Classification Approach for Diagnosis of Arteriosclerosis using B-Mode Ultrasound Carotid Images

121 Pages·2012·12.71 MB·English

Faculdade de Engenharia da Universidade do Porto
Classi(cid:28)cation Approach for Diagnosis of Arteriosclerosis
using B-Mode Ultrasound Carotid Images
Catarina de Brito Carvalho
Mestrado em Engenharia BiomØdica
AurØlio Campilho
Professor CatedrÆtico da Universidade do Porto,
Faculdade de Engenharia
Rui Rocha
Professor Adjunto do Instituto Superior de Engenharia do Porto,
Instituto PolitØcnico do Porto
October 22, 2012

I would live to study, and not study to live.
Francis Bacon

Acknowledgments
It would not have been possible to write this master thesis without the help, all the
willingness, the sharing of knowledge and advices given by Professor AurØlio C. Campilho,
whowasarealtutorandsupervisor,onbothanacademicandapersonallevel. Thesupport
of my second supervisor, Professor Rui Ant(cid:243)nio Rocha and his availability in providing me
his unpublished work was also invaluable.
My sincere thanks goes also to Dr. Elsa Azevedo from the department of neurology
of the Hospital de S. Joªo for the given images and to the INEB-Instituto Nacional de
Engenharia BiomØdica.
I thank my fellow labmates of Biomedical Bioimaging group, to CÆtia Silva for the
stimulating discussions, help and fun and to David. In particular, I am grateful to Dr.
JosØ R. Maseda for the several brainstorms session and the invaluable suggestions.
Last but not the least, I would like to thank my family for the unwearying support.

Abstract
According to the World Health Organization (WHO), arteriosclerosis is the third lead-
ing cause of worldwide deaths. Anatomically, this pathology is characterized by arteries’
hardening or the increasing of the thickness between the intima and media arterial layers.
Arteriosclerosis is commonly diagnosed using carotid ultrasonography. The ultrasound
images are very challenging because they have a signi(cid:28)cant amount of speckle and gener-
ally poor quality. The above-mentioned intima-media thickness (IMT) is often measured
manually by medical experts or using some commercial software. However, the manual
segmentation is very time consuming and subjective and the commercial software lack the
detection of large IMT.
The main purpose of this work was the development of an automatic classi(cid:28)cation
approach to improve a previous work developed within the Bioimaging Group of INEB.
This method uses the instantaneous coe(cid:30)cient of variation as primary edge detector, a
fuzzy classi(cid:28)cation stage, that returns a score map for each interface, and a dynamic
programming function to construct the (cid:28)nal contours. It is suitable to real-time processing
anddoesnotrequiredanyuserinteraction. Thehereinpresentedmethodintentstoimprove
the fuzzy classi(cid:28)cation method (of the previous work) by replacing the heuristic approach
by a classi(cid:28)cation based scheme.
We followed a pattern recognition approach that can be described by the following
sequence. Firstly, the important features are computed. Then, a feature selection method
is applied to reduce the dataset dimensionality, the computational cost and feature redun-
dancy. In this method, the classi(cid:28)cation error is used as evaluation measure. The next
step is the classi(cid:28)ers training and evaluation. Four classi(cid:28)ers were trained and evaluated:
a quadratic, a k-nearest neighbor, a parzen and a support vector classi(cid:28)er. To evaluate
their performance, a cross validation method was used. Two image databases were used
to train and test the proposed method.
The classi(cid:28)cation result is a fuzzy score map where each pixel is labeled with its likeli-
hood of belonging to the searched interface. These maps (one for each searched interface)
are then applied to the already developed dynamic programming function and the (cid:28)nal
interfaces are obtained.

Resumo
SegundoaOrganiza(cid:231)ªoMundialdeSaœde, aarterioscleroseØaterceiracausademortenos
pa(cid:237)ses desenvolvidos. Anatomicamente, esta doen(cid:231)a Ø caracterizada pelo endurecimento
das paredes ou pelo aumento do espessamento entre a camada intima e media das artØrias.
Esta doen(cid:231)a Ø usualmente diagnosticada recorrendo a exames de ultra-sonogra(cid:28)a da
car(cid:243)tida. Asimagensprovenientesdoultra-somsªomuitocomplicadasdeclassi(cid:28)cardevido
(cid:224) presen(cid:231)a de grandes quantidades de ruido associado, normalmente, com baixa qualidade
da imagem. O par(cid:226)metro de avalia(cid:231)ªo desta doen(cid:231)a Ø a espessura entre a camada intima e
a camada media (IMT). Esta IMT pode ser medida manualmente, por mØdicos, ou usando
algumas aplica(cid:231)ıes comerciais. Apesar disto, a segmenta(cid:231)ªo manual Ø muito morosa e
subjectiveeasaplica(cid:231)ıescomerciaisandanªoconseguemsegmentargrandesespessamentos
da IMT.
OprincipalobjectivodestetrabalhoØodesenvolvimentodeumaabordagemdeclassi(cid:28)-
ca(cid:231)ªocompletamenteautomÆticaparaserusadacomoalternativaaumaoutradesenvolvida
no grupo de bio imagem do INEB. Este mØtodo usa o coe(cid:28)ciente de varia(cid:231)ªo instant(cid:226)neo
como detector principal de bordos, uma etapa de classi(cid:28)ca(cid:231)ªo difusa, que retorna mapas
de pontua(cid:231)ªo para cada interface, e uma fun(cid:231)ªo de programa(cid:231)ªo din(cid:226)mica que constr(cid:243)i o
contorno(cid:28)nal. EstametodologiaØexecutadaemtempo-realenªonecessitadenenhumain-
terac(cid:231)ªo com o utilizador. O mØtodo aqui proposto pretende desenvolver uma metodologia
alternativa (cid:224) classi(cid:28)ca(cid:231)ªo difusa (o trabalho anterior) substituindo a abordagem heur(cid:237)stica
por uma baseada em classi(cid:28)ca(cid:231)ªo.
Seguimosumaabordagembaseadaemreconhecimentodepadrıesquepodeserdescrita
pela seguinte sequŒncia. Inicialmente, as caracter(cid:237)sticas mais importantes sªo calculadas.
Depois Ø aplicado um mØtodo de selec(cid:231)ªo de caracter(cid:237)sticas para reduzir o tamanho dos
conjuntos de dados, os tempos de computa(cid:231)ªo e a redund(cid:226)ncia entre caracter(cid:237)sticas. O
erro de classi(cid:28)ca(cid:231)ªo Ø usado nesta altura como medida de avalia(cid:231)ªo. O passo seguinte Ø
o treino e avalia(cid:231)ªo do classi(cid:28)cador. Foram treinados e avaliados quatro classi(cid:28)cadores.
Estes sªo um classi(cid:28)cador quadrÆtico, um classi(cid:28)cador de k-vizinhos mais pr(cid:243)ximos, um
classi(cid:28)cadorparzeneumclassi(cid:28)cadordevectoresdesuporte. Paraavaliarasperformances
destes classi(cid:28)cadores foi utilizado um mØtodo de valida(cid:231)ªo cruzada. Este procedimento foi
aplicado em duas bases de imagens.
O resultado da classi(cid:28)ca(cid:231)ªo Ø um mapa de probabilidades onde cada pixel Ø etique-
x Resumo
tado com a sua probabilidade de pertencer (cid:224) interface procurada. Estes mapas sªo entªo
aplicados a uma fun(cid:231)ªo de programa(cid:231)ªo din(cid:226)mica e as interfaces (cid:28)nais sªo obtidas.