niguarda database confirmed the efficacy of the system realized,
allowing us to say that this is the best method presented so far in
the literature. This sentence is based also on the fact that this is the only
system tested on such an amount of images, and they are belonging to two
different databases.
Chapter 2 is aimed at the description of the multi-scale enhancement and
the extraction methods.
The enhancement allows to produce an image where the ?conspicuity? of
nodules is increased, so that nodules of different sizes and located in parts
of the lungs characterized by completely different anatomic noise are more
visible. Based on the same assumption the candidates extraction procedure,
described in the same chapter, employs a multi-scale method to detect all
the nodules of different sizes. Also this step has been compared with two
methods ([8] and [1]) described in the literature and tested on the same
images. Our implementation of the first one of them ([8]) produced really
poor results; the second one obtained a sensitivity ratio (See Appendix C
for its definition) equal to 86%. The considerably better performance of our
method is proved by the fact that the sensitivity ratio we obtained is much
higher (it is equal to 97%) and also the number of False positives detected
is much less.
The experiments aimed at the classification of the candidates are described
in chapter 3; both a rule based technique and 2 learning systems, the Multi
Layer Perceptron (MLP) and the Support Vector Machine (SVM), have
been investigated. Their input is a set of 16 features. The rule based system
obtained the best performance: the cardinality of the set of candidates left is
highly reduced without lowering the sensitivity of the system, since no True
Positive region is lost. It can be added that this performance is much better
than the one of the system used by Ginneken and Schilam in [1], since its
sensitivity is lower (equal to 77%) and the number of False Positive left is
comparable. The drawback of a rule based system is the need of setting the
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thresholds used by the rules; since they are experimentally set the system is
dependent on the images used to develop it. Therefore it may happen that,
on different databases, the performance could not be so good.
The result of the MLPs and of the SVMs are described in detail and the
ROC analysis is also reported, regarding the experiments performed with
the SVMs.
Furthermore, the attempt to improve the performance of the classification
leaded to other experiments employing SVMs trained with more complicate
feature sets. The results obtained, since not better than the previous,
showed the need of a proper selection of the features. Future works will then
be focused at testing other sets of features, and their combination obtained
by means of proper feature selection techniques.